Surrogate Modelling of an Industrial Distillation Column Obtained from Statistical Techniques and Machine Learning

Abstract

Purpose: To study a case of modeling an industrial distillation system, using the Aspen Plus simulator as the mathematical model and subsequently generating surrogate models using Machine Learning techniques in Matlab. Theoretical Framework: Metamodels are reduced models obtained from data generated by rigorous models, replacing them entirely or partially when the computational codes originating from them require excessively large computational effort to be used feasibly. Method/Design/Approach: The simulation of the process was performed in Aspen Plus. Subsequently, the most important variables were selected, and an experimental design was created using Latin Hypercube Sampling in Matlab, generating points to be used in sensitivity analysis in Aspen Plus. The next step was the construction of metamodels using the Statistics and Machine Learning toolbox in Matlab, employing linear regression and Gaussian process regression models. Finally, a statistical analysis was conducted. Results and Conclusion: The distillation system simulation converged, and the obtained metamodels had good regression indicators, especially the Gaussian process regression models, making them the most suitable for representing the rigorous Aspen Plus model. Research Implications: Understanding the multicomponent distillation process integrated with computational tools and data regression models, leading to a reduction in computational effort. Originality/Value: Development of a tool that enables the simulation and evaluation of a distillation process without the need to acquire software with rigorous equation databases.