Numerical Methods and Stochastic Simulation in Chemical Engineering

Chemical engineering problems often involve complex, multi-dimensional systems that cannot be solved analytically. To design efficient processes and predict system behavior, you must understand how to model physical phenomena using numerical computation and stochastic simulation. This text-based course guides you from the fundamental principles of numerical methods to the practical application of probability theory in engineering contexts. You will start by building a strong foundation in basic mathematical terminology and key simulation concepts before moving on to hands-on computational workflows. Through clear, step-by-step written explanations, you will learn to translate physical chemical engineering problems into structured, solvable algorithms. What you'll learn: Understand foundational numerical methods and how to apply them to chemical engineering calculations; Practice probability theory concepts to model uncertainty in chemical systems; Construct and analyze stochastic simulations to predict molecular and process-level behaviors; Formulate mathematical models for one-dimensional transport and reaction systems; Apply modern programming practices, such as code readability and modular debugging, to chemical engineering scripts; Analyze simulation outputs to make data-driven engineering decisions. The course begins with core definitions and basic mathematical principles, gradually progressing to complex simulation models and algorithmic workflows. This course is designed for beginner-level engineering students and professionals seeking a clear, structured introduction to computational modeling, with no advanced simulation experience required. Start reading today to unlock the power of numerical modeling in chemical engineering.