(474h) Global Deterministic Surrogate-Based Process Design
Various works on ANN-based process optimization are available (e.g. [7-10]). In these works, ANNs learn unit operations or sub-process from simulations or experimental data. Then, the ANNs are aggregated to entire (hybrid) process models, which are finally optimized. In the previous literature, ANN-based process optimization has mostly been limited to local or stochastic global solution approaches (e.g., genetic algorithm). These methods can fail to identify global optima and cannot give a guarantee for global optimality. Recent advances in global deterministic optimization with ANNs embedded allow to overcome this limitation .
The emerging research field of global deterministic ANN-based process optimization develops new potentials but raises research questions, which are discussed using an organic Rankine cycle process optimization as an illustrative case study. Herein, the complex Helmholtz thermodynamics of the ethanol working fluid is learned via ANNs to eliminate implicit functions and auxiliary variables from the optimization problem. The results show that the solution time of the problem is reduced significantly using the proposed approach. Further, the presentation discusses the influence of the ANN size on its prediction accuracy and the efficiency of global optimization, i.e., tightness of its relaxations.
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