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

Abstract 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.