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在不确定性下过程的综合设计和调度:量子神经网络方法

Lavinia M P Ghilardi^1,2, Gabriel D Patrón^1,2, Antonio Alcántara³

¹Department of Computing, Imperial College London, London SW7 2AZ, U.K.

Industrial & engineering chemistry research

|November 10, 2025

概括

这项研究引入了量子神经网络,以优化生产厂的设计和调度,在不确定的电价下. 这种方法降低了计算成本,并允许做出风险意识的决策,提高了工厂的弹性.

科学领域:

化学工程是化学工程的重要组成部分.
优化优化优化优化
机器学习机器学习

背景情况:

基于电解的生产厂的设计和调度面临着来自电价预测的不确定性.
对于这个问题,传统的二阶段随机编程方法是计算密集的.

研究的目的:

为生产中随机编程的第二阶段开发一个计算效率高的替代模型.
为了实现风险意识优化,使用量子神经网络进行集成设计和调度.

主要方法:

利用量子神经网络作为二阶段随机程序中的第二阶段值函数的替代模型.
将神经网络代理嵌入到优化框架中,以避免基于采样的近似.
融入的有条件风险价值 (CVaR),以及对联合优化的预期.

主要成果:

与样本平均近似相比,以代孕为基础的方法显著减少了计算要求.
整合风险措施 (CVaR) 的优化导致了对电解器和存储的投资增加.
该方法为集成电厂的设计和调度提供了高质量的决策.

结论:

量子神经网络代理提供了一个高效和有效的解决方案,以优化在价格不确定性下的生产.
整合风险措施可以提高工厂的稳定性,以应对不稳定的电力成本.
这种基于替代品的方法推进了可再生能源应用过程系统工程领域的发展.

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