Implementation of a Process Optimization Methodology Using Aspen Plus and Python

This study presents an innovative approach to chemical process optimization by integrating Aspen Plus v14 simulation software with Python 3.1 programming language, leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The methodology establishes a seamless connection between Aspen Plus and Python using the Windows Component Object Model (COM) interface, allowing for automated process optimization. A methanol separation process was selected as a representative case study to demonstrate the effectiveness of this approach. The multi-objective optimization problem aimed to simultaneously maximize methanol production and minimize energy consumption, subject to a minimum purity constraint of 95% in the final product. The NSGA-II algorithm, implemented using the Pymoo library in Python, was employed to explore the solution space efficiently. Results revealed a clear trade-off between methanol production (ranging from 1470 kg/h to 1840 kg/h) and energy consumption (0.95 MW to 2.95 MW). The obtained Pareto front provides process operators with a range of optimal solutions, balancing productivity and energy efficiency. This methodology not only offers a cost-effective alternative to traditional optimization techniques but also enhances accessibility and flexibility in process optimization. The successful application of this approach to methanol separation underscores its potential for improving efficiency and sustainability in various industrial chemical processes.