Introduction to artificial intelligence in chemical engineering

This book chapter examines how artificial intelligence (AI) has been used in chemical engineering, emphasizing its development, present uses, and prospects for the future. Examining machine learning (ML) and deep learning (DL) approaches for fault identification and process optimization is one of the goals, as is tackling data management issues. The main techniques used in this study are reinforcement learning for real-time process control, supervised learning for property prediction, and unsupervised learning for anomaly detection. Notably, the study uses methods like LIME for model interpretability and highlights the significance of explainable AI to promote trust in AI systems. According to the research, putting AI into practice can increase operational efficiency by up to 30%, lower expenses by about 20%, and increase safety by enabling proactive monitoring. The innovative aspect of this work is its all-encompassing framework, which incorporates AI techniques specifically designed to address the difficulties in chemical engineering. The creation of hybrid AI systems that integrate ML with process simulation tools is one example of future applications that will advance sustainable chemical manufacturing methods and allow for real-time decision-making. The chapter also emphasizes how important data management techniques, like feature engineering and data cleaning, are to the successful application of AI. Additionally, it tackles ethical issues, like AI bias and accountability, guaranteeing that AI solutions are not only efficient but also equitable and open. The study’s conclusions offer a fundamental understanding of how to use AI in the chemical industry with the ultimate goals of process optimization, innovation promotion, and navigating the intricacies of ethical and regulatory issues.