Nonlinear Process Modeling in Chemical and Particle Systems delivers a comprehensive guide to the analysis and application of nonlinear models in chemical engineering. Addressing the growing demand for simulation-driven design, process optimization, and sustainable innovation, the book integrates nonlinear ordinary and partial differential equations with real-world chemical and particle system applications. Readers are introduced to fundamental principles of nonlinear transport, reaction kinetics, and multiphase systems, followed by advanced treatments of particle dynamics, electrochemical processes, and environmental modeling. Each chapter combines theoretical underpinnings with detailed methods, computational strategies, and practical case studies, ranging from catalytic reactor dynamics to biosensor design and CO₂ capture technologies. The inclusion of both semi-analytical and numerical approaches, alongside predictive analytics and machine learning, ensures that the book speaks equally to mathematical rigor and industrial relevance.

Written for graduate students, researchers, and practicing engineers, this resource provides the skills to model, analyze, and optimize nonlinear processes across a range of chemical engineering applications. Its balance of theory, methods, and applied insights makes it an indispensable reference for advancing research, teaching, and professional practice in the field.

• Provides a unified approach to solving nonlinear ODEs and PDEs in chemical engineering
• Focuses on real-world processes such as reaction-diffusion, catalytic systems, and transport phenomena
• Emphasizes the use of computational techniques, including Matlab and Maple, for simulation and model validation
• Incorporates predictive analytics, AI, and machine learning for process monitoring and optimization
• Supports sustainable process design aligned with global energy and climate goals
• Aims to serve researchers, students, and industry professionals involved in advanced chemical process modeling