Case-study of a flowsheet simulation using deep-learning process models for multi-objective optimization of petrochemical production plants

This work demonstrates a case-study to create machine-learning based process models from process data, which are connected into an overall process flowsheet and provide a high level of numerical stability for further multi-objective optimization. The used models are black-box and gray-box models, which are further compared to the proven LP approach. The proposed methodology is demonstrated using real-world measurement data from a refinery, involving a distillation unit, a hydrotreater, a reformer and an ethylene plant. The developed unit models are connected into an overall process flowsheet, which is solved by a sequential-modular approach and optimized in view of maximizing production margin and minimizing CO₂-emissions. This work points out that the combination of engineering knowledge with data-driven techniques enables the incorporation of indirect information for process units, e.g., the crude oil composition vector for downstream units, leveraging the prediction performance of the unit models, compared to models not involving this information.