Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process

Since wastewater treatment processes (WTP) are generally accompanied with intense coupling and fuzziness, conventional biochemical mechanisms-based methods cannot comprehensively express the WTP due to limited computational ability. In response to the challenge caused by fuzziness, this paper proposes a hybrid control and prediction system for modeling WTP with the fuse of Activated Sludge model, Convolutional neural network and Long short-term memory neural networks (AS-CL) with knowledge and data-driven characteristics. Moreover, the activated sludge model is employed to model the wastewater treatment process based on the perspective of knowledge. Besides, the hybrid neural network that combines convolutional neural network and long short-term memory model is adopted to model the WTP from the perspective of data. Then, a multi-layer perception model is set up to realize collaborative awareness of data and knowledge. Lastly, the proposed AS-CL has been evaluated by a real-world data-set collected from a real sewage treatment plant. The results show that compared with typical existing methods, the proposal improves modeling efficiency. With the collaborative modeling scheme, influence from fuzziness on WTP can be reduced to some extent. Compared with five benchmark methods of the two evaluation indexes, the results of AS-CL show that the average performance of this method exceeds 7% of the baseline.