An energy efficiency integration optimization scheme for ethylene production with respect to multiple working conditions

摘要

Ethylene production is an energy-intensive process, hence energy management and optimization play a crucial role in saving energy and increasing economic benefits. In industrial-scale ethylene production, the energy efficiency level is greatly influenced by different working conditions and multiple energy arrangements in different sub-processes. Energy efficiency optimization is a more direct and scientific way to improve efficiency and reduce consumption. However, conventional energy optimization schemes are implemented without due consideration of the above two factors adequately, and energy efficiency indicators are not considered a key objective of optimization. Aiming at the energy efficiency optimization problem of ethylene plant under multiple working conditions, an energy efficiency integration optimization scheme is proposed, combining multi-level production process and multi-condition technology. The traditional single optimization model cannot achieve the energy efficiency improvement of the ethylene production process characterized by the multi-condition and hierarchical architecture. To this end, by establishing dynamic models of the system level, process level and equipment level, and considering the associations at different levels, energy efficiency optimization models of ethylene production for different working conditions are established to realize an energy optimization management that maximizes the overall energy utilization efficiency of production. For the solution of model, a multi objective particle swarm optimization algorithm based on historical working condition knowledge base is proposed to improve the performance of the optimization algorithm by guiding the oriented local area search. The effectiveness of the proposed scheme is verified through the application in a Chinese ethylene plant. The optimization results show that the overall energy efficiency of ethylene production has been significantly improved despite frequent changes in working conditions. (C) 2019 Elsevier Ltd. All rights reserved.