期刊
NEURAL COMPUTING & APPLICATIONS
卷 33, 期 17, 页码 11401-11414出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05659-z
关键词
Radial basis function (RBF) networks; Adaptive task-oriented; Second-order algorithm; Water quality parameters prediction
资金
- National Science Foundation of China [61903012, 61533002, 61890930-5]
- National Key Research and Development Project [2019YFC1906004-2]
- Beijing Natural Science Foundation [4204088]
An adaptive task-oriented radial basis function (ATO-RBF) network was developed to design prediction models for accurate timely acquirements of effluent BOD and TN in wastewater treatment plants. The network combined error correction-based growing strategy and second-order learning algorithm to enhance learning ability and generalization performance of prediction models. The ATO-RBF network analysis based on the Lyapunov criterion showed superior prediction accuracy compared with conventional methods.
The real-time availability of key water quality parameters is of great importance for an advanced and optimized process control in wastewater treatment plants (WWTPs). However, due to the complex environment conditions and costly measuring instruments, it is generally difficult and time-consuming to measure certain key water quality parameters online, such as the effluent biochemical oxygen demand (BOD) and the effluent total nitrogen (TN). Recently, artificial neural networks have powered the online prediction tasks in several WWTPs. Hence, in this paper, an adaptive task-oriented radial basis function (ATO-RBF) network is developed to design prediction models for accurate timely acquirements of the effluent BOD and the effluent TN. The advantage of ATO-RBF network is that the architecture is not designed by human engineers; it is adaptively generated from the data to be processed. First, to enhance the learning ability and generalization performance of prediction models, an error correction-based growing strategy and a second-order learning algorithm are combined to design the ATO-RBF network. Then, RFB nodes with low significance would be pruned without sacrificing the learning accuracy, making the prediction model more compact. Additionally, the convergence of the ATO-RBF network is analyzed based on the Lyapunov criterion, which can guarantee its feasibility in practical applications. Finally, the proposed methodology is verified by benchmark simulations and real industrial data, showing superior prediction accuracy in compared with conventional methods.
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