Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering.

Key Features
* Allows the reader to learn AI quickly using inexpensive personal computers
* Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions
* Includes a computer diskette for an illustrated case study
* Demonstrates an expert system for separation synthesis (EXSEP)
* Presents a detailed review of published literature on expert systems and neural networks in chemical engineering

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