Prediction of Gas Phase Adsorption Isoth

The study of gas-phase adsorption is critical for optimizing various industrial processes, especially when considering the effects of temperature on adsorbent efficiency. This paper aims to investigate the relationship between temperature and adsorption capacity, providing a comparative analysis of multiple existing adsorption isotherm models alongside a novel approach utilizing artificial neural networks (ANNs). A thorough overview of conventional models is presented to highlight their predictive capabilities in relation to temperature variations, particularly focusing on the limitations of traditional methods. The proposed methodology leverages ANNs to enhance the prediction accuracy of gas-phase adsorption isotherms across different temperatures, thereby addressing the challenges posed by heat accumulation and its effects on adsorbent materials like granular activated carbon. Experimental validation of the ANN model demonstrates its effectiveness and potential advantages over conventional isotherm models, revealing stronger correlations with empirical data. Results indicate that the ANN-based approach not only captures the intricacies of adsorption behavior under varying thermal conditions but also offers a robust framework for future research in adsorption phenomena. This integrated methodology provides a significant contribution to the field of chemical engineering, paving the way for advanced modeling techniques in adsorption studies.