Bayesian Parameter Estimation for Chemical Engineers

Chemical engineering processes rely heavily on mathematical models, but real-world experimental data is always accompanied by uncertainty. Understanding how to estimate unknown process parameters and quantify this uncertainty is essential for designing safe, efficient, and reliable chemical systems. This text-based course guides you from the foundational math of probability to implementing practical numerical methods for parameter estimation. You will learn how to approach chemical engineering problems using Bayesian statistics, allowing you to make data-driven decisions and refine your process models. What you'll learn: Understand the core principles of Bayesian statistics and parameter estimation in engineering contexts; Calculate 1D intervals and analyze probability density functions for process variables; Apply numerical approximation methods to solve complex chemical modeling equations; Evaluate model fit and quantify uncertainty in experimental data; Implement clean, structured scripting workflows for numerical solvers and optimization. You will start with key definitions of probability, prior distributions, and likelihood functions before moving on to practical 1D interval calculations and numerical solvers. The course concludes with modern workflows for validating parameters and analyzing model sensitivity. This course is designed for student engineers, researchers, and professional chemical engineers new to Bayesian methods, requiring no advanced statistical background. Start reading today to master the numerical tools needed to solve modern chemical engineering estimation problems.