Generative AI in Chemical Engineering
English | 23 Jan. 2026 | ASIN: B0GJFNFPWT | 278 pages | PDF | 3.77 MB
Chemical engineering has always thrived at the intersection of theory, experimentation, and computation. From hand calculations to process simulators and first-principles models, each technological shift has expanded what engineers can design, optimize, and understand. Today, generative artificial intelligence marks a new inflection point. Unlike traditional tools that analyze predefined scenarios, generative models can propose novel molecules, processes, control strategies, and data-driven hypotheses, often at a speed and scale previously unimaginable. This book explores how these capabilities reshape the chemical engineer's toolkit, not as a replacement for fundamental principles, but as a powerful complement that augments human insight with machine creativity. Generative AI in Chemical Engineering is written for students, researchers, and practitioners seeking to navigate this emerging landscape with both ambition and rigor. It bridges core chemical engineering concepts, thermodynamics, transport, kinetics, separations, and systems engineering with modern generative methods such as large language models, diffusion models, and reinforcement learning. Through practical examples, case studies, and critical discussions, the book aims to demystify the technology, highlight its limitations, and articulate best practices for responsible use. Ultimately, it invites readers to reimagine how chemical engineering problems are formulated and solved in an era where intelligent machines can generate possibilities as readily as engineers evaluate them.
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