Energy efficiency enhancement of energy and materials for ethylene production based on two-stage coordinated optimization scheme

Nowadays with the growing requirements of sustainable development, energy efficiency enhancement plays a crucial role in the petrochemical industries. In fact, the energy efficiency level depends on the coordination management of energy and materials. Therefore, a two-stage optimization scheme with respect to coordination of energy and materials from the entire process to the key sub-process is proposed for improving energy efficiency level of ethylene production. Firstly, a data-driven model for feedstock and plant-level economic indicators and is established by extreme learning machine (ELM) integrated domain adaptive manifold regularization (DAMR) and particle swarm optimization (PSO), called DAMR-PSO-ELM. Secondly, a multi-objective optimization model for feedstock proportions is established and a cascading-priority weight strategy is made for obtaining the optimal feedstock proportions under the different market demands. Finally, fuel-feedstock ratio optimization models for cracking furnaces are built with respect to the obtained optimal proportions, and an improved elitist teaching-learning-based optimization algorithm is proposed to acquire the optimal fuel. The effectiveness and feasibility of the proposed coordinated optimization scheme are validated from a practical ethylene plant, results show that energy consumption of cracking production is reduced by 4.89% and energy efficiency of the entire ethylene process is increased by 6.82% on average. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

A two-stage coordinated optimization scheme for ethylene production is proposed, which includes the establishment of a data-driven model and a multi-objective optimization model. The effectiveness of this scheme is validated in a practical ethylene plant.