CHF 209.00

Modelling and Optimisation of Fluidparticle Processes in
Environmental Engineerin

Inglese · Copertina rigida

Pubblicazione il 19.05.2026

Descrizione

Ulteriori informazioni










This comprehensive book explores fluid-particle interaction processes in environmental engineering, combining theoretical foundations with advanced numerical simulation techniques. Covering eight key areas from particle agglomeration to membrane fouling, the work integrates fundamental physics with practical computational tools to address critical challenges in environmental systems and reactor design.
It provides a detailed overview of hydrodynamic modelling, interparticle forces, and membrane separation dynamics, alongside practical numerical simulation approaches for real-world applications. It also delivers essential insights into solid-liquid interface processes, mathematical modelling for fouling prediction, and design optimisation strategies for stirred tank reactors, rotatory vortex pelleting systems, and hydrothermal carbonisation reactors. Readers will gain access to proven methodologies that bridge theoretical understanding with practical implementation, offering workflow simplification tools for complex environmental engineering challenges.
Ideal for both academic study and professional engineering practice, this essential resource targets graduate students and researchers in environmental and chemical engineering, along with practicing engineers in water treatment, membrane technology, and reactor design.
Please access additional digital resources at https://github.com/hollander196/SciML-Fluid-Particle


Info autore










Benjamin Oyegbile holds a PhD in Environmental Engineering and is currently a visiting researcher at the University of Bradford. He has over six years of university-level teaching experience and a focused research agenda in fluid-particle modelling and environmental fluid dynamics. His research work is focused on the integration of experimental analysis and high-fidelity numerical simulations with advanced machine learning techniques to investigate and optimise complex fluid-particle processes in environmental engineering.


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