数据知识驱动的多目标适应性最佳控制多种操作条件下对废水处理过程的最佳控制
IEEE transactions on cybernetics
|March 3, 2025
概括
一个新的数据知识驱动的多目标适应性最佳控制 (DK-MAOC) 战略增强了废水处理过程 (WWTP). 这种方法确保了废水质量,并通过适应各种操作条件来降低能源消耗.
科学领域:
- 环境工程 环境工程
- 过程控制 过程控制
- 废水处理 废水处理
背景情况:
- 废水处理过程 (WWTP) 面临着由于多种操作条件的挑战.
- 有效的控制策略对于安全和高效的WWTP运行至关重要.
- 识别和适应不同的条件对于最佳性能至关重要.
研究的目的:
- 为WWTPs提出一个新的数据知识驱动的多目标适应性最佳控制 (DK-MAOC) 战略.
- 为了解决废水处理中的多种操作条件的复杂性.
- 改善废水质量,减少WWTP中的能源消耗.
主要方法:
- 利用模糊神经网络 (FNN) 预测酸盐和总度,以确定操作条件.
- 开发了一个自适应目标函数 (AOF),以根据特定条件动态调整运营指数.
- 将操作知识集成到FNN模型中,以在不断变化的条件下提高预测准确度.
- 采用协作梯度下降算法来解决设定点和控制规律.
主要成果:
- DK-MAOC的战略有效地防止了废水中酸盐和总的超值.
- 拟议的方法证明了WWTPs的能源消耗减少.
- 使用基准模拟模型编号验证的有效性. 1. 1. 1. 1. 这是一个很棒的节目.
结论:
- DK-MAOC 战略提供了一个强大的解决方案,用于在各种条件下优化WWTP运行.
- 这种方法确保符合废水标准,同时提高能源效率.
- DK-MAOC保证了废水处理厂的最佳运行.
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