Numerical Methods for Chemical Engineering

Chemical engineering challenges often involve complex, non-linear equations and coupled differential systems that cannot be solved with simple pencil-and-paper algebra. To design reactors, optimize separation processes, and analyze transport phenomena, you need a robust toolkit of numerical techniques. This course guides you through the process of translating physical chemical systems into mathematical models and solving them using modern computational methods. You will start by learning foundational definitions, error analysis, and basic approximation concepts before moving on to practical algorithms. Through clear, text-based explanations and step-by-step code demonstrations, you will gain the skills needed to tackle realistic engineering scenarios. What you'll learn: - Learn the core mathematical foundations of numerical approximation and error analysis. - Solve non-linear algebraic equations representing chemical equilibrium and reactor design. - Apply numerical integration and differentiation to transport phenomena and kinetics. - Solve ordinary and partial differential equations describing transient heat and mass transfer. - Implement clean, modern scientific computing scripts using Python's standard numerical libraries. - Practice translating chemical process flows into structured algorithmic steps. The course begins with key terminology and foundational concepts of error propagation, then moves systematically through root-finding, linear systems, numerical integration, and differential equations. Designed for chemical engineering students and practicing professionals looking to build their computational skills, this course requires only basic calculus and a willingness to learn. Start building your computational engineering toolkit today.