Python in Chemical Process Simulation

Python is a powerful programming language that has become increasingly important in the field of chemical engineering. One reason for its growing popularity is its versatility and ability to handle a wide range of tasks. It offers libraries, such as SciPy and NumPy to perform complex mathematical calculations, or libraries for data analysis, such as pandas and scikit-learn to work with large sets of data.

Despite its many strengths, Python is not currently able to fully replace specialized process simulation software in chemical engineering. These simulation software packages, such as Aspen Plus, HYSYS, CHEMCAD, ProII and DWSIM, have been developed specifically for use in the chemical industry and have many more advanced features than those available in Python libraries. For example, they may have more detailed thermodynamic models, a more comprehensive library of chemical compounds, or pre-built unit operations, e.g., distillation columns and reactors.

There are several ways to integrate Python with a chemical process simulator depending on the specific chemical process simulator being used and the desired level of integration. Directly integrating Python with a chemical process simulator by using its Application Programming Interface (API) allows to run the chemical process simulator and automate it with Python scripts.

This enables to combine simulator-internal and external optimizers creating complex simulations that can be used to analyze and predict chemical processes, which aids in the design and optimization of chemical processes, leading to more efficient and sustainable, and cost-effective production.

This contribution shows how HYSYS, CHEMCAD, and DWSIM can be used from Python to create surrogate models (SM) from flowsheet simulations (PFD), embed SM in PFDs, optimize PFDs using SM, and create new PFDs using AI assistance.