Abstract
This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.
Highlights
Nowadays, freshwater is considered one of the most critical resources for humans, since it can ensure the availability of an acceptable quantity of water for livelihoods, health, ecosystems and production
The data sets of BOD/NH3-N effluents are collected from a wastewater treatment plant in Beijing and are used to verify the effectiveness of the proposed approach
In order to demonstrate the superiority of the proposed improved bald eagle search (IBES)-least-squares support vector machine (LSSVM) method, it is compared with some existing results, i.e., CNN, LSTM, ELMAN, WOA-LSSVM, GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and SSA-LSSVM
Summary
Freshwater is considered one of the most critical resources for humans, since it can ensure the availability of an acceptable quantity of water for livelihoods, health, ecosystems and production. SVM is a small-sample learning method and has been widely used to solve the wastewater prediction problem, the calculation process is multifarious, which is difficult to implement for large-scale training samples [27] To overcome these disadvantages, the least-squares support vector machine (LSSVM) has been proposed. The main idea of this method is to directly construct the upper and lower bounds of PI by optimizing the coefficients of the neural network according to the interval quality evaluation index This approach can provide good performance and does not consider strict data distribution assumptions, such that it can provide more information about the prediction results, which motivates the work of this paper.
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