CJCE’s latest virtual issue (VI) is now live: Artificial Intelligence and Machine Learning Applications in Chemical Engineering Virtual Issue. This virtual issue features articles published in the recent Artificial Intelligence and Machine Learning Applications in Chemical Engineering Special Issue Section from the March 2025 issue.
Within the preface for this special issue section, guest editor Simant Upreti of Toronto Metropolitan University summarizes the 16 invited articles that make up this special issue section and notes the role of artificial intelligence and machine learning in the chemical engineering field: “Artificial intelligence (AI) and machine learning (ML), as they penetrate human endeavours from all walks of life, hold significant prospects in not only replacing complex tasks requiring supervision but also solving difficult problems and enhancing performance in a variety of scenarios. Chemical engineering, which is intimately involved with the transformation of materials and energy, is a fertile ground for AI and ML applications.”
Within this virtual issue, you will find articles exploring current applications of artificial intelligence and machine learning in chemical engineering. For example, in “Assuring optimality in surrogate-based optimization: A novel theorem and its practical implementation in pressure swing adsorption optimization”, authors Carine Menezes Rebello, Erbet Almeida Costa, Antonio Santos Sánchez, Fredy Vides, and Idelfonso B. R. Nogueira note that “The main contribution of this work is the derivation of a robustness test that guarantees the optimality of surrogate-based optimization. The derivation of this metric is based on the universal approximation theorem. The full framework proposed in this work is also composed by a sampling sizing methodology to randomly select samples within a feasible operating region (FOR) resulting from the optimization population, reducing the computational cost of the analysis and avoiding biases in the robustness calculation.” They continue that “The applicability and importance of this methodology are demonstrated through a case study of a complex chemical process—a pressure swing adsorption (PSA) unit—which presents a high computational cost to solve optimization problems. The results highlight the need and importance of evaluating the optimality of surrogate-based optimization schemes.”
In “A new intelligent prediction model using machine learning linked to grey wolf optimizer algorithm for O2/N2 adsorption”, authors Hossein Mashhadimoslem, Vahid Kermani, Kourosh Zanganeh, Ahmed Shafeen, and Ali Elkamel “address the deficiency and predict the adsorption performance in different adsorbents” by proposing “a new optimizer linked to the machine learning (ML) model considering the performance of the adsorption process.” They continue that “The main goal is to predict adsorption under different process conditions with different adsorbents and provide a unified framework, leading to the prediction of adsorption phenomena instead of traditional isotherm models. This research focuses on predicting the adsorbed amount of O2 and N2 on several carbon-based adsorbents using the ML approach linked to the grey wolf optimizer algorithm (GWO).” Ultimately, “The new ML-generated model can accurately predict the adsorption process behaviour of different carbon-based adsorbents under various process conditions. The results of this research have the potential to assist a wide range of gas separation industries.”
Articles within this virtual issue are open access or have been set as free-to-read for a limited time. Be sure to check out this issue to explore articles published in the recent Artificial Intelligence and Machine Learning Applications in Chemical Engineering Special Issue Section from the March 2025 issue.