An efficient and invertible machine learning-driven multi-objective optimization architecture for light olefins separation system

Abstract

The transience, cyclicity, and flexibility of process operation make the optimization of light olefins separation (LOS) system computationally intensive. An efficient and invertible architecture by incorporating machine learning techniques into optimization routines is proposed. Initially, a steady-state model of the LOS system was built allowing verification with industrial data and invocation through a COM interface. Then, artificial neuron networks (ANNs) were implemented to establish surrogate models for individual fitness calculation. Finally, to find the optimal operating parameters under the targets of energy and economy, an improved non-dominated sorting genetic algorithm was employed along with the ANN-based surrogate models. Optimization results demonstrates that the proposed architecture provides much better performance of computational efficiency with only 0.8 h, while the traditional surrogate models take more than 53 h. Furthermore, the reintroduction of optimized parameters into the LOS system model constructed by Aspen Plus V12.1 demonstrates the satisfactory fulfillment of the separation requirements and affirms the excellent invertibility of the proposed architecture. Essentially, it has been proved to be a powerful tool for multi-objective optimization that can be applied to large-scale chemical processes to derive insights for sustainable development and clean production.