In recent years, the chemical engineering field has been shifting from MATLAB to Python for large-scale optimizations and data science applications. However, a new programming language, Julia, is gaining terrain in the field and has the potential to improve simulation speed and accuracy further. This work explores the differences between the two languages and proposes a detailed overview of the best-suited applications for each language. A GitHub repository containing tutorials on how to get started with Julia and examples of comparable code in both Python and Julia is introduced. The integration of Process Analytical Technology (PAT) with advanced modelling and simulation is greatly enhanced by the performance capabilities of modern programming languages like Julia and Python, underscoring the critical role of language selection in optimizing analytics. This study will aid researchers and practitioners in making an informed decision on which language fits a specific project best.
Python and Julia are both valid options in the field of chemical engineering. They do have, however, clear advantages:
- Python advantages: Easy-to-learn syntax, broad documentation, vast selection of machine learning models, large and active community, good libraries for visualizations, widespread and accepted.
- Julia advantages: fast and optimized for heavy numerical and scientific computing, similarities with MATLAB which can facilitate the transition, SciML library has very good ML models.