Boundary Value Problems in Chemical Engineering using Python

Chemical engineering processes often depend on variables that change over space, from temperature profiles in a reactor to concentration gradients in a catalyst pellet. Solving these physical systems requires a solid grasp of Boundary Value Problems (BVPs). This text-based course introduces you to the core mathematical principles and modern numerical techniques needed to model and solve these engineering scenarios successfully. You will transition from theoretical transport equations to practical, working code. By exploring finite difference methods, shooting techniques, and modern scientific Python libraries, you will gain the confidence to simulate complex chemical systems. What you'll learn: - Understand the foundational physics and boundary conditions behind chemical transport phenomena - Set up and discretize boundary value problems using the Finite Difference Method - Solve multi-dimensional steady-state heat conduction and mass diffusion equations - Apply modern Python packaging and virtual environments to manage your scientific computing projects - Use scientific Python libraries to solve systems of linear and non-linear algebraic equations - Practice analyzing error, stability, and convergence criteria for numerical approximations The course begins with foundational definitions of boundary conditions and mathematical classifications. Next, you will learn to discretize equations, build system matrices, and write clean, structured Python code to solve realistic chemical engineering models step-by-step. This course is designed for undergraduate chemical engineering students, researchers, and practicing engineers who want a beginner-friendly, practical path to solving differential equations numerically. No prior experience with advanced numerical analysis is required, though a basic understanding of Python and calculus is helpful. Start translating chemical engineering theory into precise numerical solutions today.