Welcome to CHME 212 Course

Welcome to CHME 212 Course#

Scientific computing leverages computers to support tasks such as data processing, simulations, and modeling. In chemical engineering, it plays a central role in:

  • Process modeling and simulation: Predicting reactor performance, separation processes, and heat and mass transfer

  • Process optimization: Maximizing yield, efficiency, or energy savings

  • Control and monitoring: Analyzing sensor data for real-time process control

  • Material and property prediction: Estimating thermodynamic, transport, or kinetic properties

  • Safety analysis: Simulating worst-case scenarios to design safer processes

Learning programming allows students to implement these models, analyze results, and extend their work to emerging areas such as machine learning, data-driven process optimization, and predictive modeling. No prior programming experience beyond first-year chemical engineering knowledge—including heat and mass balances, basic mathematics, and chemistry—is assumed. Python is particularly effective because it is:

  • Easy to learn and use, even for beginners

  • Versatile, supporting numerical computing, data analysis, visualization, and modeling

  • Extensible through libraries, such as NumPy, SciPy, matplotlib, pandas, and scikit-learn

  • Free and open-source, running on Windows, macOS, and Linux

  • Widely adopted in industry and research, making skills directly transferable, especially for machine learning applications

Learning Python equips students to perform traditional modeling tasks and extend their work to machine learning, predictive modeling, process optimization, and advanced data analysis, preparing them to tackle modern challenges in chemical engineering.

alt text

Basic Python

Numerical Computation

Lab Exercises