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Deep learning-based prediction of effluent quality of a constructed wetland

ABSTRACT: Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering. Graphical abstract Image 1 Highlights • Four prediction models are successfully established through multisource data fusion.• The LSTM shows a satisfactory forecast accuracy.• The moving average method can obviously improve the accuracy of the GA-BPNN.

SUBMITTER: Yang B

PROVIDER: S-EPMC9529666 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Project description:The study assessed the suitability of two effluent types, namely anaerobic filtered (AF) and horizontal flow constructed wetland (HFCW) effluent for Moistube irrigation (MTI). Secondary to this, the study determined the plugging coefficients (α) on MTI for the respective effluents. The feed water was supplied from a raised tank (3.5 m), and mass-flow rates were recorded at 15 min intervals using an electronic balance. The effluent feed water concentrations and experimental room temperature (25 °C ± 1 °C) were continuously monitored and kept constant. Hermia's models based on the [Formula: see text] coefficient was used to select the best fitting fouling mechanism model and, consequently, the plugging coefficients. In addition, microbial colony analysis and scanning electron microscopy (SEM) analysis was carried out to assess the composition of the deposited sediment (DS) and adhered bacterial film (ABF) onto the MTI lateral. The study revealed that MTI pore blocking was a complex phenomenon described by complete pore-blocking model ([Formula: see text] ≥ 0.50). Discharge followed an exponential decay with early fouling observed on AF effluent because of a high concentration of total suspended solids (TSS) and dissolved organic matter (DOM). Discharge declined by 50% after 20 and 10 h of intermittent operation for AF and HFCW effluent, respectively. The α for each effluent (foulant) were [Formula: see text] = 0.07 and [Formula: see text] = 0.05, respectively, for AF and HFCW. The microbial analysis revealed bacterial aggregation structures that contributed to pore blocking. SEM imaging revealed complete surface coverage by deposited sediment. It is concluded that water quality determines the operation life span of MTI, and the two effluents promote accelerated MTI pore fouling or blocking. Continuous use without flushing the MTI will promote membrane degradation and reduced discharge efficiency. Additional filtration can potentially mitigate the membrane degradation process.

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Deep learning-based prediction of effluent quality of a constructed wetland

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