Presents a systematic review of optimizing sustainable process systems through multiscale modeling and uncertainty analysis
The global pursuit of net-zero carbon emissions has created an urgent need for chemical engineers and energy researchers to design systems that are both sustainable and resilient. While renewable energy sources such as solar and wind offer great potential, their variability introduces significant challenges that must be addressed through advanced optimization techniques. Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties connects optimization fundamentals with their applications in sustainable energy systems with a particular emphasis on the challenges posed by uncertainty.
Divided into two parts, the book first introduces the core mathematical frameworks and methods needed to model and optimize uncertain systems, including stochastic programming, robust optimization, reinforcement learning, and multiscale algorithms. The authors clearly explain these state-of-the-art tools with attention to both theory and computational practice. The second part shifts to applications, demonstrating how these techniques are applied in real-world contexts such as renewable-based hydrogen, methanol, and ammonia production; carbon capture; shale gas systems; biomass integration; and power system optimization. Throughout the text, the authors emphasize the integration of renewables with chemical industries while highlighting strategies to manage variability, strengthen supply chains, and improve system-wide efficiency.
Combining rigorous fundamentals with cutting-edge applications through a tutorial-style approach, Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties:
- Provides the foundation and tools needed to design resilient, optimized, and sustainable energy systems.
- Addresses optimization methods under uncertainty tailored to energy and process systems
- Presents a unified treatment of stochastic programming, robust optimization, and reinforcement learning techniques
- Integrates renewable-based systems with chemical industry supply chain design and operation
- Addresses computational challenges in large-scale optimization of energy systems
Both a theoretical resource and a practical guide for applied problem-solving, Optimization of Sustainable Process Systems: Multiscale Models and Uncertainties is ideal for graduate-level courses in chemical engineering, process systems engineering, energy systems optimization, and operations research. It is also a valuable reference for industrial researchers, system modelers, and developers working on sustainable process design and energy transition strategies.
Can Li, PhD, is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University. His research group focuses on optimization, machine learning, and applications to sustainable process systems. His research has been recognized by the NSF CAREER Award, ACS PRF Doctoral New Investigator Award, and the Amazon Research Award on Sustainability.
