Applied Numerical Methods for Chemical Engineering

Many critical problems in chemical engineering, from process design to optimization, require computational approaches to find solutions. This course will equip you with the foundational understanding and practical skills to apply numerical methods for solving a wide range of chemical engineering challenges, enabling you to analyze, predict, and optimize processes effectively. What you'll learn: Understand core concepts of numerical analysis, precision, and error propagation. Apply root-finding methods like bisection and Newton-Raphson to solve non-linear chemical engineering equations. Implement numerical integration and differentiation techniques for rate calculations and process modeling. Solve systems of linear and non-linear algebraic equations relevant to mass and energy balances. Use numerical methods for solving ordinary differential equations (ODEs) encountered in reaction kinetics and transport phenomena. Practice implementing these methods efficiently using Python and its scientific libraries. Learn basic principles of computational efficiency and stability in numerical algorithms. The course begins with fundamental concepts of numerical analysis and error, then progresses through various essential numerical techniques. Each method is explained theoretically and demonstrated with practical examples, culminating in their application to real-world chemical engineering scenarios. This course is designed for aspiring chemical engineers, students, and professionals with no prior experience in numerical methods or programming, who wish to build a strong foundation in computational problem-solving. Begin your journey into computational chemical engineering and enhance your problem-solving capabilities.