Machine Learning Applications in Industrial Solid Ash 1st Edition

Machine Learning Applications in Industrial Solid Ash — 1st Edition by Chongchong Qi; Qiusong Chen; Erol Yilmaz

Discover a practical, forward-looking guide that bridges advanced data science and industrial waste management. This 1st Edition offers engineers, plant managers, environmental scientists, and data practitioners a clear pathway to applying machine learning to the challenges of industrial solid ash—from power plants and incinerators to cement and mining operations.

Begin with accessible explanations of key algorithms, then move into tailored workflows for ash characterization, quality prediction, process optimization, and emissions control. Emphasis on model selection, feature engineering, and deployment ensures the book is immediately useful for real-world projects. Readers will find robust examples of predictive maintenance, anomaly detection, and process control that reduce costs, improve safety, and support regulatory compliance.

Whether you are operating in North America, Europe, or rapidly industrializing regions in Asia, the methodologies presented are globally applicable and adaptable to local feedstocks and regulations. The authors blend academic rigor with industry-tested solutions, making this essential reading for researchers, consultants, and in-house analytics teams seeking measurable performance gains.

Practical, actionable, and future-ready, this volume empowers professionals to transform ash management into a strategic advantage through data-driven decision making. Add Machine Learning Applications in Industrial Solid Ash to your library and lead smarter, cleaner, and more efficient operations today. Order now to start turning data into impact.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.