Overview

The chemical industry stands at a pivotal moment where decades of engineering expertise are converging with the accelerating capabilities of artificial intelligence. AI in the Chemical Industry: Predictive Modeling, Process Optimization, and Safety Analytics is written to help professionals navigate this transformation with clarity and confidence. Chemical plants generate vast amounts of data, sensor streams, laboratory results, operating logs, maintenance histories, and environmental metrics. When paired with modern AI and analytics, these data sources enable unprecedented improvements in yield, quality, efficiency, sustainability, and safety. This book provides a practical, structured introduction to how AI can support engineers, operators, and decision-makers across the full lifecycle of chemical processes, from digital twins to anomaly detection and optimization. Grounded in both industrial realities and emerging technological trends, the chapters guide readers through the fundamentals of chemical processes, data infrastructure, hybrid physical-AI modeling, predictive maintenance, emissions monitoring, and human-AI collaboration in control rooms. Rather than focusing on algorithms alone, the book emphasizes the importance of domain knowledge, data quality, governance, and responsible adoption elements that ultimately determine whether AI succeeds at scale in the chemical sector. Whether you are an engineer seeking to leverage data more effectively, a data scientist entering the world of chemical manufacturing, or a leader shaping a digital transformation roadmap, this book offers a comprehensive foundation for using AI to build safer, cleaner, and more efficient chemical operations.

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