Mohammad Hadi Dehghani & R Gobinath 
Soft Computing Techniques in Solid Waste and Wastewater Management [EPUB ebook] 

Soporte
Soft Computing Techniques in Solid Waste and Wastewater Management is a thorough guide to computational solutions for researchers working in solid waste and wastewater management operations. This book covers in-depth analysis of process variables, their effects on overall efficiencies, and optimal conditions and procedures to improve performance using soft computing techniques. These topics coupled with the systematic analyses described will help readers understand various techniques that can be effectively used to achieve the highest performance. In-depth case studies along with discussions on applications of various soft-computing techniques help readers control waste processes and come up with short-term, mid-term and long-term strategies. Waste management is an increasingly important field due to rapidly increasing levels of waste production around the world. Numerous potential solutions for reducing waste production are underway, including applications of machine learning and computational studies on waste management processes. This book details the diverse approaches and techniques in these fields, providing a single source of information researchers and industry practitioners. It is ideal for academics, researchers and engineers in waste management, environmental science, environmental engineering and computing, with relation to environmental science and waste management. Provides a comprehensive reference on the implementation of soft computing techniques in waste management, drawing together current research and future implications Includes detailed algorithms used, enabling authors to understand and appreciate potential applications Presents relevant case studies in solid and wastewater management that show real-world applications of discussed technologies
€180.22
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Idioma Inglés ● Formato EPUB ● Páginas 522 ● ISBN 9780323859301 ● Editor Mohammad Hadi Dehghani & R Gobinath ● Editorial Elsevier Science ● Publicado 2021 ● Descargable 3 veces ● Divisa EUR ● ID 7974375 ● Protección de copia Adobe DRM
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