基于分布预测的强大的多目标优化,用于废水处理过程的时间链接不确定性.
IEEE transactions on cybernetics
|February 19, 2026
概括
本研究介绍了废水处理过程的新算法,以提高运行稳定性. 基于分布预测的强大的多目标优化 (DP-RMO) 算法有效地管理时间相关的不确定性,改善废水质量并降低成本.
科学领域:
- 环境工程 环境工程
- 优化技术 优化技术
背景情况:
- 废水处理过程 (WWTP) 由于不确定性而面临运营挑战.
- 连续的WWTP阶段的时间链接不确定性使强大的优化变得复杂.
研究的目的:
- 提出一个基于分布预测的强大的多目标优化 (DP-RMO) 算法.
- 通过获得强大的最佳设定点来提高WWTP的运行稳定性.
- 解决废水质量 (EQ) 和运营成本 (OC) 目标中的时间联系不确定性.
主要方法:
- 使用自适应内核函数建立了强大的多目标优化 (MOO) 目标.
- 开发了一种基于高斯过程 (GP) 的数据驱动预测器,以捕捉时间链接不确定性.
- 实施了一种自我调整的进化策略,以优化强大的目标功能.
主要成果:
- DP-RMO算法有效地减少了时间链接不确定性的不利影响.
- 通过DP-RMO获得的最佳设定点显示了改善的情商和OC.
- 保持了强度性能,同时提高了EQ和OC.
结论:
- DP-RMO提供了一个强大的解决方案,用于管理WWTP中复杂的不确定性.
- 该算法提高了废水处理的运行稳定性和经济效率.
- DP-RMO提供了一种可行的方法来动态优化WWTP.
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