Abstract Two separate goals should be jointly pursued in wastewater treatment: nutrient removal and energy conservation. An efficient controller performance should cope with process uncertainties, seasonal variations and process nonlinearities. This paper describes the design and testing of a model predictive controller (MPC) based on neuro-fuzzy techniques that is capable of estimating the main process variables and providing the right amount of aeration to achieve an efficient and economical operation. This algorithm has been field tested on a large-scale municipal wastewater treatment plant of about 500,000 PE, with encouraging results in terms of better effluent quality and energy savings.

中文翻译：

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基于机器学习的污水处理厂实时模型预测控制

摘要废水处理应共同追求两个不同的目标：去除营养物和节约能源。高效的控制器性能应能够应对过程不确定性、季节变化和过程非线性。本文描述了基于神经模糊技术的模型预测控制器（MPC）的设计和测试，该控制器能够估计主要过程变量并提供适量的曝气以实现高效且经济的运行。该算法已在约 500,000 PE 的大型城市污水处理厂进行了现场测试，在更好的出水水质和节能方面取得了令人鼓舞的结果。