Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book begins with an in-depth introduction that provides historical context, highlights the current state and trends in AI applications, and discusses the challenges and opportunities within the field. This sets the stage for readers to understand the transformative potential of AI in chemical engineering. The foundational principles of AI and machine learning are thoroughly covered in the second section. Readers gain 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. This section lays the groundwork for leveraging high-quality data in subsequent AI applications.

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