Effluent water quality prediction method based on no-inflected point neural network

Tonglai Xue; Tianyu Gao; Meng Zhou; Fei Han; Chun Liu

doi:10.1117/12.2673610

22 May 2023 Effluent water quality prediction method based on no-inflected point neural network

Tonglai Xue, Tianyu Gao, Meng Zhou, Fei Han, Chun Liu

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401B (2023) https://doi.org/10.1117/12.2673610
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

In practice, the effluent quality is the most important indicator to measure the performance of wastewater treatment. However, most existing sensing technology are difficult to maintain the real-time accurate of online monitoring. In his paper, a novel no-inflection point neural network (NI-BP) with adaptive learning rate is proposed for wastewater quality indicators prediction. The proposed algorithm updates the weight parameters by an adaptive learning rate method when some inflection points are detected, it can increase the weight iteration step size and make the network converge quickly. In the simulation examples, the data from Benchmark simulation model NO.1 (BSM1) are used to demonstrate the effectiveness of the proposed method. The results show that the proposed NI-BP can significantly accelerate the model training efficiency and the prediction accuracy, simultaneously.

Citation Download Citation

Tonglai Xue, Tianyu Gao, Meng Zhou, Fei Han, and Chun Liu "Effluent water quality prediction method based on no-inflected point neural network", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401B (22 May 2023); https://doi.org/10.1117/12.2673610

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