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Taito Watanabe, Yohei Kono

Compressed air systems usually run at a constant set pressure well above the end-use requirement; the excess margin is wasted energy. A lightweight hierarchical nonlinear model predictive control (NMPC) scheme, retrofitted to the sequencing controller of a centralized compressor station typical of small and medium-sized plants, lowers the set pressure as far as the end-use requirement allows. It uses only two pressure gauges (receiver and one critical end-use point) and compressor data, without flow meters or piping surveys. The end-use pressure is predicted by a low-order pressure-drop model identified by least squares under stability, damping and steady-state equilibrium constraints, which serve rather than accuracy: they let the measured pressure drop be held over the horizon with unit gain. Validated in three stages (plant data, a pipe-network simulator checked against measurements, and the actual controller, at most 1.41 s per update), the NMPC meets the required pressure under real demand and reduces the estimated annual energy consumption by 2.8% relative to the current constant operation (9.3% relative to 0.70 MPa). A PID cascade with the same gauges saves about as much only when tuned by a standard rule at its recommended setting from a constant-load step test, difficult in an operating plant; the NMPC needs no such test and keeps its performance with its initial model and default weights after plant expansion and network split. The margin becomes a design parameter instead of a fixed excess, and the controller’s signals show where the supply system lacks headroom.

Elena Bruce, Rossana Sepúlveda

Mathematical modeling of columnar leaching is a useful tool for predicting and evaluating the kinetics of copper extraction. One commonly used model for this process is the shrinking core model (SCM). In this study, the aim was to develop a model for column leaching of oxidized copper ore based on the SCM, which incorporates the ore sulfation stage before leaching. In sulfation and leaching laboratory-scale tests, we studied the effect of acid dosage (at 22.8, 34.2, and 45.6 kg/t), humidity (at 90%, 100%, and 110% of the saturation humidity of the mineral), ore granulometry (−3/4′′ and −3/8′′), and rest time (at 24, 48, 72, and 96 h) on sulfation. We found that the highest sulfation reached 49.7% for both granulometries in studies. In the column tests, the effects of acid dosage (at 34.2, 45.6 kg/t), ore granulometry (−3/4′′, −3/8′′), and rest time (at 24, 48 h) were studied. When the SCM was applied to these tests, we obtained fit qualities within 63.4% and 74.9%. By incorporating the sulfation factor into the SCM predictions, we observed an average increase in adjustment between 24% and 28%. This method is effective for minerals and operating cond.

Alpha Chi Him Tsang, Jing Ji

With the popularization of electric vehicles, lithium-ion batteries (LIBs) have begun to be used in large quantities, and the scale of the waste batteries is large and growing rapidly. So, the environmental pressure faced and the economic benefits contained in waste batteries are rapidly increasing every year. The valuable metals (e.g., Ni, Co and Mn) in the cathodes of LIBs are important non-renewable resources with high recovery value. So, the recovery and reuse of valuable metals in waste LIBs have constituted an important issue of concern. This review focuses on the hydrometallurgy of spent-LIB cathode, the extraction of valuable metals (solvent extraction and filtration), and co-precipitation methods. A perspective and outlook associated with the challenges and limitations of current hydrometallurgy research is proposed for breakthrough development of such technology for future industrialization.

Yevhen Synehin, Volodymyr Ruban

This study investigates the process that forms centerline porosity in the axial zone of a continuously cast steel billet during its hardening. The task addressed relates to the need to establish rational parameters for the temperature-speed mode of casting to minimize axial porosity, which is critical for increasing the density of the metal and the quality of rolled products. A comprehensive methodology for physical modeling of the hardening process using stearin as a model substance has been devised. Based on the analysis of similarity numbers (Biot, Fourier, Kosovych), the exact scales of modeling were established: by time – 1.854, by linear dimensions – 0.1778, by heat transfer coefficient – 0.04639. It was determined that for an adequate simulation of the formation of the bloom blank, the closing angle of the liquid core varies within 2.3–6°. Quantitative parameters of the experiment on the model were established: overheating of the melt in the range of 1.8–4.2°C, which corresponds to 15–35°C for steel. The resulting data are explained by the laws of convection heat transfer and phase transition. The angle of inclination of the crystallization front and the intensity of heat removal determine the moment of formation of 'bridges' and interceptions in the thermal center, which directly defines the morphology and volume of shrinkage pores.

Öznur KILIÇ, Kerem Altuğ GÜLER

In this study, different melting and refining strategies were applied to G17CrMo5-5 cast steel in order to evaluate their influence on inclusion evolution and microstructural cleanliness. The objective was to determine how variations in the Argon Oxygen Decarburization (AOD) process can affect the morphological characteristics of inclusions within the steel matrix, which is critical for enhancing mechanical properties and performance. A systematic methodology was developed, involving the comparison of metallurgical outcomes from several experimental batches subjected to distinct refining techniques. The results indicate that the refining conditions significantly influenced the size, distribution, and composition of inclusions, as well as the overall microstructural integrity of the cast steel. Notably, improvements in cleanliness were observed, leading to a more favorable microstructure that enhances the material's mechanical properties. These findings contribute valuable insights to the field of materials engineering and have practical implications for the processing of high-performance cast steels. The study underscores the importance of optimizing refining processes to achieve desired material characteristics.

Daria Togobitska, Allа Bielkova

This work presents a systematic approach to describing the physicochemical processes occurring in the metal-slag system during steel refining in a ladle furnace. The aim of the study was to develop methods for predicting the distribution of main and impurity elements based on a comprehensive analysis of the initial and final melts of metal and slag. Industrial data on the chemical composition of SAE1006 grade steel and the corresponding slag were used as the input. Based on the methodological framework of the Directed Chemical Bonding Concept (DCBC) and mathematical processing of multivariate data, the applicability of interatomic interaction parameters and technological regime indicators as model parameters was substantiated. Complex indicators for the metal-slag and metal-additive systems were constructed using Harrington’s desirability function, which enables integration of multidimensional parameters into a single generalized index. Analytical dependencies were developed for calculating the distribution coefficients of sulphur (S), silicon (Si), manganese (Mn), and aluminium (Al), considering both the composition of metallurgical phases and the smelting conditions. The proposed approach provides a quantitative assessment of the distribution coefficients of elements as variable quantities, which fundamentally distinguishes it from traditional methods. The results obtained form the basis for developing algorithms to predict the chemical composition of the final melt and for creating practical recommendations on the selection of optimal additives and slag-forming mixtures aimed at improving steel quality and reducing production costs.

Fuhai LIU, Tantong SHI

The study explores the application of the CO2 bottom-blowing method in the 120t LF (Ladle Furnace) refining process to improve metallurgical outcomes. The objective was to evaluate the effectiveness of CO2 over traditional argon methods in achieving inclusion removal and quality enhancement in molten steel. Utilizing both experimental setups and numerical modeling, various bottom-blowing rates and gas types were analyzed to determine their impact on metallurgical properties during the refining process. Results indicated that the CO2 method significantly influences the total inclusion removal rate and size distribution of inclusions, outperforming the argon method in certain conditions. Key findings include improved removal rates of non-metallic inclusions and favorable changes in chemical composition, such as carbon and sulfur levels, leading to enhanced steel quality. Additionally, operational parameters such as temperature variations and refining time were assessed, confirming the potential of CO2 as an effective agent in steel refining applications. This research provides valuable insights into optimizing refining processes using innovative methods, ultimately contributing to advancements in metallurgical engineering and carbon neutrality efforts in steel production.

Erhan SELÇUK, Kerim Emre ÖKSÜZ

This study investigates the effect of the vibratory assisted severe surface plastic deformation (VA–SPD) method on the mechanical properties of Ti6Al4V alloy. The primary objective was to assess the enhancements in mechanical characterization resulting from this innovative surface modification technique. A systematic approach involving experimental procedures was adopted to analyze the material's response to the VA–SPD method. Various mechanical tests were conducted, including hardness and tensile strength assessments, to quantify the changes in the alloy's properties. The results demonstrated significant improvements in strength and hardness, attributed to the refined microstructure and increased dislocation density induced by the VA–SPD process. Furthermore, the study highlighted the potential of VA–SPD as a viable alternative for enhancing the performance and durability of Ti6Al4V alloy in various industrial applications, particularly in aerospace and biomedical fields. Overall, the findings provide crucial insights into the mechanical characterization of titanium alloys subjected to advanced surface deformation techniques.

M. Bronovets, P. Bukovskiy

Rolling bearings are among the most common components, used in virtually all branches of industry—from bicycles to space stations, including industrial and machinery equipment, automobiles, aircraft, rockets, and household appliances. Residual stresses and surface quality play a significant role in enhancing the tribological characteristics of friction pairs. Laser shock peening (LSP) is a promising method for residual stresses design, allowing for their magnitude and depth to be varied over a wide range. This study involved the LSP of friction pairs made from high-chromium bearing steel grade AISI 440-С (95 Х18Ш in Russia). The results demonstrate significant improvements in tribological characteristics under both dry and boundary friction conditions. LSP led to a change in the wear mechanism, which in turn reduces wear.

Wei Xu, Gaojian You

The surface properties of aviation titanium alloy directly affect its service safety and reliability, and high-performance coating are essential for its practical application. However, existing studies lack clear guidance on laser power parameters for preparing high-quality TiAl coating on titanium alloy substrates, leaving a research gap in optimizing coating quality and performance. To fill this gap, this work used selective laser melting (SLM) to fabricate TiAl coating on titanium alloy substrates, focusing on the influence of laser power (350–380 W) on coating microstructure, phase composition, elemental distribution and microhardness. SEM, XRD and EDS characterizations were used for systematic analysis. The results show that laser power is the key factor determining coating quality. Severe longitudinal cracks appeared at 350 W and improved microhardness and coating uniformity were observed at higher laser powers.

Zhiyuan Sun, Renjie Su

Bearing steel, as the core material of key transmission and support components, has its surface performance directly determining the reliability, stability, and service life of the bearing system. To address this, research has established four major surface modification technology systems: surface mechanical strengthening, surface heat treatment strengthening, surface coating strengthening, and hybrid strengthening. This review systematically summarizes the progress of these four systems, focusing on their strengthening mechanisms, microstructural evolution, and effects on the service performance of bearings. Surface mechanical strengthening improves the surface layer’s resistance to crack initiation and propagation through grain refinement induced by existing methodologies. Furthermore, surface heat treatment enhances durability by altering the microstructure, while surface coatings provide an additional layer of protection against wear and corrosion. The advancements in hybrid strengthening also show promising results by combining multiple technologies for superior performance. These developments not only extend the lifespan of bearing steels but also enhance their application in demanding environments.

Xiong Wu, Yu Zhang

Converting offshore wind energy into hydrogen is a promising approach to improving renewable energy utilization. Coordinated offshore and onshore operation can further enhance hydrogen supply flexibility. Despite this potential, most existing studies model offshore hydrogen production and onshore demand delivery separately. This paper therefore proposes a coordinated supply scheduling model of offshore and onshore hydrogen considering both power and hydrogen transportation networks. The model coordinates power system operation with maritime and onshore hydrogen transportation scheduling under network constraints, capturing the spatiotemporal interactions among offshore production, port buffering, onshore production, truck routing, storage operation, and hydrogen refueling demand satisfaction. A time-window mapping method is developed to couple continuous time transportation events with discrete time power and hydrogen scheduling constraints. To handle renewable generation uncertainty, the proposed model is extended into a two-stage stochastic formulation. An accelerated progressive hedging algorithm (PHA) with adaptive penalty updating and stable binary variable fixing is developed to improve the efficiency of solving problems with multiple scenarios. Case studies on modified IEEE 24-bus and IEEE 118-bus systems validate the effectiveness of the proposed model and algorithm. The proposed accelerated PHA substantially reduces solution time while maintaining negligible deviations in the objective value.

Fengming Shi, Siyang Liao

Large-scale integration of high-penetration renewables aggravates net-load fluctuations and brings uncertainties to secondary frequency-regulation demands. Limited by adjustable capacity, ramping capability and operational costs, conventional thermal generating units cannot independently provide frequency-regulation support balancing economic and dynamic performance. Different from existing user-side frequency-regulation schemes relying on end-user authorization, massive communication terminals, aggregator coordination and dedicated compensation-settlement mechanisms, this work exploits voltage-power coupling of feeder loads and grid-controllable voltage-regulation devices to incorporate feeder clusters into the active-power frequency-regulation resource pool. Three core challenges are addressed: quantification of feeder safe adjustable capacity, coordinated command allocation among heterogeneous resources, and sustained Point of Common Coupling (PCC) power tracking over regulation cycles. First, the Conservation Voltage Reduction (CVR) coefficient of feeder loads is identified via grid-side voltage-driven exogenous excitation. Combined with a Z-bus-based linearized power-flow model, feeder safe adjustable capacity is evaluated subject to full-node voltage constraints. Second, considering discrepancies in cost, tracking accuracy and response speed between continuously-regulated thermal units and feeder clusters, a coordinated command allocation strategy is proposed.

Changming Chen, Aokang Pang

With the rapid advancement of cloud computing, big data and artificial intelligence technologies, data center microgrids (DCMGs) have become crucial infrastructures supporting the digital economy. In this context, a key challenge is how to fully exploit their multi-dimensional scheduling potential in the peer-to-peer (P2P) electricity market to enhance operational economics and demand-side flexibility. Therefore, a coordinated computing-energy optimization method of DCMGs in a joint kilowatt (kW) and negawatt (nW) P2P market is proposed. First, a coordinated computing-energy management optimization model for DCMGs is established, explicitly modeling the computing load scheduling and cooling systems. Then, a joint kW-nW P2P market mechanism is developed to coordinate the endogenously determined DCMG flexibility across electricity and demand-side flexibility transactions. On this basis, an RHC-based dynamic clearing algorithm is introduced by embedding iterative double-sided-auction clearing within outer rolling optimization, thereby coupling market decisions with operational states and improving adaptability to source-load uncertainty. Case studies show that the proposed DCMG model reduces net energy demand by 6.9% – 36.6% and energy costs by 8.6% – 41.6% compared with simplified models. Relative to alternative market configurations, the joint kW-nW market reduces total operating cost by 4.9% – 7.7%. Compared with static clearing, the proposed dynamic algorithm improves performance significantly.

Qiansa Wang, Yanjiao Wang

In the context of the transition from clean energy to sustainable development, as an emerging low-carbon energy carrier, hydrogen is of crucial importance. The planning of a hydrogenation facility network (HFN) with a reliable hydrogen supply has thus become an important research topic in the field of hydrogen energy provision. This study investigates the planning of the HFN from the perspectives of economic cost and network reliability, under conditions of uncertain hydrogen supply capacity. To address the challenges posed by uncertainty, the renewable energy-powered hydrogen production process is categorized into multiple scenarios. We formulate an ambiguity set using the Kullback-Leibler (KL) divergence and establish ambiguous chance constraints, thereby proposing a novel bi-objective distributionally robust HFN optimization model (DR-HFN). Based on the decision-maker’s preferences in practice, an economic-oriented optimization model and a reliability-oriented optimization model are developed, respectively. Subsequently, a mixed-integer linear programming (MILP) representation is derived from the proposed model to ensure computational tractability. A customized branch-and-cut (BC) algorithm is further designed to enhance the model’s solvability and verify the advantages of our proposed methods via a real case.

Defan Feng, Haoran Zhang

Energy-efficient indoor control is often formulated as direct energy minimization, with comfort and indoor environmental quality (IEQ) represented by weighted objectives or soft penalties. Such formulations can report low energy use even when indoor conditions remain outside acceptable IEQ bounds. This paper proposes CKM-CF, a Constraint-first Knowledge-Memory Multi-agent Controller for qualified low-energy room-level operation. CKM-CF coordinates heterogeneous room devices through actuator-bounded device agents, deterministic conflict-aware bundle planning, and execution-derived knowledge-graph memory. Candidate bundles are selected by a constraint-first priority hierarchy: hard IEQ feasibility and task adequacy must be satisfied before energy is considered among qualified alternatives. The controller is evaluated in a simulated single-zone room-control environment, demonstrating effective management of energy consumption while adhering to strict indoor quality standards. Across multiple episodes, CKM-CF achieves a qualified performance rate significantly exceeding baseline methods, highlighting its potential for practical applications in smart building energy management.

Yogesh Pipada, Sunil Kumara

Reliable forecasting of prosumer flexibility is critical for demand response aggregators participating in frequency controlled ancillary service markets, where reliability requirements such as the P90 standard are enforced. Limited behavioural data and dependence on exogenous forecasts introduce uncertainty into flexibility estimates, which can lead to reserve overestimation and financial penalties. This study applies and systematically evaluates established uncertainty quantification approaches based on Monte Carlo dropout (MCD), conformal prediction (CP), and conformalised quantile regression (CQR) to construct prediction intervals for aggregated prosumer flexibility and support uncertainty-aware aggregator bidding. The approaches are evaluated for a behind-the-meter aggregator participating in the Danish manual frequency restoration reserve capacity market. A large-scale synthetic dataset is generated using an industry-grade home energy management system, combined with publicly available load, solar, price, activation, and device-level data. Multiple multivariate CP strategies are evaluated using MCD predictions, alongside CQR using a separately trained quantile regression model. Under nominal synthetic test conditions, conformal methods substantially improve joint coverage compared with uncalibrated MCD baselines. More conservative methods satisfy the 90% lower-tail reliability requirement, whi.

Zekai Jin, Yue Zhou

Driven by the rapid growth of cloud computing and artificial intelligence, data centers (DCs) are becoming large, flexible loads that increasingly affect active distribution networks (ADNs). Coordinating these loads motivates joint assessment of thermal-model accuracy, operating performance, and online computation. This paper proposes a physics-informed hierarchical multi-agent reinforcement learning (H-MARL) framework for grid–thermal–computational scheduling of a single-operator DC park within an ADN. A physics-informed neural network (PINN) is first developed as a dynamic cooling surrogate that captures zone heat balance and envelope heat gains while promoting thermodynamic consistency through residual losses and a soft coefficient-of-performance (COP) plausibility penalty. The PINN achieves a test mean absolute percentage error of 1.40% in cooling-power prediction, averaged across four DCs and five training seeds, with an approximately 5000-fold speed-up over EnergyPlus based on mean per-DC evaluation times. A H-MARL scheduler coordinates spatiotemporal workload migration, dynamic voltage and frequency scaling (DVFS), and cooling, while alternating-current optimal power flow (AC-OPF) dispatches electrical assets under network constraints. Agents observe carbon-inclusive nodal prices and share a rew.

Bram van Duinen, Karin van der Wiel

Energy system models are increasingly dependent on representative climate input. Yet, a fundamental mismatch persists between the hundreds of simulated years often used in climate science and the handful of years that computationally demanding power system models can process. Current practice, including ENTSO-E’s European Resource Adequacy Assessment, relies on climate year selections that have not been validated against explicit representativeness criteria. This risks biased investment decisions and blind spots for plausible weather conditions. This study proposes simulated annealing as an optimisation method for selecting representative subsets of complete climate years from large climate ensembles. Representativeness is quantified using the seasonal sliced Wasserstein distance, a metric from optimal transport theory that captures representativeness of marginal distributions, inter-variable correlations, and seasonal structure simultaneously. We evaluate simulated annealing against the alternative methods of random search, filtered random search, and K-Medoids clustering across three test cases spanning the Netherlands and Europe, using 180 climate years from the Pan-European Climate Database as a reference. Simulated annealing consistently produces the most representative subsets and outperforms the compared methods in generating effective energy statistics.

Yang Lv, Lilin Jie

Power batteries, as core components of new energy systems, exhibit significant regional variability in field reliability under heterogeneous operating conditions. However, accurate prediction of region-specific reliability remains challenging due to the scarcity of comprehensive field failure data across diverse regions and the homogeneity assumptions commonly adopted in existing regional reliability modeling. To address these limitations, this paper proposes a novel region-aware field reliability prediction method for power batteries under heterogeneous conditions, leveraging an improved deep clustering algorithm. First, environmental factors and a macro-level socioeconomic covariate associated with regional field reliability are systematically analyzed and identified, and a dynamic panel dataset is constructed. Mutual information is employed to quantify their statistical associations with regional field reliability. Second, an improved Transformer-based multi-kernel ensemble spectral clustering (TMESC) algorithm is developed to enable intelligent regional classification driven by heterogeneity in observed regional characteristics. Third, based on the derived regional clusters and available failure data, experimental results show failure-rate prediction errors below 5% and generalize to unobserved regions without retraining.

Jichao Hong, Lei Zhang

Voltage inconsistencies in lithium-ion battery packs are a primary driver of performance degradation and thermal runaway. Accurate diagnosis from real-world operational data remains hindered by highly dynamic driving conditions, continuous measurement noise, and scarce labeled faults. To address these challenges, an unsupervised anomaly detection framework utilizing a time-segmented Density-Based Spatial Clustering of Applications with Noise algorithm is proposed. High-dimensional voltage features, capturing both transient fluctuations and long-term aging behaviors, are extracted via a multi-scale sliding window mechanism. Principal Component Analysis is subsequently applied to decouple absolute voltage levels from dynamic inconsistency patterns. Time-segmented clustering then facilitates highly efficient local anomaly detection, generating a continuous Consistency Health Index for the quantitative grading of individual cell health. The framework was rigorously validated using actual data.

Muhammad Waqar, Yong-Woon Kim

The rapid growth of electric vehicles (EVs) is increasing stress on power distribution networks by amplifying peak demand and reducing charging-station utilization under uncoordinated charging. This study proposes a Large Language Model-guided Hyper-Heuristic (LLM-HH) for adaptive EV charging coordination. The framework uses a lightweight LLM-based meta-controller that monitors compact optimization feedback signals, including hypervolume (HV) improvement, population diversity, and stagnation, to orchestrate a portfolio of multi-objective evolutionary algorithms (MOEAs) through constrained solver selection and bounded parameter updates. Under an hourly coordination model, the framework coordinates a portfolio of MOEAs to jointly optimize three competing objectives: reducing on-peak demand, improving charger utilization, and preserving a cluster-level energy-per-session proxy across three behaviorally distinct charging regimes: Quick Top-Up, Home-Based, and Public-Stay. Experiments on a real-world Korean EV charging dataset demonstrate that the optimization-feedback-driven LLM-HH consistently improves Pareto front quality, achieving statistically significant improvements in Pareto-front quality.

Shiting Cui, Wenqiang Tao

Small-scale distribution-side entities, such as buildings and charging stations, can provide considerable flexibility for renewable energy (RE) accommodation but usually lack sufficient scale and institutional channels to participate independently in carbon-related accounting and settlement. This paper proposes a cooperative framework that connects a building cluster, a charging cluster, and a microgrid park for coordinated sharing of energy, carbon allowances, and green electricity certificates (GECs). Buildings and charging stations are aggregated into cluster-level participants, and carbon allowance sharing is modeled as coalition-level carbon-responsibility settlement among aggregated participants, with external carbon-market interactions implemented through eligible entities where applicable. The framework couples physical energy sharing with GEC-based green-electricity accounting and carbon allowance sharing, enabling low-emission flexible resources to support carbon-cost mitigation for the microgrid park. A bi-level Nash bargaining model is developed to coordinate coalition-level cooperation and intra-cluster settlement, and is solved by a predictor-corrector-mechanism (PCM) based alternating direction method of multipliers (ADMM). Case studies in Lhasa, China, show that compared with S4, where the three aggregated participants operate independently, the proposed framework reduces total operating cost and accounted carbon-emission liability by 43.40% and 7.87%, respectively.

Xiaobing Zhang, Zhifang Yang

With the large-scale integration of clean energy resources — including hydropower, wind, and solar power — together with battery energy storage systems (BESS), improving the efficiency of market-based resource allocation has become increasingly imperative for power systems. The day-ahead market (DAM) is conventionally cleared over a fixed 24-h horizon. For intertemporal resources such as reservoir-based hydropower and BESS, however, the end-of-day reservoir level or state-of-charge determines not only the feasible dispatch space for the current day but also the economic value attainable over subsequent operating days. When the end-of-day state is set by empirical rules or left unconstrained, as in the conventional treatments embedded in current DAM dispatch models, the future value of stored water or stored electricity is disregarded. This raises the multi-day system operating cost and can also reduce the realized profit of intertemporal resources, which is reflected in their loss of opportunity cost (LOC) evaluated separately in the settlement analysis. To address this study gap, we propose a DAM dispatch framework that explicitly embeds a calibrated end-of-day state penalty via multi-day look-ahead economic information.

Juan Wei, Lu Liu

Floating offshore wind farms are characterized by long offshore distances and poor transportation accessibility, which result in higher failure loss cost under fault conditions in existing radial networks. Therefore, it is urgent to propose a more reliable ring topology planning scheme for the collection system. To solve the problem, a two-stage joint planning method (TJPM) is proposed for floating offshore wind farm collection system to enhance operational reliability and reduce the full life cycle cost. In the first stage, the offshore substation location is optimized by the covariance matrix adaptation evolution strategy (CMA-ES), which performs an adaptive search by continuously and dynamically adjusting its probability distribution parameters. The optimal offshore substation location obtained in the first stage can be jointed as the constraints of the relationship optimization between wind turbines (WTs) and offshore substation areas in the second stage. In the second stage, the relationship is optimized based on a Tabu search algorithm, which is customized by integrating neighborhood operations such as reallocation and swapping on key WTs. The continuous Hopfield neural network is used for cable topology planning by linearizing the full life cycle cost of floating offshore wind farms, thereby improving the computational speed in the whole topology process of the collection system. Case studies demonstrate the superiority of the proposed joint planning method for floating offshore wind farms.

Chuanpu Zhang, Qinghao Cao

Accurate battery health estimation from incomplete and irregular charging records remains challenging because local fragments differ not only in length but also in diagnostic quality. This study interprets random local charging fragments as reliability-varying partial health observations and develops RLCF-Net, which fuses local Q(V) response, masked voltage-location context, and scalar operating conditions. A training-only multi-evidence procedure selects a coverage-stable mother voltage interval, while category-level empirical reliability is defined as the inverse calibration RMSE for each span-region category and used for prediction-level aggregation. The framework is evaluated on two laboratory datasets comprising 121 NCA/NCM cells and a field dataset from 20 commercial EVs. On the main NCA dataset, validation-derived category reliability transferred strongly to held-out cells (Spearman 0.

Lingfeng Lv, Xiaotong Zhang

Accurate estimation of photovoltaic (PV) potential under future climate scenarios is critical for global decarbonization and energy transition planning. This study employed the Global Solar Energy Estimation model to calculate PV potential and carbon mitigation across three Shared Socioeconomic Pathways (SSPs) using calibrated data of 2015 – 2100 from the CMIP6 data based on the Bayesian Model Averaging method. Results showed that the global annual mean PV capacity factor (CF) (power generation) was 0.1622 (2010 TWh), 0.1597 (1993 TWh), and 0.1543 (1961 TWh) under low (SSP1-2.6), intermediate (SSP2-4.5), and high (SSP5-8.5) carbon emission scenarios respectively, indicating that climate change impacts significantly affect global PV potential and its driving mechanisms.

Wei Xu, Yufeng Guo

Demand increases at some buses can load a shared distribution network and raise distribution locational marginal prices (DLMPs) at electrically coupled locations. Other users may keep their electricity use unchanged—for example, to maintain routine production or meet basic daily needs—yet face higher local prices and bills. Direct DLMP settlement shows where network charges are borne but does not explicitly allocate baseline-to-realized price changes to individual settlement deviations, creating the exposure–source mismatch studied here. Using regularized LinDistFlow DLMPs, the proposed settlement uses an ex-ante essential-service reference to separate network-component charges into baseline and deviation-related charges and traces changes in loss, voltage, and congestion prices from affected buses to source-bus deviations. Loss-price changes are decomposed analytically, whereas voltage- and congestion-price changes are allocated through baseline-conditioned Aumann–Shapley path integrals. The rule exactly preserves the network-component settlement total; its baseline part is allocated ex ante. Explicit differentiable LinDistFlow functions for the four DLMP components provide the required sensitivities. Flexible loads are modeled as internalizing the resulting marginal responsibility charge. Source-attribution marginal feedback can make the regularized price-anticipating game nonconvex; its smooth first-order conditions are therefore formulated as an effective decision-space var.

Bangyan Wang, Xiuli Wang

With the increasing frequency and intensity of typhoons causing severe damage to power systems, resilient transmission proactive-hardening and post-recovery (RTPHPR) has emerged as a crucial strategy for enhancing power system resilience. This paper presents a comprehensive RTPHPR scheme specially designed to mitigate cascading failure impacts with decision-dependent uncertainty (DDU) considered. Through spatiotemporal typhoon impact modeling, a probabilistic DDU set formulation is introduced to capture dynamic failure sequences with cascading effects included. To overcome the intractability of RTPHPR optimization, a metric-area-based estimation method is proposed and the decoupled sequential optimization framework for RTPHPR optimization is formulated. A tri-level robust optimization framework is established to address the line hardening challenge, accompanied by the proposed strengthened dictionary decomposition algorithm based on two heuristics. Case studies demonstrate the effectiveness of the proposed approach and highlight two critical insights: (i) the substantial impact of typhoon-induced cascading failures on system resilience, and (ii) the importance of incorporating cascading failure considerations in resilience hardening strategies.

Junhua Zhu, Xiaobing Yu

The increasing penetration of wind and photovoltaic generation introduces significant uncertainty and security challenges to the operation and scheduling of modern power systems. Many existing optimal power flow (OPF) studies typically treat renewable uncertainty, load variability, and contingencies separately, so the influence of these factors on the feasible region and on the trade-offs among economy, network efficiency, and voltage security remains poorly characterized. Moreover, most adopt a single constraint-handling strategy, which limits their search capability in the resulting strongly constrained problem. To close these gaps, this paper makes two contributions. First, a renewable-integrated multi-objective optimal power flow (MOOPF) framework is developed to model probabilistic wind and PV generation and to evaluate three groups of cases: baseline MOOPF cases, representative-load MOOPF cases, and selected-contingency security-constrained cases. The resulting formulations are nonconvex constrained multi-objective optimization problems. Second, a reinforcement-learning-based dual-population evolutionary algorithm (RLDPEA) is proposed to solve this problem. Two co-evolving populations adopt the ε-constraint method and a constraint-ignoring strategy, enabling effective navigation of both feasible and infeasible regions, while embedded Q-learning adaptively selects parents and operators from archive feedback to balance feasibility, convergence, and diversity.

Yuqian Fan, Yaqi Liang

Accurate estimation of the state of charge (SOC) is critical for the safe and efficient operation of lithium-ion batteries. However, SOC estimation based solely on external electrical signals struggles to capture internal battery states under dynamic loading and temperature variations, thereby limiting accuracy and robustness. To address this limitation, we propose a SOC estimation framework that integrates multiphysics sensing with electrochemistry-constrained sequence modeling. Specifically, an implantable fiber Bragg grating (FBG) sensing scheme is developed by combining an armored FBG with a bare FBG, enabling in situ decoupled measurement of internal thermo-mechanical states. Building on this sensing paradigm, a dual electrochemical-constrained Mamba framework (DEC-Mamba) is proposed. The framework employs an electrochemistry-constrained state-space model (SSM) as the sequential backbone, where electrochemical constraints are embedded into the state transition and update processes. In addition, physics-informed regularization during training and an uncertainty-aware adaptive calibration mechanism during inference are introduced to improve physical consistency and mitigate residual estimation bias under non-stationary operating conditions.

Zhilong Lv, Shiqi (Shawn) Ou

Reliable task-level energy assessment is essential for sustained mission execution and safe operation of battery-powered mobile robots under dynamic conditions. State of charge (SOC) and fixed-capacity estimates provide limited information about the effective energy reserve under changing electrical, thermal, and locomotion conditions. This study proposes a two-level indicator system comprising task-available energy (TAE) and a predictive relative risk margin. TAE characterizes the energy reserve available for a specific task under current operating conditions, extending conventional SOC-based assessment toward task-conditioned energy availability. An 85-day operational dataset covering five gait modes is segmented into task fragments. The proposed physics-constrained multi-task model combines categorical embeddings, stacked long short-term memory encoding, temporal attention, and current-state fusion to jointly predict TAE, future energy consumption, future minimum voltage, and future maximum battery temperature. On the independent test set, normalized root-mean-square error (NRMSE) values of 1.34%, 1.60%, 0.68%, and 0.59% are obtained for the four targets, respectively.

Xinxiong Jiang, Jian Xu

The development of renewables and electricity demand induces the growth of inter-area power transfer of power systems, potentially causing dynamic congestion events (DCEs) of transmission interfaces that threaten system stability and adequacy. The lack of procedures for perceiving real-time DCEs often leads to inadequate time for decision-making and resource mobilization. In this paper, a trustworthiness-enhanced learning approach is proposed for online probabilistic early warning (PEW) of DCEs, promoting the system proactivity in resisting risks. An explainable PEW network is first built utilizing tensorized neural blocks and tandem attention, enabling more explicit variable-wise information encoding. Next, an interface for static covariates is designed to fuse heterogeneous data and improve prediction performance. Then, an ensemble confidence calibrator is developed to modify the mapping between logits and congestion posterior probabilities via negative log-likelihood mini-mization, outputting more credible multi-class probability estimates. Test results verify the efficacy of the proposed trustworthiness-enhanced PEW method. By combining class predictions with calibrated probability estimations, more comprehensive decisions can be made.

Lilly Lingrun Yang, Siqi Ke

Fragile states face low electrification rates, acute vulnerability to climate change, and a dependence on imported diesel, which can cause violence related to fuel transport. To address these unique challenges, we develop a two-stage multi-objective stochastic optimization model for planning off-grid hybrid renewable energy systems (O-HRES) that jointly optimizes upfront investment, diesel consumption, community coverage, and supply reliability. The first stage determines the technology mix and installed capacities, whereas the second stage determines hourly dispatch. This complex, large-scale optimization problem is solved by leveraging an augmented 𝜖-constraint (AUGMECON) approach, whose results are compared to the true optimum and a multi-objective metaheuristic benchmark (NSGA-II). Uncertainty in renewable availability is captured using multiyear historical weather data and a new iterative extension of an existing integer programming-based (IP-based) scenario reduction method. Our scenario reduction method provides 4000-fold runtime savings with a negligible (0.23%) hypervolume loss. Moreover, AUGMECON obtains 4.2% higher hypervolume than NSGA-II and requires only 10% of the runtime of NSGA-II. Overall, our combination of iterative IP-based scenario reduction and AUGMECON-based multi-objective stochastic optimization successfully scales to real-world problem sizes in fragile states. Five case studies across three sub-Saharan African countries with site-specific.

Huangyi Zhu, Xiaofan Zhang

Ionic osmotic energy conversion harnesses salinity-gradient energy through selective ion transport in charged nanochannels. While existing studies primarily focus on high-performance single-unit materials, large-scale applications require integration of multiple units to enhance power output. However, strong nonlinear coupling among ion transport, charge accumulation, and interfacial processes prevents simple linear scaling of individual unit performance, with key parameters and their coupling mechanisms at the system level remaining poorly understood. In this study, a loss factor (Lf) is proposed to quantify power loss in tandem power generation units (T-PGU). Based on an equivalent circuit model, a quantitative relationship between Lf and the electrical signal ratios of individual units in T-PGU is established. System parameters are further classified into shared operating and channel-specific categories for investigating power output and energy loss under varying conditions through experiments and simulations. Results reveal a trade-off between maximum output power (Pmax) and Lf with concentration variations. Increasing temperature enhances Pmax with minimal impact on Lf, while KCl electrolytes significantly improve performance, reducing Lf to 4 × 10⁻⁴ compared to NaCl and LiCl. Additionally, tuning nanochannels with lower current enables simultaneous improvement in power output and reduction in Lf. Based on these findings, an optimization strategy involving temperature elevation, electrolyte substitution, and surface charge matching with pH regulation is proposed and validated, achieving 283% increase in Pmax and 92.5% reduction in Lf.

Ruizhe Deng, Yuhan Liu

Accurate global photovoltaic (PV) mapping is essential for renewable energy planning, grid integration, and carbon-neutral transition assessment. AI-based remote sensing segmentation offers an efficient way to update PV inventories, but pixel-wise annotation of PV imagery remains expensive at large scale, especially across distributed rooftop systems and centralized utility-scale plants with different resolutions and geographic backgrounds. A key practical challenge is therefore to achieve high-accuracy PV segmentation with the smallest possible annotation budget under realistic labeling constraints. To address this challenge, we propose a hierarchical CLIP-calibrated active learning (AL) strategy for label-efficient global PV segmentation. Due to the heterogeneous appearance of PV scenes, CLIP is introduced as an external visual-semantic calibrator, and sample acquisition is designed to enhance both coverage and efficiency in PV scene mapping, while also validating the approach through worldwide distributed and centralized PV benchmarks that confirm its robust performance. Furthermore, deployment analysis quantifies the labeling savings achieved for high-accuracy PV mapping.

Quan Zhou, Fei Chen

Direct methanol fuel cells (DMFCs) are promising power sources, but their practical performance remains limited by sluggish methanol oxidation, CO poisoning, Pt-site degradation, and low Pt utilization. This review critically examines Pt-centered interface engineering for the methanol oxidation reaction (MOR) in DMFC anodes. First, MOR pathways, performance descriptors, in situ and operando methods, and the factors governing the transfer of half-cell activity to DMFC output are assessed. Representative catalysts are then compared according to the dominant kinetic or degradation limitation addressed by their local interfaces, covering adsorption regulation, pathway control, oxygen-species cooperation, Pt-site retention or regeneration, and cooperative low-Pt utilization. The analysis assigns the dominant function of multifunctional interfaces, evaluates that function using process-specific evidence, and determines whether it survives working-state evolution and membrane electrode assembly integration. Cross-case comparison shows that material class does not uniquely define function and that similar activity, CO tolerance, or durability may arise from different mechanisms. It further distinguishes suppression of adsorbed CO (CO_ads) formation from oxidation of pre-adsorbed CO_ads, nominal oxophilicity from verified water activation, and structural retention from functional retention and controlled regeneration. Finally, problem-matched design rules are proposed for efficient, low-Pt, and durable DMFC anodes.

Yan Sui, Nan Xu

"Infoergy" is not a typo, but rather the energization of information, which is an abbreviation for "information-energy". Vehicle energy-saving is progressively evolving toward intelligent energy management systems that integrate information interaction; however, there remains a lack of quantitative description of the coupling relationship between information quantity and energy consumption. This hinders the full utilization of information to uncover energy-saving potential in energy management decisions and prevents an objective assessment of information's contribution to enhancing system energy efficiency. Therefore, this study proposes for the first time an information-energy equation that describes the intrinsic connection between information and energy in vehicle energy management technology. Aiming at the comprehensive information representation of multi-source time-varying information within the context of vehicle energy management, this equation defines vehicle velocity as the observable random variable of working condition information to quantify its uncertainty. An energy-saving potential evaluation system is then established. Then, by utilizing information metrics and energy-saving indicators to construct a nonlinear mapping model, it reveals the impact of working condition complexity on energy consumption from the perspective of information. The validation results demonstrate that the proposed infoergy equation exhibits excellent fitting accuracy and generalization capability.

Zeqing Wu, Yunfei Mu

As residential energy systems become increasingly electrified and coordination-intensive, home energy management systems (HEMS) must operate economically while remaining aligned with evolving user-authorized comfort and service boundaries. Existing personalization approaches rely on questionnaire initialization, fixed preference parameters, or indirect behavioral corrections, limiting alignment under changing needs and the use of runtime natural-language feedback for long-horizon calibration. This paper proposes a large language model (LLM)-enabled human-in-the-loop framework that converts user utterances into authorization-aware, contract-constrained, machine-checkable events for downstream control and preference updating. The LLM is a contract-constrained semantic interface, not a direct controller. Explicit intents support bounded execution and direct profile correction, whereas vague feedback requires option negotiation and user confirmation before bounded updating. A Contract-Informed Preference Update (CIPU) mechanism combines structured interaction evidence with projection and trust-region stabilization, while model predictive control remains the safety-constrained execution layer. In simulations, the proposed method reduces normalized preference distance by 22.50% and complaints by 74.46% relative to Survey MPC across three role-conditioned profiles.

Yincheng Zhao, Zhan Xiong

With the development of renewable energy and the increase of carbon emission limits, the operation optimization of power systems under the consideration of the coupling of the electric energy market, carbon emission market, and green certificate (GC) market has received wide attention. However, the high nonlinearity and dynamic characteristics of the power system after introducing renewable energy sources make it difficult to obtain the optimal operation decision under the coupled electricity-carbon-green certificate market. To solve this, we formulate a hierarchical game between the VPP operator and multiple aggregators. Specifically, a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based MADRL algorithm employs a centralized training with decentralized execution (CTDE) framework to optimize the operator's multi-aggregator dispatch strategy. Meanwhile, multi-type resource aggregator models are developed to minimize operational costs, with a generalized regression neural network (GRNN) introduced to approximate the nonlinear mapping between the V.

Jia Yan, Yue Kang

Thermal runaway (TR) in lithium-ion batteries poses a critical challenge to the safety and reliability of electric vehicles and large-scale energy storage systems. Conventional battery management systems (BMSs), which primarily rely on voltage and temperature signals, frequently exhibit delayed response and limited capability for early-stage fault detection. Gas signals, generated at the early stages of TR, provide rich physicochemical information on internal reactions and offer unique potential for timely warning and fault diagnosis. This review presents a gas-signal-driven early warning framework that systematically integrates mechanism understanding, signal sensing, and intelligent decision-making. The framework is designed to upgrade current BMS architectures without major hardware redesign. Specifically, this review (i) deconstructs multi-stage gas generation mechanisms, characteristic gas fingerprints across different battery chemistries, and their spatiotemporal evolution; (ii) critically evaluates state-of-the-art gas sensing technologies and their applicability to practical battery systems; and (iii) synthesizes intelligent early warning architectures, with an emphasis on multi-sensor fusion and AI-based methods to enhance detection reliability, extend warning lead time, and enable predictive safety interventions. In addition, key engineering challenges-including trace gas detection under realistic conditions, environmental interference, long-term sensor stability, and constraints for edge deployment-are systematically addressed.

Wanbing Zhao, Yan Xu

Integrated energy systems (IES) face coupled uncertainties from variable renewable generation, electricity and heat demands, and electricity prices. Forecasting models optimized only for statistical accuracy may therefore produce scenarios that are poorly aligned with downstream economic and risk-sensitive operation. This study develops an end-to-end framework that combines Mamba-based joint scenario generation with differentiable two-stage stochastic scheduling. The forecaster generates aligned multi-horizon trajectories for wind power, photovoltaic output, electric load, heat load, and electricity price. A scenario-wise entropic imbalance-exposure score is embedded in the scheduling layer. A threshold-based Entropic Value-at-Risk (EVaR)-inspired loss complements prediction and regret objectives during training. Simulation tests compare deterministic, stochastic, and entropic risk-aware cases. The complete Sto-EVaR case achieved the lowest operating cost, decision regret, empirical EVaR, and 95th-percentile cost while maintaining forecast accuracy comparable to the stochastic baseline. It also reduced realized electrical and thermal imbalance in the representative dispatch window. These results indicate that jointly learning uncertainty scenarios from prediction, economic, and entropic risk signals significantly enhances operational performance.

Yang Wang, Shaojie Zhang

Rechargeable aqueous zinc batteries fail through reactions that couple the two electrodes as well as through local degradation. Dissolved cathode products, soluble redox intermediates and electrolyte gradients can cross the separator, alter the receiving interface and redistribute active material, zinc and water. This Review defines electrode crosstalk by a source, a transmitted species or perturbation, and a consequential interaction at the opposite electrode. It distinguishes direct chemical transfer from electrolyte-mediated coupling while allowing both pathways to operate in the same cell. Manganese, vanadium, Prussian blue analogue, halogen, selenium and organic cathodes are compared through their carrier chemistry and zinc-side reactions. Liquid, gel and solid electrolytes are examined in terms of their conducting phase, species partitioning and interfacial contact. A six-level evidence hierarchy separates source generation, crossover, arrival, receiver reaction and causal intervention. Consistent definitions of permeability, molar dose and reaction-associated charge loss connect these measurements with cell capacity without conflating ionic charge and reaction stoichiometry. Quantitative examples show how imported species and interfacial regulation affect corrosion, hydrogen evolution and zinc deposition. Mitigation is assessed against resistance, recoverable inventory and cell-level energy, including the possibility of relocating failure.

Hongyu Sun, Peidong Zheng

Accurate estimation of battery state of health (SOH) is essential for ensuring the safety, longevity, and reliable operation of energy storage systems. However, existing data-driven SOH estimators still face two coupled limitations. First, many methods rely on fixed or manually selected aging features, which may become unreliable when degradation-sensitive regions shift across battery chemistries and operating protocols. Second, localized degradation-sensitive responses are often modeled as isolated feature vectors or purely sequential inputs, making it difficult to capture non-stationary variations in local-response patterns and inter-fingerprint dependencies within each cycle. To address these limitations, we propose an Electrochemical-Guided Time-Frequency Transformer (EG-Former). EG-Former first introduces a battery-specific fingerprint detection module that adaptively extracts incremental capacity (IC) peaks or current-switching points as compact degradation-sensitive markers. A graph convolutional embedding then encodes statistical dependencies among localized fingerprint regions, enabling structured representation of coupled intra-cycle responses. An adaptive spatial Transformer further models global.

Zaixin Li, Jia Zhang

Zengze Zhu, Long Zhao

Geometric orientation is an overlooked design factor in cross-flow planar solid oxide fuel cells (SOFCs). A three-dimensional multiphysics model was developed to evaluate the effects of cell aspect ratio and orientation on electrochemical performance, current-density uniformity, flow resistance, and thermal behavior. Five geometries with comparable active areas (~5500 mm²) and aspect ratios of 1.00 – 2.20 were evaluated at cathode air flow rates of 900 – 2700 sccm. At 0.7 V and 1800 sccm, orientation caused up to 24.04% variation in average current density. Rotating the 62 × 98 mm² cell to 98 × 62 mm² increased the average current density by 15.81% (4612 to 5341 A m⁻²), whereas rotating the 50 × 110 mm² cell to 110 × 50 mm² reduced it by 24.04% (5138 to 3903 A m⁻²). The 98 × 62 mm² cell achieved the highest Mean score 1 (1.000) but also the highest maximum temperature (1125.29 K), while the 62 × 98 mm² cell achieved the highest Mean score 2 (0.815). The 50 × 110 mm² cell had the lowest current-density coefficient of variation (0.421) but the largest pressure drop (89.05 Pa). These results reveal trade-offs among electrochemical output, current-density uniformity, fuel utilization, pressure drop, and thermal reliability.

Yangyang Wei, Sheng Chen

Producing hydrogen via renewable electrolyzer plants (REPs) is crucial for reducing carbon emissions in industrial sectors. Meanwhile, with renewable energy generation and electrolyzer units integrated at a common grid connection, these plants provide high-quality resources for regulating power grid operations. Nonetheless, fully utilizing this potential relies on the profitable operation of REPs across joint energy and frequency containment reserve markets in conjunction with the application of data-driven reinforcement learning (RL) scheduling approaches to counteract the stochasticity in renewable generation and the volatility of markets. However, current scheduling frameworks often neglect detailed internal thermal-electrical dynamics and discrete operational states, which result in sub-optimal or physically infeasible policies. The present work addresses this issue by proposing a comprehensive imitation-enhanced RL (IE-RL) scheduling framework. First, the framework incorporates a physics-informed neural network surrogate model to capture transient electrolyzer dynamics accurately. Then, the operational environment is decoupled into continuous subspaces based on whether the electrolyzer unit is in the on state or in the standby state, and dedicated expert policies are pre-trained for each operational environment. Finally, the IE-RL agent is tailored to optimize scheduling performance while ensuring operational feasibility.

Thai-Quyen Quach, Jin Young Park

Solid oxide fuel cell (SOFC) systems face significant performance and cost challenges due to stack degradation and the resulting off-design operation of balance-of-plant (BoP) components. This study presents an optimization framework for SOFC operation that explicitly accounts for stack degradation and off-design BoP dynamics. A 10-cell hydrogen-fueled SOFC stack was fabricated and tested under varied flow and humidity conditions to obtain polarization data, which were used to calibrate the system model with an R² of 94.7 – 99.2%. The calibrated model, coupled with surrogate-based optimization, enables time-segmented scheduling of current density and fuel utilization with periodic setpoint updates over a 20-year techno-economic assessment based on levelized cost of electricity (LCOE), electrical efficiency, and lifetime electricity output. The results show that, under the assumptions of the representative case study, the optimized schedules can reduce LCOE by up to 16%, increase lifetime electricity output, and improve overall efficiency compared to fixed operation strategies.

Guiqi Hou, Wei Yuan

Characterizing the thermophysical evolution preceding overcharge-induced thermal runaway (TR) remains important for improving the thermal safety modeling of lithium iron phosphate batteries. Fixed thermophysical parameters remain commonly used in reduced-order battery thermal models, which limits representation of the evolving thermal response during overcharge-induced TR. This study proposes a temperature gradient inversion method integrated with a coupled electrochemical-thermal-mechanical framework to dynamically identify these transient parameters. The method characterizes the coupled evolution of effective lumped thermal resistance ( R C ) and heat capacity ( C C ) during overcharging. R C exhibits pronounced stage-dependent fluctuations and reaches a maximum of 5.2 K/W, consistent with progressive interfacial and structural changes during overcharging. Concurrently, C C evolves from an initially stable regime through progressive attenuation to terminal depletion. These parameter trajectories provide effective dynamic indicators of the evolving internal thermal state under the investigated overcharge conditions. Compared with the constant-parameter reference, incorporating the identified dynamic thermophysical parameters reduces the core-temperature root mean square error by up to 64.5% for independent-cell validation. This work provides a physics-based dynamic modeling framework for representing overcharge-induced TR thermal evolution and establishes.

Elenya Grant, Kaitlin Brunik

In the United States, roughly 95% of hydrogen is produced via steam methane reforming (SMR) and natural gas is becoming increasingly valuable for other applications such as power generation and export. This study evaluates pathways to diversify supply and lower hydrogen costs through a high-resolution, physics-based, open-source assessment of off-grid hybrid energy systems for electrolytic hydrogen. We resolve plant design, nonlinear electrolyzer performance and degradation, and hydrogen storage across 58,225 sites, executing approximately 173 million simulations that sweep 198 wind-solar-battery configurations, five hydrogen storage options and three electrolyzer CapEx scenarios, to produce geographically resolved design guidance. Key findings: (1) Hybrid and hybrid-battery plants minimize levelized cost of hydrogen (LCOH) at nearly all sites, with optimal designs consistently oversizing generation relative to electrolyzer capacity. (2) Declining electrolyzer costs simplify plant designs by eliminating batteries, this change in plant design leads to lower capacity factors and shorter stack lives. (3) Electrolyzer cost reductions yield larger LCOH reductions without storage than with buried pipe. (4) Optimal storage adds approximately $0.64/kg on average; buried pipe is never the least-cost option but is widely considered deployable today. Together, these results show that off-grid hybrid energy systems can become increasingly competitive with SMR as electrolyzer CapEx costs fall and indicate where that competitiveness emerges first. Future work should assess advanced dispatch to limit degradation and LCOH, optimize for fixed delivery profiles, and expand to.

Shichao Wang, Shuo Wang

Rubidium, as a strategic critical material, exhibits broad application prospects in many high-tech fields, including aerospace, bioengineering, medicine, energy, and environmental science. At present, rubidium resources mainly occur in solid minerals, such as lepidolite and pollucite, as well as in liquid resources, including salt lake brines, seawater, and mineral leachates. With the gradual depletion of high-grade rubidium-bearing ores and the continuous increase in rubidium prices, the extraction of rubidium from liquid resources with abundant reserves has become a research hotspot. Based on a brief overview of the global status of rubidium resources, this paper systematically reviews the main technologies for extracting rubidium from liquid mineral resources, including fractional crystallization, precipitation, solvent extraction, adsorption, and membrane separation. The processes and underlying mechanisms of each method are summarized in detail. Their applicability and technical bottlenecks are comparatively analyzed. Corresponding improvement strategies are proposed to address these limitations. On this basis, future research should further strengthen fundamental studies on rubidium extraction to provide theoretical guidance for the structural optimization of high-efficiency separation materials and process design. Meanwhile, novel adsorbents, extractants, and membrane materials with high selectivity and stability should be developed, and their performance should be validated in real and complex liquid resource systems. In addition, integrated processes coupling solvent extraction, adsorption, and membrane separation should be promoted to enhance mass transfer efficiency and resource utilization.

Chengbing Chang, Jianying Guo

Microbial methods offer environmentally friendly, low-energy strategies for enhanced mineral CO2 sequestration. Most studies focus on mineral leaching, and while coupling leaching with mineralization to achieve efficient carbon fixation remains limited. In this study, Talaromyces pseudofuniculosus . hzt-3 ′ was employed to promote wollastonite dissolution. Leaching conditions were optimized using single-factor experiments and response surface methodology (RSM), evaluating the effects of initial pH, inoculum size, pulp density, and temperature on Ca2+ and Si4+ leaching. Optimal parameters were initial pH 7.35, inoculum size 1.17 %, pulp density 0.4 %, and temperature 30 ◦C, achieving 88.79 % Ca2+ and 12.61 % Si4+ extraction. Residue analysis revealed reduced lattice spacing, more dispersed Ca2+ distribution, and that fungal-secreted organic acids lowered solution pH and complexed with Ca2+, promoting dissolution; hyphal mechanical action disrupted the mineral lattice, enhancing reactivity. Carbonation of the leachate under alkaline conditions (40 ◦C, CO2 flow rate 100 mL/min, stirring speed 500 rpm, NH3⋅H2O concentration 1.2 mol/L) reached 97.29 % Ca2+ mineralization efficiency within 5 min. This work establishes a fungal-enhanced wollastonite leaching and indirect mineralization strategy for CO2 sequestration, providing theoretical and experimental foundations for industrial.

Kaikai Chen, Yanxin Zhang

To achieve the synergistic high-value utilization of gold tailings (GT) and coal gangue (CG), mullite composite ceramics were prepared via reaction sintering using coal gangue and gold tailings as the main raw materials, with the addition of industrial alumina and AlF3. The effects of AlF3 content, gold tailings content, and sintering temperature on the phase evolution, microstructure, and properties of the samples were systematically investigated. The results show that the sintered samples consist primarily of mullite, corundum, quartz, and an amorphous aluminosilicate phase. After optimization of the AlF3 content, samples containing 40 wt% gold tailings exhibited the best overall properties when sintered at 1500 °C, with flexural strength, bulk density, apparent porosity, and water absorption reaching 110.88 MPa, 1.79 g · cm³, 0.73 %, and 0.41 %, respectively; the resulting low-density/high-strength characteristics are associated with the interwoven mullite framework, the intergranular aluminosilicate amorphous phase, and isolated residual pores. FactSage calculations suggest that K- and Na-bearing components promote the formation of low-melting alkali aluminosilicate liquids. The study shows that the properties of this ceramic depend on the balance between liquid-phase-assisted densification and the formation of the mullite framework. This study achieved the synergistic utilization of two types of bulk mining solid waste — coal gangue and gold tailings — and provides a reference for the preparation.

Junhang Sun, Yuqing Pan

Controlling the particle size distribution (PSD) of high-pressure grinding rolls (HPGR) presents a fundamental challenge in mineral processing, requiring the simultaneous coordination of three conflicting quality objectives: target size fraction yield tracking, coarse particle suppression, and over-grinding mitigation. This paper formulates the HPGR PSD control problem as a switched affine nonlinear system with three operating modes (coarse particle breakage, over-grinding mitigation, and efficiency maintenance), and proposes a co-design methodology integrating mode-dependent state feedback control with multiple Lyapunov functions (MLF) based switching law. First, a switched affine nonlinear model is established with explicit control objective constraints. Second, mode-dependent feedback gains are synthesized via linear matrix inequalities (LMIs). Third, a three-layer composite switching law is constructed incorporating particle size thresholds, MLF constraints, and minimum dwell time, with theoretical proofs of exponential stability under ideal conditions and uniform ultimate boundedness under bounded disturbances and estimation errors. Model-based numerical evaluation using nonlinear HPGR models identified from industrial data shows that the proposed method achieves an aggregate constraint violation rate of 4.10%, compared with 7.50% for the Mamdani fuzzy-expert HPGR controller and 17.07% for gain-scheduled PID, while reducing IAE by 86.5% and 89.0%, respectively. The simulations also demonstrate robustness to the tested parameter perturbations and state-estimation errors. These results describe simulated closed-loop performance under the stated model assumptions.

Yuanhui Li, Zhou Fang

The processing capacity of high-throughput vision-based intelligent ore sorting systems is strictly constrained by the physical dead zones of mechanical actuators. Under random Poisson arrivals, particle clustering in space translates to severe time-domain congestion that degrades sorting performance. While algorithmic optimization cannot overcome this physical bound and homogeneous multi-stage configurations suffer from superimposed dead-zone penalties, this paper proposes a heterogeneous dual-stage sorting architecture and a corresponding adaptive collaborative scheduling strategy (ACSS). By cascading a primary high-torque actuator (HTA, modeled as M/D/1/0 loss queue) and a secondary high-dynamic actuator (HDA, modeled as M/G/1/0 loss queue), the architecture achieves spatiotemporal decoupling of congestion via multi-stage spatial stratification. The ACSS integrates dynamic time windows, state-holding logic and a predictive abort mechanism to proactively mitigate sorting losses arising from combined dead-zone effects across the two stages. Discrete-event simulations (DES) and hardware-in-the-loop (HIL) experiments demonstrate that, at 50 % target particle proportion, the system raises the 80 %-recovery threshold from 2 particles/s (single-stage HDA) to 4 particles/s, doubling the processing capacity. The proposed system exhibits an asymmetric U-shaped performance characteristic.

Lingzhi Zhou, Min Wei

Selective flotation separation of ilmenite from calcium–magnesium silicate gangue remains challenging due to the weak natural hydrophobicity of ilmenite and the insufficient selectivity of conventional collectors. This study evaluates 8-hydroxyquinoline (8-HQ) as a selective collector for ilmenite–diopside separation. Using conditions of pH 7.0 ± 0.2 and an 8-HQ concentration of 1.0 × 10 −4 mol L−1, single-mineral recoveries of ilmenite and diopside were determined to be 90.1% and 25.0%, respectively. Flotation of artificially mixed minerals under these conditions yielded a concentrate containing 43.8% TiO2 with a recovery of 86.7%, thus demonstrating effective separation of ilmenite from diopside. The contact angle of ilmenite increased from 42.1° to 78.2° after 8-HQ treatment, while diopside's increased from 27.2° to 35.8°. UV–vis analysis revealed an adsorption amount of 0.138 mg g−1 of 8-HQ on ilmenite, approximately 3.6 times that on diopside. Additional spin-polarized calculations suggested an adsorption energy of approximately −1.66 eV with optimized Fe–N and Fe–O distances of 2.14 and 2.48 Å. These results indicate that selective adsorption of 8-HQ on exposed Fe sites enhances ilmenite's hydrophobicity, facilitating its effective flotation separation from diopside.

Xuyang Wang, Mingyuan Rao

The efficient and environmentally friendly utilization of low grade boron-bearing iron concentrate resources remains challenging due to the complex mineral composition and strong association between boron and iron minerals, coupled with high energy consumption and environmental impacts associated with conventional coal-based reduction processes. This study aims to explore the potential of corn straw as a biomass-derived reductant to replace conventional coal powder for the high temperature reduction and magnetic separation of boron-bearing iron concentrate. The pyrolysis characteristics of corn straw and its reduction behavior were investigated, followed by electric furnace reduction and magnetic separation experiments. The effects of reduction temperature, reduction time, and C/O molar ratio on iron metallization and boron iron separation were systematically evaluated. Characterization of the reduction products was conducted using ICP-AES, XRF, XRD, and SEM to elucidate the reduction and separation mechanisms. The results indicate that corn straw undergoes pyrolysis to generate H2, CO, and biochar, forming a gas-solid composite reduction system conducive to the conversion of iron oxides to metallic iron. Under optimal conditions of 1300 °C, a C/O molar ratio of 1.2, and a reduction time of 30 minutes, the iron metallization ratio achieved 91.47%, with an iron concentrate exhibiting an iron grade of 87.9% after magnetic separation.

Hongda Xu, Yang Li

High-phosphorus oolitic hematite remains difficult to utilize efficiently because most direct reduction-magnetic separation processes rely on dephosphorization agents, which increase reagent consumption and process complexity. In this study, Huoshaoping high-phosphorus oolitic hematite was processed by direct reduction-magnetic separation without dephosphorization agents. Thermodynamic analysis and characterization of reduction atmosphere, phase transformation, iron-phosphorus distribution, microstructure, and magnetic properties were used to clarify the separation mechanism and ore-specific applicability. Under suitable conditions of 1200 °C, 70 min, and 10 wt.% anthracite, direct reduced iron with 93.07 wt.% TFe, 91.60% Fe recovery, and 0.087 wt.% P was obtained. The results show that favorable P occurrence, inherent Ca-bearing components, and spatial separation between metallic iron and P-bearing non-magnetic phases are essential. For high-phosphorus oolitic hematite with suitable mineralogical characteristics, this route can simplify the process and reduce reagent use, but its applicability still depends strongly on ore properties.

Zhicheng Zhong, Xiaoyu Sun

Rare earth elements (REEs), particularly middle and heavy REEs, are strategic metals critical to advanced technologies. Ion-adsorption type rare earth ores (IAREOs) constitute the primary global source of these REEs, making the development of efficient and environmentally friendly extraction methods highly significant. Conventional in-situ leaching using magnesium sulfate suffers from high reagent consumption and prolonged percolation cycles. Here, we introduce a novel approach combining low-concentration magnesium sulfate leaching with pulsed microwave irradiation, and establish a microwave-coupled column leaching system simulating in-situ conditions. Comparative experiments with conventional heating leaching reveal that, under low microwave energy density (1.19 W/g), a pulse regime of 20 s on – 5 min off optimally enhances leaching performance. Relative to conventional heating leaching, pulse microwave assisted leaching accelerated seepage by 32% – 39% and increased REEs extraction efficiencies by 6.42% – 10.73%. The study further explores pulsed microwave-assisted gradient injection leaching. A two-stage injection scheme (stage I: 1.25 wt% MgSO4, L/S of 2:5; stage II: 0.5 wt% MgSO4, L/S of 3:5), corresponding to a total MgSO4 concentration of 0.8 wt%, achieves a REEs recovery of 90.12%, representing a 20.54% increase compared with conventional continuous leaching at the same concentration, demonstrating the significant enhancement effect of pulsed microwave irradiation on the leaching process.

Qi He, Lang Luo

Mojtaba Mostafavi, Milad Alborzi Sibak

This study presents a numerical investigation of the deformation, breakup, and particle-retention behavior of particle-laden bubbles subjected to shear flow. A coupled Eulerian – Lagrangian framework employing the Volume-of-Fluid (VOF) method together with the isoAdvector interface reconstruction algorithm is developed to simultaneously resolve gas – liquid interface dynamics and particle transport. Particle attachment to the bubble surface is modeled using a hydrophobic interaction force, enabling the simulation of particle retention and detachment during bubble deformation and breakup. The effects of Capillary number, density ratio, and viscosity ratio on bubble morphology and particle retention are investigated. The results show that increasing the Capillary number changes the deformation regime from stable elongation to complete breakup. The maximum particle-retention ratio of approximately 42% is obtained at Ca = 0.5 after 2 s, whereas increasing the Capillary number to 1.0 reduces the retention ratio to approximately 29% because of breakup-induced particle detachment. As the density ratio increases from 0.001 to 2.0, the number of attached particles at t = 0.5 s increases from 23 to 40, although stronger breakup subsequently reduces long-term attachment stability. Increasing the viscosity ratio from 9.06 × 10 ⁻ 3 to 1 decreases the number of retained particles at t = 0.5 s from 34 to 28 owing to enhanced interface oscillations and particle migration. Bubble breakup is identified as the primary mechanism governing particle detachment through interfacial deformation and transient flow disturbances. The developed framework provides a robust computational tool for predicting particle-retention behavior in flotation and related gas – liquid – solid separation processes.

Bhavya Manjeera, Partha Sarathy Iyengar

Ilmenite recovery from heavy mineral sand processing rejects was investigated using an auxiliary electrode electrostatic roll separator (AEERS). The AEERS incorporates an independently positioned lower electrode beneath the grounded rotating drum, which is intended to extend electric field control into the particle discharge region relative to a conventional high-tension roll separator. A representative reject sample containing 11.97% ilmenite and 66.20% garnet, along with minor amounts of rutile, zircon, and sillimanite, was characterized and used as feed. A six-factor Central Composite Design (CCD) comprising 30 experimental runs was employed to evaluate the effects of feed rate, feed-roll speed, separation-roll speed, feed temperature, splitter position, and applied voltage on ilmenite recovery. A quadratic response surface methodology (RSM) model was developed and validated by analysis of variance (ANOVA), leading to a statistically significant model (F = 58.19, p < 0.00001, R2 = 0.9987). The maximum ilmenite recovery of 52.3% at a concentrate grade of 90.4% was achieved under specific operating conditions. Further analysis using decision tree regression and SHapley Additive exPlanations (SHAP) helped in classifying the operating space into zones and identifying the contributing variables impacting the recovery process.

Mehmet Hakan Morcali

Nickel laterite processing is commonly reviewed route by route, although ore mineralogy, thermal preparation, sulfur transfer, reduction selectivity, metal-carrier choice, and downstream refining are physically coupled. This critical review treats the thermal front end as one interface in an ore-to-product chain. A reproducible OpenAlex update search performed on 01 October 2026 retrieved 148 unique records; 59 advanced from title-and-abstract screening to manual assessment, supplemented by citation chaining and verified DOI metadata. Evidence is graded from concept or model to controlled laboratory, pilot or plant-adjacent, and industrial operating data. The synthesis separates limonitic and saprolitic front ends and places the legacy Caron route between reduction roasting and aqueous refining. It shows that low-carbon claims are controlled by ore grade, moisture, product basis, electricity and hydrogen source, allocation, and refining depth. In mixed-energy modeling, ferronickel and nickel pig iron required 309 and 598 GJ per tonne of contained Ni and emitted 18 and 69 t CO2-eq per tonne of Ni, respectively; these values are boundary-specific rather than universal rankings. A 2025 hydrogen-plasma study produced Ni-enriched ferronickel in 10-g batch tests and projected up to 18% energy savings and 84% lower direct CO2 emissions under renewable-electricity and green-hydrogen assumptions, but continuous operation has not been demonstrated. Kiln sulfur transfer and inhibition remain controlled laboratory evidence and govern operability, emissions, and product sulfur rather than direct decarbonization. Product claims must distinguish mixed hydroxide or sulfide precipitate, ferronickel, nickel pig iron, matte, and battery-grade salts because each carries different impurity and refining burdens. The resulting framework links mineralogy, front-end state, metal carrier, refining duty, residue closure, carbon boundary, and evidence maturity to define testable priorities for lower-carbon nickel supply.

Hamidreza Saffarian, Kevin P. Galvin

Building on our previous study, which demonstrated for the first time that froth flotation could selectively recover metallic silver from end-of-life photovoltaic cells, this work investigated the transitioning from batch mechanical flotation to an intensified continuous operation in a Reflux Flotation Cell. Approximately 22 kg of crystalline silicon solar cell material, equivalent to about 468 kg of original rooftop PV panels, was wet ground and fed directly to the single-stage flotation process. AEROPHINE 3418A and AEROFLOAT242 were used as collectors with methyl isobutyl carbinol (MIBC) as the frother. Following an initial transient period, the system reached a hydrodynamic steady state at approximately 42 min, after which the solids yield stabilised at around 1.25 ± 0.024 %. During this steady-state window, the concentrate contained an average silver grade of approximately 48.8 wt ± 1.16 %, corresponding to an 83-fold upgrade relative to the representative feed grade of 0.590 ± 0.006 wt% Ag. Silver was below the ICP-MS limit of reporting (0.001 g/tonne) in all steady-state tailings samples, corresponding to a calculated silver recovery of ~100 %. These results demonstrate that the high silver recovery previously achieved in the bench flotation can be sustained under continuous operation, while achieving full silver recovery and substantially greater upgrading in a single stage. The findings demonstrated a continuous, highly selective separation route for recovering metallic silver from end-of-life photovoltaic cells.

Hanjun Wu, Meichen Xu

In this work, trihydroxyl-and phosphate-functionalized Cu/Zn bimetallic organic frameworks (CZOH-2 and CZP-2) were synthesized via a solvothermal method, and applied for the extraction of low-concentration and high-value rare earth yttrium ions (Y(III)) from wastewater. Batch adsorption experiments demonstrated that both materials exhibited outstanding adsorption capacity, salt tolerance and adsorption selectivity at pH = 6. In the presence of coexisting interfering ions including La(III), Ni(II), Co(II), Mg(II) and Mn(II), the distribution coefficients of Y(III) on CZOH-2 and CZP-2 reached 10170.45 mL ⋅ g-1 and 17716.09 mL ⋅ g-1, respectively. The efficiency for Y(III) uptake stayed higher than 85 % after five cycles, revealing excellent reusability. Fitting results of adsorption isotherms and kinetic models indicated that the theoretical maximum capture efficiency of CZOH-2 and CZP-2 toward Y(III) were 139.70 mg ⋅ g-1 and 361.30 mg ⋅ g-1, respectively. The adsorption behavior of Y(III) on the two materials conformed to the characteristics of monolayer chemisorption. Analysis based on the Array of Adsorption Occupied Sites and External Diffusion Resistance-Internal Diffusion Resistance model showed that the rate constants of chemisorption were remarkably higher than that of physisorption, confirming that chemisorption dominated the entire adsorption process. According to the Approximate Site Energy Distribution theory, the adsorption sites on material surfaces presented obvious energetic heterogeneity.

Jiachang Xiao, Jun Wang

High-purity barium sulfate is essential for reflective coatings, radiation-shielding materials, and high-end fillers, where residual Fe, Al, and Si impurities affect whiteness and service performance. However, deep purification of natural barite is often hindered by quartz, Fe2O3, and aluminosilicate inclusions closely associated with the BaSO4 matrix. These limitations are primarily attributable to the structural stability of aluminosilicate impurities and low reactivity of quartz-type SiO2 during conventional leaching. This study proposes a combined roasting – sulfuric acid leaching – alkaline leaching process to achieve stepwise impurity removal from barite concentrate. The results indicate that, under the optimized conditions, Fe decreased from 400 ppmw to approximately 50 ppmw, Al decreased from 1700 ppmw to approximately 50 ppmw, and Si decreased from 3.11 wt% to 2.78 wt%, while BaSO4 purity increased from 89.64 wt% to 92.47 wt%. The overall Al removal efficiency reached 97.06%, and the final Fe content satisfied the impurity-control requirement of GB/T 2899 – 2017. The results demonstrate that roasting at 1023 K mainly promotes aluminosilicate decomposition through the cleavage of Si – O – Al bonds. During sulfuric acid leaching, Fe2O3 is preferentially dissolved, and part of the Al released from decomposed aluminosilicate enters the liquid phase. This study clarifies the stepwise purification mechanism of complex barite and provides a feasible route for preparing high-quality BaSO4 raw materials.

Zhiping Wen, Yuhang Tan

Online prediction of concentrate grade is important for flotation monitoring, optimization, and control. However, robust industrial prediction remains difficult because grade-related information is usually inferred from indirect and heterogeneous data. This review examines machine-learning-based concentrate grade prediction in froth flotation from Jan 2015 to Aug 2026. It focuses on four main data routes: froth vision, radiometric/XRF, laser spectroscopic, and process-variable data. The review discusses data representation, feature engineering, label alignment, model development, data quality, and explainability. Recent literature shows an increasing use of deep and structured learning architectures alongside conventional machine-learning methods. These approaches include CNNs, attention mechanisms, Transformer-based temporal or spatiotemporal models, semi-supervised learning, transfer learning, and hybrid models. They have been used to represent froth appearance, temporal dynamics, multimodal information, and limited-label data. Their reported advantages, however, depend strongly on data quality, label alignment, acquisition conditions, data partitioning, and validation design. Supervised learning remains the main route for direct continuous-grade prediction. Semi-supervised and unsupervised methods are mainly used to reduce label dependence or improve representation learning. Transfer learning is useful when target-domain labels are limited, but its reliability depends on source – target similarity. Future studies need clearer label-alignment rules, time or condition-based validation, modality-aware fusion, robust tests under operating-condition shifts, and explanation-guided model diagnosis.

Lei Xu, Jiahao Yang

Rare earth elements (REEs) are strategically important in the energy, defense, and other sectors. As the global energy transition accelerates, recovering REEs from value-bearing solid wastes has become an important route for building future strategic reserves. Coal-based solid wastes are regarded as promising secondary REE resources; however, conventional recovery technologies remain limited in terms of selective enrichment, environmental performance, and economic viability. This study developed an integrated metallurgical process combining ammonium chloride activation roasting with microwave-assisted leaching in a citric acid-ammonium fluoride system. The results showed that, at a citric acid concentration of 1 mol L⁻¹, an ammonium fluoride dosage of 9 %, a liquid-to-solid ratio of 50:3 mL g⁻¹, a microwave temperature of 80 °C, a microwave power of 600 W, and a microwave irradiation time of 120 min, the REE leaching efficiency from coal fly ash reached 80.45 %. During roasting activation, HCl gas generated by the decomposition of ammonium chloride disrupted the aluminosilicate phases in the coal fly ash and exposed encapsulated REE-bearing sites. During the subsequent leaching stage, microwave irradiation intensified molecular motion in the solution and solid phases and enhanced mass transfer at the solid – liquid interface.

Xinyi Yang, Xia Zhang

Phosphogypsum (PG) utilization is restricted by the presence of residual phosphorus (P), fluorine (F), and metal impurities. In this study, sulfuric acid leaching was investigated as an industry-compatible method for PG treatment. The effects of acid concentration, liquid-to-solid (L/S) ratio, temperature, and time were systematically evaluated, and the responses of different impurity fractions were analyzed through correlation analysis, XRD, SEM – EDS, gypsum phase composition analysis, and whiteness measurement. Under the selected conditions of 40 wt% H2SO4, a L/S ratio of 4:1, 50 °C, and 2.5 h, the removal efficiencies of operationally defined total P, total F, water-soluble P, water-insoluble P, water-soluble F, and water-insoluble F reached 86%, 82%, 94%, 82%, 97%, and 70%, respectively, while the apparent mobilization efficiency of selected metal impurities reached up to 88%. The treated PG retained its gypsum phase, while the whiteness increased from 40.3% to 61.85%, residual soluble P (0.0133%) and F (0.0063%) contents in PG met the first-class standard for Chinese building materials. Correlation analysis revealed distinct leaching-response patterns among the P, F, and metal impurities. Based on the observed impurity-removal patterns, an end-use-oriented graded purification framework was proposed for potential applications, subject to application-specific assessment.

Parthasarathi Ravi Raman, Samdavid Swaminathan

Copper recovery from waste printed circuit boards (WPCBs) is often limited by poor metal accessibility caused by polymeric and glass-fibre encapsulation, resulting in inefficient acid utilisation. In this study, density-based segregation using a gas – solid fluidized bed (GSFB) was combined with static nitric acid leaching to isolate the effect of physical pre-treatment on copper dissolution. Physical conditioning of the nominal 250 – 500 µm sieve fraction increased the copper content from 29.57 wt% to 80.7 wt% without additional comminution. Under identical leaching conditions, the conditioned feed showed significantly higher copper dissolution and acid utilisation than the unsegregated feed. At 4 M HNO3 after 1 h, the accessibility enhancement factor (AEF) and acid utilisation gain (AUG), which quantify improvements in copper dissolution per unit feed mass and per unit volume of leaching solution, respectively, were both approximately 3. The conditioned feed also exhibited rapid early-stage dissolution owing to improved copper accessibility, whereas diffusion-related limitations became increasingly important as leaching progressed. SEM – EDS and FTIR analyses showed a substantial reduction in the non-metallic fraction, changes consistent with improved copper accessibility, and progressive surface evolution during leaching. Overall, the results demonstrate that physical conditioning and the associated improvement in copper accessibility enhance static nitric acid leaching beyond the effect of increasing acid concentration alone.

Yan Long, Junhan Yang

The efficient separation between ilmenite and forsterite is critical for improving titanium resource utilization. However, the development of novel flotation collectors still heavily relies on inefficient trial-and-error experimentation. In this work, a high-throughput computational framework integrating Quantum Chemistry (QC), Machine Learning (ML), and Molecular Dynamics (MD) simulations was established for the design and screening of selective collectors. A dataset comprising 66 reported collectors was compiled, and 15 QC-derived descriptors were calculated at the PBE0/Def2-TZVP level. Among the evaluated ML algorithms, Gradient Boosting Regression achieved the optimal predictive performance, confirming a pronounced nonlinear structure – activity relationship between collector binding energies and their electronic descriptors. Feature importance analysis further identified the electron-donating ability of primary bonding atoms (ENDA1) and vertical ionization potential (VIP) as the dominant descriptors governing the interactions with Ti4+, Fe2+, and Mg2+ sites, revealing that electron-donating capacity and charge distribution play decisive roles in flotation selectivity. Accordingly, unsaturated hydroxamic acid collectors featuring C = C double bonds, including 2-OcHA (trans-2-Octenohydroxamic acid), 2-DeHA (trans-2-Decenohydroxamic acid), 2-DdHA (trans-2-Dodecenohydroxamic acid), 2-DyHA (trans-2-Dodecynohydroxamic acid), 9-HHA (trans-9-Hexadecenohydroxamic acid), and 9-OdHA (trans-9-Octadecenohydroxamic acid) were proposed as effective options.

Siyou Li, Lixia Li

In the staged grinding – staged magnetic separation process of magnetite ores, the selection of suitable grinding fineness is often mainly based on empirical design or single – index evaluation, which makes it difficult to balance mineral liberation degree, energy consumption, and magnetic separation performance. To this end, taking a – 3 mm magnetite ore after High Pressure Grinding Rolls (HPGR) as the research object, this paper introduces an experimental implementation method for determining suitable finenesses of staged grinding based on mineralogical properties, Bond Ball Mill Work Index, and magnetic separation performance. Results showed that a grinding fineness of – 0.074 mm passing 75 % was suitable for the first – stage grinding of this magnetite ore. Under this condition, the corresponding Bond Ball Mill Work Index was 8.00 kWh/t, while the concentrate yield of the first – stage magnetic separation was 53.32 %, with magnetic iron (MFe) grade and recovery of 51.00 % and 94.22 %, respectively. This proposed approach effectively integrates mineralogical characteristics, grinding energy consumption, and separation performance, and can provide a valuable reference for the optimization of staged grinding – staged magnetic separation processes for fine – grained embedded magnetite ores.

Mathieu Kayembe Musala, Matthieu Matthieu Tshanga

This study presents a comprehensive machine learning framework for the optimisation of metallurgical parameters for the grinding, leaching, and solid/liquid separation in a Congolese Copperbelt hydrometallurgical plant. Three deep learning architectures namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) were designed, trained, and comparatively evaluated. Models were trained to predict Cu and Co leaching recoveries from ten critical physicochemical process variables: feed rate, particle size distribution (PSD), percentage solids at key settling or decantation stages (Leach feed thickener (LFT), Counter current decantation (CCD4), Leach discharge thickener (LDT)), metal losses in overflow streams, and flocculant addition rates. The DNN achieved superior predictive performance with MAE = 1.328 % and R2 = 0.721 for Cu recovery, substantially outperforming CNN (MAE = 2.381 %, R2 = 0.017) and LSTM (MAE = 2.421 %, R2 = -0.024). Permutation-based feature importance identifies PSD (granulometry) as the dominant predictor (Δ MAE = +0.368), followed by metal losses and thickener underflow densities. A 3-month DNN forecast projects Cu recovery improving from the 2024 – 2025 mean of 96.0 % to 97.2 – 97.8 % as graduation.

Dongyue Sui, Baoxu Song

Alkaline regulators can alter the surface properties of scheelite and thereby affect its interaction with collectors. This study compared the effects of NaOH and Na2CO3 on the surface properties and morphology of representative crystal surfaces using microflotation tests, contact-angle measurements, atomic force microscopy (AFM), N2 adsorption – desorption measurements, X-ray diffraction (XRD), and inductively coupled plasma atomic emission spectrometry (ICP-AES). At pH 11, the mean scheelite recoveries were 90.41 % in the NaOH system and 81.93 % in the Na2CO3 system. The XRD patterns showed no appreciable change in the bulk crystal structure after either treatment. NaOH induced pronounced surface modification and increased the BET specific surface area from 0.0778 to 0.1696 m2⋅g−1; these changes coincided with a stronger NaOL-induced hydrophobic response. By contrast, Na2CO3 treatment was associated with surface flattening, a decrease in BET specific surface area to 0.0599 m2⋅g−1, and weaker NaOL-induced hydrophobic response. The combined AFM, BET, XRD, and ICP-AES results were consistent with localized surface modification in the NaOH system and carbonate-associated surface modification or passivation in the Na2CO3 system. In single-run closed-circuit tests of the actual ore, the NaOH system produced a concentrate with a WO3 grade of 66.43 % and a WO3 recovery of 67.40 %, compared with 65.98 % and 64.89 %, respectively, in the Na2CO3 system. Under the investigated conditions, NaOH caused more pronounced surface modification and a stronger NaOL-induced hydrophobic response than Na2CO3.

Yanhong Wang, Jianyu Chen

Talc, a naturally hydrophobic magnesium silicate mineral, often enters flotation concentrates as a gangue mineral, adversely affecting downstream processes. However, the flotation behavior of talc in salt water environments remains less understood. This study investigated the flotation behavior of talc in deionized water, sodium and calcium salt water across varying pH conditions. The flotation results revealed that the recovery of talc in deionized water is pH-independent, which varied in salt water, with a significant decrease in 0.001 mol/L Na+ or Ca2+ salt water at pH 12. The change of talc recovery in different salt water was strongly correlated with its surface wettability. The dissolved Ca2+ and Mg2+ in the inner structure of talc hydrolyzed to form hydrophilic complexes, Ca(OH)+ and Mg(OH)+, and precipitates, Ca(OH)2 and Mg(OH)2, which adsorbed on talc surface and reduced the contact angle. Density functional theory (DFT) calculations demonstrated that cationic complexes, Ca(OH)+ and Mg(OH)+, exhibited stronger adsorption energies with talc (110) surface oxygen atoms, compared to their hydroxide precipitates, Ca(OH)2 and Mg(OH)2, which enhanced talc hydrophilicity at pH 12. These findings provide critical insights into the role of salt water chemistry in talc flotation and offer a theoretical reference for depressing talc in saline flotation systems.

Ahmet Deniz Bas, Eren Yurtseven

The selective separation of feldspar from associated gangue remains a significant challenge in flotation chemistry due to overlapping surface properties especially between feldspars and iron/titanium-bearing silicates. This study investigated the individual, binary, and ternary modifier effects of tannic acid (TA), guar gum (GG), and sodium silicate (SS) on feldspar flotation using froth grade distributions of Al2O3, Fe2O3, TiO2, Na2O and K2O under the light of Fourier-transform infrared (FTIR) spectroscopic responses of floated and non-floated products. Among the individual reagents, GG alone exhibited the highest flotation selectivity, producing concentrates enriched in heavy metal oxides, Al2O3, and K2O with the lowest Na2O grade, whereas its selectivity progressively declined with decreasing GG content in binary mixtures. Froth mass increased with increasing TA dosage; however, its influence on froth modification was constrained by its relatively weak depression efficiency. SS addition increased froth mass while diminishing flotation selectivity by enhancing water entrainment. In binary reagent systems, SS mitigated the modifying effects of both TA and GG, likely due to its non-selective adsorption onto the surfaces of feldspar and floated mineral particles. Binary and ternary reagent systems exhibited both synergistic and antagonistic interactions, governed by modifier type and dosage ratios. FTIR analysis revealed systematic surface modifications arising from the combined adsorption of the tested modifiers, with polysaccharide-dominated ternary systems producing a distinct interfacial structure.

Wei Wang, Yuqi Gao

Vibrating screens are widely used for particle-size classification, yet the nonlinear coupling between vibration amplitude and frequency, together with the dynamic evolution of particle-size composition within the bed, limits the applicability of conventional vibration parameters and segregation indices. In this study, the segregation and undersized-particle penetration dynamics of a binary mixed-particle bed under different vibration conditions were investigated using the discrete element method. An empirical composite vibration parameter (ECVP) A1d × f1.87875d was developed based on the multipeak-multitrough evolution of screening-rate curves to unify the characterization of screening-state transitions under different frequency – amplitude combinations. The screening process was further divided into three stages: early burst penetration, intermediate concentrated penetration, and late dispersed penetration. A dynamically stable particle-size structure was identified in the near-screen region during the concentrated-penetration stage, suggesting an approximate dynamic balance between fine-particle penetration in the lower bed and fine-particle settling or segregation in the overlying region. Accordingly, a novel dynamic segregation index (DSI) Δˆh incorporating mean particle height and volume fraction was proposed. The results demonstrate that the combined use of the ECVP, DSI, and fine-particle height statistics enables the screening process to be classified into low-rate, transitional, and high-rate regimes. These findings provide theoretical support for optimizing vibrating-screen operating parameters and elucidating the synergistic mechanisms of particle segregation and undersized-particle penetration.

Zhihong Jiang, Zijian Zhong

The crusher product particle size distribution is a critical technical indicator in mineral processing, governing downstream grinding efficiency, separation recovery, and circuit economics. Existing breakage matrix models struggle to adapt to variations in ore properties and operating conditions, whereas pure data-driven methods lack physical consistency and depend on large datasets. Physics-informed neural networks (PINNs) embed mechanisms through differential operators and are effective for continuous differential equations; for discrete breakage matrix models, however, they can only impose soft algebraic constraints, which regularize fitting but may not ensure robust extrapolation. To address this limitation, we propose a dual-path physics-embedded neural network (DP-PINN) that hard-embeds the classification function C, breakage function B, and breakage matrix model as differentiable computational layers in an independent physics reference path. This path operates in parallel with a data-driven main path, jointly optimizing mechanism constraints and data fitting, while a size-class-adaptive fusion gate balances the two predictions by operating condition. Validation on industrial data from a tungsten ore processing plant shows that DP-PINN achieves a coefficient of determination (R2) of 0.978 ± 0.015 and a mean absolute error (MAE) of 0.83% ± 0.17% on the test set (mean ± standard deviation over ten repeated random splits), outperforming data-driven, physics-based, and standard-PINN baselines on average across three evaluation metrics under the repeated random-split protocol. The embedded physical parameters remain trainable, enabling adaptation to varying ore properties and equipment conditions without offline recalibration and providing a practical route to high-precision particle-size prediction under limited data in comminution circuits.

Viktória Feigl, Anna Budai

Bauxite residue (red mud) represents a promising secondary resource for critical raw materials (CRMs). Bio-hydrometallurgy offers an eco-friendly route for CRM recovery. In this study, an Aspergillus sp. was used to bioleach CRMs from bauxite residue (BR) at a relatively low temperature (22 °C), a short contact time (5 days), and high pulp densities (up to 20 w/w%). During one-step bioleaching, fungal activity lowered the pH to 3.1 – 5.3, depending on the BR concentration. The highest recovery efficiencies were achieved at 1% BR, reaching 77% Al, 47% Ti, 50% V, 42% Ga, 61% Y, 65% Zr, 20% La, 18% Ce, and 35% Nd. Increasing the BR concentration led to higher CRM concentrations in the leachate but reduced overall recovery efficiency. Vanadium extraction was maximised at 20 w/w% BR, yielding 48 mg/L in the leachate, corresponding to an 18% leaching efficiency. Ecotoxicological assessment revealed increased toxicity of the leaching residue obtained at 20% BR compared with the original material. Washing further increased residue toxicity and generated a toxic washing effluent, indicating that alternative post-treatment strategies are required. Overall, the results demonstrate that low-temperature, short-term fungal bioleaching at lower pulp densities can provide a promising approach for CRM recovery from bauxite residue.

Shengpeng Li, Haiyun Xie

Tetrahedrite [(Cu,Fe)12(As,Sb)4S13], a Cu-rich polymetallic sulfide, contains high levels of As and Sb (contains As 5.12%, Sb 11.46%) that resist physical beneficiation, impairing concentrate quality and burdening smelting with environmental and operational challenges. To address this cleaner production challenge, we propose an innovative ultrasonic-enhanced alkaline leaching process for the selective removal of As and Sb from tetrahedrite. The effects of ultrasonic power, leaching agent concentration, liquid-solid ratio, and reaction time on removal efficiencies were systematically investigated. The underlying mechanism was elucidated through X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and far-infrared (FIR) spectroscopy. XRD analysis revealed the near-disappearance of tetrahedrite characteristic peaks and the emergence of new copper-iron sulfide phases (CuS, (Fe,Cu)S2, Cu2S, and CuS2), while FIR spectra confirmed the cleavage of (Sb/As)-S bonds. Microscopic observations showed that the originally intact and crack-free crystal surfaces evolved into highly porous, honeycomb-like structures after leaching, facilitating efficient impurity removal. Under optimized conditions (NaOH-Na2S = 3.84 mol/L + 1.92 mol/L, ultrasonic power 500 W, liquid-solid ratio 25:1, time 6 h), the leaching rates of As and Sb reached 97.35% and 97.14%, respectively, with residual contents as low as 0.152% As and 0.368% Sb—well below the smelting standard for copper concentrates (As < 0.5%, Sb < 0.5%). This work demonstrates an efficient and selective strategy for the cleaner detoxification of high-As and high-Sb tetrahedrite.

Xin Qin, Jie Dong

Industrial process monitoring and fault diagnosis are essential for ensuring high-quality production and safe operation in manufacturing systems. However, insufficient labelled data, imbalanced fault samples, and frequent changes in operating conditions limit the diagnostic performance and generalization capability of existing deep learning-based methods. Moreover, conventional models often struggle to adapt rapidly to newly encountered operating conditions when only limited labelled samples are available. To address these challenges, this paper proposes a data-augmented meta-learning (DAML) framework for fault diagnosis under varying operating conditions. The framework aims to extract transferable meta-knowledge from different diagnostic tasks, thereby enabling rapid model adaptation to new operating conditions with limited labelled data. First, to alleviate fault class imbalance in practical industrial processes, a generative adversarial network is employed to generate minority-class fault samples and improve the balance among different fault classes. Multiple fault classification tasks are then constructed through random sampling within the meta-learning framework. Second, to address few-shot diagnosis under new operating conditions, a meta-learning model integrating a long short-term memory network and a fully convolutional network (Meta-LSTM-FCN) is developed. Meta-training is performed over a task domain comprising multiple related fault diagnosis tasks.

Pradipta Sankar Maiti, Tapan Kumar Dhar

Nanoscience and nanotechnology have moved from the laboratory benchtop into industrial use, re-engineering surface coating technology. Since nanomaterials behave differently from their bulk counterparts, they can confer protection and entirely new functions at much lower loadings. This review surveys, from an industrial-application perspective, several of the domains in which that promise is now maturing: (i) nanoscale fillers that reinforce mechanical and tribological performance; (ii) polyhedral oligomeric silsesquioxane (POSS) as a molecularly defined organic–inorganic hybrid building block for hard-yet-flexible, anti-wetting, icephobic, anti-corrosive, fire-retardant, and self-healing coatings; (iii) nanomaterials that suppress biofilm formation and marine and medical biofouling; and (iv) structural-color systems that generate durable, pigment-free color. For each domain we set out the structure–property relationship and compare the leading additive families on the criteria that govern adoption: dispersion stability, durability under field conditions, environmental and health status, and cost. Synthesis methods are deliberately kept out of scope. We argue that the field’s central limitation is no longer demonstrating a function in the laboratory but reproducing it consistently, safely, and affordably at industrial scale. We benchmark these judgments against the systems that have advanced furthest toward use, and distinguish carefully between them: ultrananocrystalline-diamond wear coatings are in established commercial service, whereas the evidence for self-polishing antifouling paints and self-assembling structural-color finishes rests largely on manufacturer-reported performance, industrial partnerships and early automotive pilots that still require independent verification.

Micheline Draye, Gregory Chatel

This last century, the development of new medicinal molecules represents a real breakthrough in terms of humans and animal life expectancy and quality of life. However, this success is tainted by negative environmental consequences. Indeed, the synthesis of drug candidates requires the use of many chemicals, solvents, and processes that are very hazardous, toxic, energy consuming, expensive, and generates a large amount of waste. Many large pharmaceutical companies have thus moved to using green chemistry practices for drug discovery, development, and manufacturing. One of them is the use of energy-efficient activation techniques, such as ultrasound. This review summarizes the latest most representative works published on the use of ultrasound for sustainable bioactive molecules synthesis.

Gregory Chatela, Catherine Goux-Henry

In this proof of concept study, the advantageous properties of both H2O2/NaHCO3/imidazole/Mn(TPP)OAc oxidation system and MOPyrroNTf2 ionic liquid have been combined under ultrasonic irradiation to give an exceptionally favorable environment for Mn(TPP)OAc catalyzed olefin oxidations. The results reveal the crucial role played by the ultrasonic irradiations that influence drastically the oxidation process. In MOPyrroNTf2 and under ultrasonic irradiation, the mechanism probably involves an oxo-manganyl intermediate at the expense of the classical bicarbonate-activated peroxide route.

Marion Zwingelstein, Micheline Draye

Viticultural by-products are known to contain high-value compounds such as stilbenes. Wine sector in Savoie Mont Blanc region (France) generates high amounts of waste including grape canes and grapevine stocks that remain unvalued. Herein, the potential of the most widespread grapevine varieties in Savoie Mont Blanc territory, Jacquère and Mondeuse, as sources of two stilbenes, trans-resveratrol (Rsv) and trans-ϵ-viniferin (Vf), was investigated for the first time. Using the reported extraction technique, the present work focused on several pre-extraction parameters influencing the extraction efficiency of these stilbenes from grape canes. The results indicated that various pre-extraction conditions significantly enhance the recovery of Rsv and Vf, ultimately contributing to a more sustainable utilization of viticultural by-products. This study provides foundational data that may lead to improved practices for extracting valuable compounds from viticultural waste in the Savoie Mont Blanc region.

N. Papa, F. Carpentier

This study investigates the synthesis and characterization of novel hydrophobic ionic liquids featuring dimethylpyridinium and trimethylpyridinium cations. The primary objective is to explore their physico-chemical properties to evaluate potential applications. The methodology employed involves mixing specific quantities of pyridine derivatives with bromobutane in suitable solvents, followed by rigorous purification processes including filtration and vacuum drying. Characterization techniques such as NMR and IR spectroscopy are utilized to analyze the resulting compounds. The results indicate a successful synthesis of the target ionic liquids, with yields reaching as high as 88%. The ionic liquids exhibited distinctive electrochemical properties, with significant variations noted in their electrochemical stability and solubility depending on the anionic components used. The findings highlight the promising potential of these ionic liquids in various applications, particularly in the fields of electrochemistry and material science, warranting further exploration of their functionalities and compatibility with various solvents.

Pascal Lignier, Julien Estager

Carboxylic acids are promising candidates for new sustainable strategies in organic synthesis. In this paper, we ascertain the potential of ultrasound for the hydrolysis of nitriles into carboxylic acids through the study of key parameters of the reaction: pH, hydrolysis medium, reaction time and activation technique. The positive influence of ultrasound under basic conditions is due to more than mechanical effects of cavitation. Indeed, the rate of hydrolysis is dramatically increased under sonication in NaOH solutions. A radical mechanism involving the oxide anion radical O• is proposed.

Alexandre Vandeponseele, Micheline Draye

This article aims to study the interest of spent coffee grounds (SCG) valorization through caffeine recovery. In an original way, this study takes into account all the parameters such as (i) the brewing coffee methods (household, coffee shops, etc.); (ii) the storage conditions, in particular the drying step; (iii) the solid/liquid extraction parameters such as the nature of solvent, the temperature, the extraction time and the solid/liquid ratio; and (iv) the liquid/liquid purification parameters such as the nature, the volume and the pH of extraction medium. Results have shown that spent coffee grounds from coffee-shops obtained by percolation contain a higher amount of caffeine than spent coffee grounds from households obtained from spent pods or filters. A drying treatment is not required when extraction is performed under one week after the spent coffee grounds collection with 96.4% of not degraded caffeine. Solid/liquid extraction performed with 25 mL.g −1 SCG of hydroalcoholic solvent (water/EtOH, v/v 60/40) at 60 ◦C during 15 min have given a caffeine recovery of significant relevance.

Alain Favre-Re´guillon, Gerard Lebuzit

Cross-flow membrane filtration equipment was operated to evaluate the selective uranium carbonate rejection from seawater of different flat sheet membranes. The membranes were discriminating by the rejection of uranium, calcium, and sodium under geochemical conditions that mimic uranium in seawater. Then, operating parameters were optimized, and the selective rejection of dissolved uranium was checked on prefiltrated seawater. Concentration factors of 1.1 and 8.5 for sodium and uranium(VI), respectively, were obtained for a nanofiltration volume ratio of 0.96.

Tony Cousin, Gregory Chatel

This minireview discusses recent advancements in the development of sustainable catalytic systems specifically designed for the oxidative cleavage of cycloalkenes utilizing hydrogen peroxide as an oxidant. The objective is to highlight the importance of such catalytic systems in producing dicarboxylic acids, which serve as essential intermediates in various industrial applications, including pharmaceuticals, cosmetics, and other chemical products. The methodology includes a comprehensive analysis of recent studies and innovative approaches in catalysis, bringing together various chemical processes and techniques employed. Results indicate that the strategic use of sustainable catalysts not only enhances the efficiency of the oxidative cleavage reactions but also contributes significantly to environmental sustainability by minimizing waste and energy consumption. The findings emphasize the need for continued research in this area to improve catalyst design and optimize reaction conditions for better yields and selectivity. This work underscores the critical role of sustainable practices in modern chemical synthesis, paving the way for future developments in green chemistry.

Raphael Turgis, Guilhem Arrachart

A novel family of ketone functionalized task specific ionic liquids (TSILs) was designed for the selective extraction of tantalum from sulfuric acid medium. We report the synthesis of functionalized hydrophobic ionic liquids based on piperidium, imidazolium or pyridinium cations bearing ketone moieties and their application in the liquid/liquid extraction of Ta(V). Recovery processes for Ta after extraction were performed and the recyclability of the TSIL was demonstrated. An eco-friendly process based on a TSIL was proposed for the extraction and stripping of tantalum from sulfuric acidic medium. Such system can be used as an alternative to the methyl-iso-butyl ketone (MIBK) which is the most commonly used extractant for the separation and purification of niobium and tantalum.

Cyril Micheau, Guilhem Arrachart

The unique properties of tantalum (Ta) have resulted in its important usage to supply the capacitors and semiconductor production for electronics devices. Technological needs make Ta economically and strategically important to industrialized countries. Among different possibilities, the recovery of Ta from industrial waste and end-of-life products represents an economically attractive and viable approach, which aims at decreasing the environmental footprint in general. Currently, there is a lack of an efficient Ta recycling strategy from e-waste. In the present work, we studied the possibility to use pure ionic liquids such as N-butyl-N-ethyl-piperidinium bis(trifluoromethylsulfonyl)imide (EBPiP-NTf2) and N-octyl for a two-step eco-friendly process designed specifically for the selective extraction of tantalum. By assessing the efficiency of these ionic liquids in tantalum recovery, we aimed to establish a more sustainable method for recycling this critical material from electronic waste, thus addressing both environmental concerns and resource conservation in the electronics industry.

Alain Favre-Réguillon, Micheline Draye

Cloud point extraction (CPE) was utilized to extract and separate lanthanum(III) and gadolinium(III) nitrate from an aqueous solution. The methodology employed involved the formation of lanthanide(III)-8-hydroxyquinoline (8-HQ) complexes which are soluble in a micellar phase of non-ionic surfactant. Subsequently, these lanthanide(III) complexes were extracted into the surfactant-rich phase when the temperature exceeded the cloud point temperature (CPT). It was identified that the structure of the non-ionic surfactant and the chelating agent–metal molar ratio are critical factors influencing both the extraction efficiency and selectivity. In scenarios with equimolar concentrations of La(III) and Gd(III), the extraction efficiency for Gd(III) was observed to reach 96%, coupled with a Gd(III)/La(III) selectivity exceeding 30 when Triton X-114 was used. Under these optimal conditions, a Gd(III) decontamination factor of 50 was obtained.

Rachel Pflieger, Manuel Lejeune

Sonoluminescence (SL) spectra of a very dry [BEPip][NTf2] ionic liquid were measured in the first minutes of sonication under Ar. The intense sonoluminescence allowed us to monitor the time-evolution of the SL spectra. Several molecular emissions were observed. Rovibronic temperatures of C2 and CN were determined giving vibrational temperatures of 5800 ± 500 K and 6000 ± 500 K and rotational temperatures (i.e. translational or gas temperatures) of 4000 ± 500 K. These temperatures stay remarkably constant during the sonolysis, while SL spectra undergo strong changes that illustrate the very fast evolution of the plasma during the first minutes of sonication.

Erika van Tonder

This thesis investigates the effect of ore blends on milling and flotation performance. Anglo Platinum’s Waterval UG2 concentrator in Rustenburg processes ore from various shafts. The thesis was initiated to gain a better understanding of the type of interactions between the ores that consequently affect metal accounting at the concentrator. Four ores were sourced from the following shafts: Salene ore (x1), Waterval UG2 ore (x2), Paardekraal ore (x3) and Townlands ore (x4). The literature review indicates various experimental mixture designs that could possibly be used. The mixture designs that are most evident in the literature are the simplex centroid design, the simplex lattice design and the axial (augmented) design. The simplex lattice design was selected for this research as it is commonly used in, for example, the pharmaceutical industry, chemical industry and the food industry. None of these mixture designs has ever previously been applied to the field of mineral processing and the thesis aimed to establish the experimental mixture design as a foundation for a methodology to evaluate and interpret the effect of ore blends. The thesis involved the characterization of four pure ores. Laboratory milling and flotation tests were conducted in order to standardize a milling time and quantify the uncertainty around the test work.

Kaiyu Zhou, Somasekhara Goud Sontti

The massive volume of oil sands tailings has been one of the most challenging environmental issues. In this work, we experimentally explore a simple and effective approach to bitumen residue separation from a highly concentrated slurry flow of the artificial oil sands tailings. By utilizing microbubbles from in-situ catalytic decomposition of hydrogen peroxide (H2O2) at low concentrations, bitumen aggregation is enhanced on the top part of the hydrotransport pipeline. The microscopic image analysis revealed the in-situ formation of microbubbles and confirmed that magnetic particles present in the slurries contributed to the fast release of the gas products and bubble formation from H2O2 decomposition. A high-speed camera was applied to capture images of the tailings flow in the pipeline through a transparent view window. A large number of tiny bubbles were identified post to the injection of H2O2 to the slurry flow. More than 70% bitumen could be recovered from a lab-scale pipeline loop within 30 mins after H2O2 injection. The bitumen recovery efficiency from the collected froth was quantitatively compared under seven conditions with varied dosages, the concentration of H2O2, and the amount of magnetic solids in the slurries. Our results confirmed that the total dosage of H2O2 is the dominant factor in in-situ microbubble formation for enhanced bitumen aggregation in the flow. Importantly, microbubbles were generated rapidly in the real mature fine tailings. The results from our study provide insights into the preferential distribution of oil residue in the flow during hydrotransport without the requirement for an additional device. Removal of oily residues from concentrated slurries may bring economical and environmental advantages.

Paulina Quintanilla, Daniel Navia

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC’s optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%.

Franziska Strube, Thomas Wilhelm

The demand for lithium, as well as other critical resources, needed for electrochemical energy storage is expected to grow significantly in the future. Slags obtained from pyrometallurgical recycling represent a promising resource of valuable materials, among them lithium and rare earth elements found in artificial minerals particulate phases. This study investigates the flotation separation of engineered artificial minerals (EnAMs) in slags, such as lithium aluminate and gehlenite as valuable and gangue phases, respectively. Flotation experiments are carried out in a Partridge-Smith cell using oleic acid (OA) as a benchmark surfactant. Particle characterization is performed using SEM-based Mineral Liberation Analysis (MLA), which provides particle discrete information. From this information, bivariate Tromp functions based on non-parametric kernel density estimation are computed to characterize the separation behavior with respect to particle descriptors. This approach enables investigating the influence of particle size and shape on separation behavior of EnAMs. Furthermore, these results allow for the optimization of flotation experiments for enriching Li-bearing EnAMs.

J. M. Kazumba

Dans les procédés hydrométallurgiques, la maîtrise des performances des échangeurs de chaleur à plaques est essentielle à la stabilité thermique et à l’efficacité énergétique des installations. Dans cette étude, nous avons proposé une approche hybride pour modéliser les performances de l’échangeur industriel a plaques 60HX002 de MMG Kinsevere, à Lubumbashi (RD Congo), soumis à des conditions d’exploitation variables et à des comportements non linéaires et rechercher des conditions opératoires associées à un performance thermique cible. La méthodologie combine un modèle flou de Takagi –Sugeno généré par clustering soustractif, un système d’inférence neuro-flou adaptatif (ANFIS) et une simulation Monte -Carlo. Sur 2367 observations industrielles, le modèle ANFIS a atteint R2=0,894, une RMSE de 429 W/m²K et une MAE de 160 W/m²K. La validation croisée à cinq plis a confirmé sa stabilité prédictive avec R 2=0,892±0,020. Une première simulation Monte-Carlo a mis en évidence les limites d’un échantillonnage indépendant des variables opératoires, tandis qu’une simulation inverse contrainte de 50000 scénarios a identifié 334 solutions (0,67 %) compatibles avec U=4000±500 W/m²K et Tes=40±3 °C. La solution la plus proche de la cible correspond à Bs=162 m³/h, Tes=40,1 °C et ΔP=0,399 bar, pour U=3999 W/m²K. Ces résultats montrent l’intérêt de l’approche que nous avons proposée pour la prédiction du coefficient global de transfert thermique U, l’identification des régimes de fonctionnement et la recherche de zones opératoire s cibles, sous réserve d’une validation expérimentale avant son utilisation pour.

Dixiu Wu, Junwei Han

Lead refining dross containing plenty of tin and other heavy metals, such as lead and antimony, is considered a hazardous waste generated in large quantities in lead smelter plants. In this study, calcium stannate was synthesized from lead refining dross using sodium carbonate roasting and alkaline leaching followed by precipitation with CaO. The effect of roasting and leaching parameters on the extraction efficiency of tin was investigated. The leaching efficiency of tin reached 94% under the optimized conditions: roasting with 60% Na2CO3 at 1000 ◦C for 45 min, and leaching using 2 mol/L NaOH solution for 90 min at 85 ◦C and 8 cm3/g liquid/solid ratio. Furthermore, more than 99% of tin in the leaching solution was precipitated using CaO. Finally, XRD, SEM, and ICP-OES analyses indicated that the final CaSnO3 product had a purity of 95.75% and its average grain size was smaller than 5 µm. The results indicated that the developed method is feasible to produce calcium stannate from lead refining dross.