Can Li – författare

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 systemsAddresses optimization methods under uncertainty tailored to energy and process systemsPresents a unified treatment of stochastic programming, robust optimization, and reinforcement learning techniquesIntegrates renewable-based systems with chemical industry supply chain design and operationAddresses computational challenges in large-scale optimization of energy systemsBoth 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.

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.

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.

Titanium dioxide nanomaterials have been showing very promising applications in many fields. Scientists from all over the world have gathered together to discuss the most recent advances in areas spanning from theoretical calculations to fundamental surface science and to materials fabrication, characterization and practical exploitation. More than 160 papers, presentations and posters were accepted in Symposium GG, 'Titanium Dioxide Nanomaterials', at the 2011 MRS Spring Meeting held April 25-29 in San Francisco, California. In this volume are some of the selected papers which cover the synthesis, properties and applications of titanium dioxide nanomaterials.

This unique handbook fills the gap in the market for an up-to-date work that links both homogeneous catalysis applied to organic reactions and catalytic reactions on surfaces of heterogeneous catalysts.

This unique handbook fills the gap in the market for an up-to-date work that links both homogeneous catalysis applied to organic reactions and catalytic reactions on surfaces of heterogeneous catalysts.

This book gives a contemporary overview of the technologies of single-frequency fiber lasers. The development of single-frequency fiber lasers is one of the most significant achievements in the field of laser photonics over the past two decades. Owing to the crucial demands of a laser sources with highly stable single-frequency operation, narrow linewidth, low noise, scalable to high output power, compact and robustness structure, fiber lasers have been intensively studied since its introduction to the single-frequency laser community and they still continuously proceed to trigger the emergence of new technologies and applications. This book systematically demonstrates the single-frequency fiber laser technologies from fundamental principles to state-of-the-art progress. Details of selected typical applications of single-frequency fiber lasers are also given and discussed. The reader will acquire a good knowledge of the current situation within this important field.