Description

This book covers the optimization of process systems operating under uncertainty across multiple spatial and temporal scales. Divided into two comprehensive parts, the first part lays the theoretical groundwork by introducing key mathematical frameworks and recent advancements in optimization under uncertainty and multiscale modeling and algorithms. Topics covered include Bayesian optimization, multi-objective and bilevel optimization, decomposition algorithms, and multiscale modeling and solution techniques.

The second part builds upon these foundations by demonstrating how they can be applied to real-world process systems. Applications span a diverse range of sectors including biomass processing, carbon capture, chemical energy storage, food packaging, and power systems. The book also highlights how emerging concepts such as the circular economy and process intensification can be integrated into the design of sustainable processes and supply chains. The book examines the critical role of renewable energy sources, such as solar and wind, in shaping the future of the chemical industry. It presents methodologies for the tight integration of renewable-based energy into the design, optimization, and operation of chemical supply chains, with a particular focus on enhancing sustainability and resilience.