关键词: 环氧乙烷, 乙二醇, 神经网络, 遗传算法, D-G模型, 代理模型, 优化

Abstract:

In the context of the national "dual carbon" initiative, optimizing process parameters to reduce energy consumption in chemical processes is crucial for energy conservation and emission reduction. The catalytic hydration process for producing ethylene glycol is energy-intensive, requiring operational parameter optimization to minimize energy consumption. However, achieving simultaneous multi-parameter optimization solely based on process simulation is challenging due to process complexity. This paper proposed a solution that integrated process simulation-generated data, surrogate model construction, and synchronized optimization of thermal integration. Process simulation was conducted for the annual production of 300000t of ethylene glycol from ethylene oxide. Surrogate model output variables were determined based on utility engineering locations, while sensitivity analysis was used to determine input variables. Sobol random sequences were used to generate sample points, and simulation provided real data considering the mechanistic model. A data-driven approach using neural networks was utilized to construct the surrogate model. Finally, a synchronous algorithm, combining a genetic algorithm and a D-G model, was utilized to optimize the surrogate model with the goal of minimizing the total public utility project cost. The obtained optimal process parameters led to a 4.89% reduction in cost compared to that before optimization. This demonstrated the effectiveness of the method and highlighted its potential for addressing complex full-process synchronous optimization problems in thermal integration.

Key words: ethylene oxide, ethylene glycol, neural networks, genetic algorithm, D-G model, surrogate model, optimization