Probability Theory for Chemical Engineering

Chemical engineering relies on predicting outcomes under uncertainty, from molecular collisions to large-scale reactor yields. This text-only course introduces the essential probability concepts and numerical methods required to model and analyze complex chemical processes. You will learn how to transition from basic statistical definitions to practical engineering applications. By completing this course, you will be able to analyze experimental data with confidence, model stochastic processes, and apply numerical techniques to solve real-world chemical engineering equations. What you'll learn: - Understand foundational probability theory and key statistical definitions used in engineering - Model chemical reaction kinetics and molecular behavior using probability distributions - Apply numerical methods to solve stochastic differential equations in process design - Analyze experimental data sets to estimate parameters and quantify measurement uncertainty - Implement modern data validation techniques to ensure robust engineering simulations The course begins with foundational definitions of probability, random variables, and distributions, before guiding you through numerical techniques and practical chemical engineering applications. You will learn through clear, written explanations and structured mathematical examples designed for self-paced study. This course is designed for beginner-level engineering students, researchers, and practicing chemical engineers who want to build a solid mathematical foundation in probability and numerical modeling. No advanced statistical background is required to begin.