Abstract:

One major challenge of hydrogen network integration in refinery is the optimization of hydrogen network with complex process flows. To address this issue, a collaborative optimization method based on evolutionary response surface method is proposed to simultaneously optimize the methanol production process and the hydrogen network. This method establishes mechanistic models for the reactor, flash tank, and distillation column in the CO₂ hydrogenation to methanol process. Based on these, this method constructs corresponding evolutionary surrogate models, which are validated and refined using mechanistic model results. An efficient optimization framework is proposed to enhance model accuracy and algorithmic iteration efficiency. This method is applied to the hydrogen network integration optimization of a refinery, considering factors such as methanol yield, equipment manufacturing costs, and hydrogen consumption. Results demonstrate that the method effectively increases methanol production while optimizing hydrogen network within the refinery, reducing equipment manufacturing and hydrogen consumption costs, and achieving an annual economic benefit of more than 7 million CNY. The method is computationally efficient and significantly improves the accuracy of optimization results compared to traditional approaches, providing an effective solution for the optimization of refinery hydrogen network with methanol production processes.

Key words: evolutionary response surface, hydrogen network, methanol, optimization, refinery

摘要：

复杂工艺流程与氢网络的协同优化是炼厂氢网络集成中的难题。为此，本文提出了一种基于进化响应面的协同优化方法，同步优化甲醇工艺流程与氢网络。该方法构建了CO₂加氢制甲醇流程中反应器、闪蒸罐和精馏塔的机理模型，基于此得到的样本集建立相应的进化响应面模型，利用机理模型验证响应面模型的优化结果并进行修正，提出高效的优化流程框架。将此方法应用于某炼厂的氢网络集成优化，综合考虑了甲醇产量、设备制造成本和用氢成本等因素。结果表明，该方法能够在提升甲醇产量的同时优化炼厂的氢资源分配，降低设备制造与用氢成本，提升了炼厂七百多万元的年度经济效益。该方法求解高效，优化结果的准确性较传统方法显著提高，为炼厂氢网络与甲醇工艺流程的协同优化提供了有效的解决方案。

关键词: 进化响应面, 氢网络, 甲醇, 优化, 炼厂