Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book introduces historical information, highlights current state and trends in AI applications, and discusses challenges and opportunities within the field. Foundational principles of AI and machine learning are thoroughly covered, giving readers a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection.

Subsequent chapters cover a wide spectrum of AI applications in chemical engineering. From supervised and unsupervised learning for process modeling to the advanced realm of deep learning applications, this book explores neural networks, convolutional and recurrent architectures, and their real-world applications in process optimization and analysis.

• Navigates the dynamic intersection of AI and chemical engineering, covering ethical considerations, interdisciplinary applications, and AI's impact on safety, sustainability, and innovation
• Bridges the gap between policy and implementation of AI in chemical engineering, facilitating a harmonious integration of AI technologies and fostering responsible and effective use within the chemical engineering industry
• Offers a forward-looking approach to guide professionals, researchers, and students in navigating the dynamic and transformative future of AI in chemical engineering