Deep Learning Seminar / September 10, 2020
Flowsheet Simulation and Optimization Supported by Machine Learning Methods
Reliability, feasibility and computationally efficiency of flowsheet simulations are major prerequisites for online plant control and optimization. However, convergence issues still disturb the automated use of flowsheet simulators in industrially relevant applications: The reasons for a non-convergent simulation run can either be numerical issues or physical infeasibility of the operating conditions to be simulated. This situation results in tedious, time-consuming investigations for the process engineer, which at the end might even compromise the simulation and its inarguable benefits themselves. Rigorous models with included sustainability metrics are typically used to ensure high quality of predictions and to obtain the objectives of interest. In this contribution, machine learning methods are coupled to CHEMASIM, the inhouse flowsheet simulator of BASF. Adaptive simulation plans are generated, which according to the results are adjusted sequentially. As a result, the border between feasible and infeasible operating conditions (in the sense of a binary feasibility classification) is obtained parametrically. Finally, the methodology is applied in the multicriteria optimization of an exemplary process, where economic and environmental targets are included.