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.

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In chemical industry, most processes face the challenge of high energy consumption.The approach presented in this study can reduce the energy footprint and increase efficiency. The energy system of a separation process in ethylene manufacturing is used to demonstrate the effectiveness of the approach. The chilling train system of the separation process in a typical ethylene plant consumes most cooling and provides appropriate feed for distillation columns. The steady state simulation of system was presented and the simulation results were proved accurate. The conventional exergy analysis identifies that Dephlegmator No.1 (a heat exchange and mass transfer device) has the highest exergy destruction (1401.28 kW). Based on advanced exergy analysis, Dephlegmator No.1 has the highest rate of avoidable exergy destruction (89.04 %). Finally, a multiobjective optimisation aiming to maximise system exergy efficiency and to minimise operational cost was performed and the Pareto frontier was obtained. The optimized exergy efficiency is 79.53 % (improved by 0.61 %) and the operational cost is 0.02031 yuan/kg (saved by 11.19 %). This study will guide future research to reduce energy consumption in process manufacturing.

Achieving carbon neutrality has been one of the main tasks these decades. In this study, renewable energies were introduced to reduce the greenhouse gas emissions of industrial energy systems. Considering solar heat and wind energy uncertainties, a data-driven stochastic robust optimization framework was proposed. Machine learning methods were applied to derive data information: a data mining method to classify the highvolume uncertain data; a kernel-based method to construct the uncertainty sets for each data class. The stochastic robust optimization model of the industrial energy system was developed as a bilevel optimization procedure: the outer level is a two-stage stochastic programming problem to optimize the expected objective value of different data clusters, and the robust optimization is nested internally to ensure robustness. A case study on the practical industrial energy system was performed, and the results are that the total annual cost is reduced by 1 507 730 $/a and 7.62% GHG emissions are decreased by introducing renewable energies; the proposed method is superior to the traditional ones in terms of PoR (2.91%) and robustness (99.79%). The results of multiobjective optimization considering economic and environmental revenues can provide multipreference schemes for decision-makers.