Stochastic Numerical Methods for Chemical Engineering

Chemical engineering systems are often governed by molecular randomness, complex reaction kinetics, and unpredictable physical variables that traditional deterministic equations cannot fully capture. To design safer reactors, optimize separation processes, and predict material properties, you need a solid grasp of probabilistic modeling. This text-based course guides you through the essential mathematical frameworks and computational algorithms used to simulate stochastic behavior in chemical systems. You will start with foundational probability theory and key statistical definitions before moving on to practical simulation techniques. What you'll learn: Understand core probability distributions and statistical concepts essential for chemical modeling; Master the Monte Carlo method to solve high-dimensional integration and simulation problems; Implement the Gillespie algorithm to model stochastic chemical kinetics and cellular reactions; Simulate random walk processes to analyze diffusion and mass transport phenomena; Apply modern error analysis and variance reduction techniques to ensure simulation accuracy. The course begins with the absolute basics of random variables, probability density functions, and statistical sampling before progressing to algorithm implementation and practical chemical engineering case studies. This course is designed for undergraduate students, researchers, and practicing chemical engineers who want to expand their numerical toolkit, with no advanced prior knowledge of stochastic methods required. Take control of system uncertainty and start modeling real-world chemical processes with confidence.