在SBR系统中AI-优化的通风控制:一个反向的SVM框架,朝着碳中和的废水处理
Qiu Cheng1,2, Zhou Yang3, Yang Guodong4
1Department of Material and Environmental Engineering, Chengdu Technological University, Chengdu, People's Republic of China.
Environmental technology
|October 4, 2025
概括
这项研究引入了用于废水处理的逆支向量机 (ISVM),优化了通风控制以节省能源,同时满足严格的氨限值. 与传统方法相比,新方法减少了20%以上的能源消耗.
科学领域:
- 环境工程 环境工程
- 环境管理中的人工智能
- 废水处理技术 废水处理技术
背景情况:
- 在测序批量反应器 (SBR) 中优化通风控制对于平衡废水处理中的能源效率和监管合规至关重要.
- 像PID控制器和机械模型这样的传统方法在动态通风控制方面存在局限性.
- 在最大限度地减少能源消耗的同时,满足对氨 (NH3-N) 的严格废水标准仍然是一个重大挑战.
研究的目的:
- 开发和验证一个反向支向量机 (ISVM) 框架,以优化SBR中的通风控制.
- 动态调整通风率,以确保废水NH3-N度保持在5 mg/L以下,同时最大限度地减少能源消耗.
- 为可持续的废水管理提供数据驱动,限制意识的优化模板.
主要方法:
- 使用支持矢量机 (SVM) 建立过程参数和废水NH3-N度之间的输入-输出相关性.
- 集成的SVM具有受约束优化,以创建ISVM框架以优化通风速率.
- 实施基于惩罚的优化和梯度剪切机制,以提高稳定性和性能.
主要成果:
- 经过20个运行周期的实验验证,与固定速率通风相比,能源消耗减少了20.3%.
- 达到95%的排放标准符合排水度NH3-N的排水.
- 该ISVM框架被证明比传统的PID控制器和机械模型更稳定和有效.
结论:
- 拟议的ISVM框架为可持续废水管理提供了一种有效的智能控制策略.
- 这种数据驱动的方法成功地平衡了SBR的能源效率和监管合规性.
- 限制意识优化模板为环境工程系统提供了有价值的工具.
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