文献类型：期刊文献

英文题名：Modeling and Optimization of a Large-Scale Ethylene Plant Energy System with Energy Structure Analysis and Management

作者：Shen, Feifei[1];Wang, Xiaoqiang[1];Huang, Lingxiang[1];Ye, Zhencheng[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2019

卷号：58

期号：4

起止页码：1686

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20190506443161);WOS:【SCI-EXPANDED(收录号:WOS:000457816000020)】；

基金：This work was supported by National Science Fund for Distinguished Young Scholars (61725301), International (Regional) Cooperation and Exchange Project (61720106008) and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Integer programming - Cooling - Fuels - Uncertainty analysis - Nonlinear programming - Cooling water - Energy utilization - Least squares approximations - Energy management - Operating costs

摘要：The energy system of industrial process, particularly in the petrochemical industry, consumes most of the utility cost. In this paper, a superstructure of a large-scale industrial ethylene plant energy system including fuel, steam, electricity and water was studied. In this system, multitype energy is transferred by water, as the working medium, which makes it feasible for the multitype energy to be synthesized according to the heating, cooling, and phase changes of water. The unit models were developed by hybrid modeling method combining thermodynamics and least-square method (LSM). The seasonal energy system optimization based on typical day method was formulated as an mixed-integer nonlinear programming (MINLP) problem. Then, an efficient decomposition-based model solving strategy was proposed for solving this difficult problem, in which the fuel, steam, electricity, and water consumption were simultaneously optimized. The optimal operational solution was obtained by the following strategies: (1) regulating the steam flow rate in letdown valves, the condensing steam flow rate extracted from turbines, and selections of power sources for low demand mechanical users synergistically; (2) determining the cooling water temperature to balance the turbine efficiency and the electricity and water consumption; and (3) employing different numbers of cooling towers according to the seasons. The flow rate-related decisions are sensitive to uncertainty in the measurement, while the temperature-related and pressure-related ones are relatively more stable. The results showed that the total energy consumption was reduced by 14.42% in spring-autumn and 13.92% in summer, which were 1.44 and 0.89% better than these using the two-type energy optimization method in literature, respectively. Further energy structure analysis exhibiting consumption proportion of different types of energy showed that part of the fuel consumption was replaced by cheaper steam and electricity to reduce total energy cost. Finally, energy management strategies were formed on the basis of the above results.