Abstract
The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA‐FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full‐scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self‐adapted fuzzy c‐means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA‐FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.
Highlights
Because of the economic advantages and low generation of excess sludge, the anaerobic biological treatment process is an e cient process for treating high-concentration organic wastewater, such as paper-mill wastewater, where the complex organic contaminants can be converted into clean energy in the anaerobic treatment process [1,2,3]
Erefore, clarification of the place of the present subject in the scheme of the fuzzy neural network (FNN) methodology can be considered a particular field of investigation to evaluate real-time effluent quality and biogas production rates that are necessary to control the anaerobic process and to establish fault diagnosis
Based on the relationship between the effluent chemical oxygen demand (COD) and the biogas flow rate under various operating parameters such as influent COD (CODinf ), hydraulic retention time (HRT), organic loading rates (OLRs), pH in the reactor, and alkalinity in the reactor (ALK), an fuzzy wavelet neural network (FWNN) model is developed to predict and estimate the effluent quality and biogas production rates based on the existing historical data. e key objective of this study was to develop a novel hybrid genetic algorithm evolving FWNN model for simulating the functioning problem of a full-scale internal circulation (IC) anaerobic wastewater treatment plant. e proposed hybrid model may be used for analyzing the biogas production rate and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system
Summary
Because of the economic advantages and low generation of excess sludge, the anaerobic biological treatment process is an e cient process for treating high-concentration organic wastewater, such as paper-mill wastewater, where the complex organic contaminants can be converted into clean energy (methane gas) in the anaerobic treatment process [1,2,3]. Erefore, clarification of the place of the present subject in the scheme of the FNN methodology can be considered a particular field of investigation to evaluate real-time effluent quality and biogas (methane) production rates that are necessary to control the anaerobic process and to establish fault diagnosis. Erefore, the hybrid FWNN offers a more efficient method for modeling, simulation, control, and operation optimization of the complex process system, such as the wastewater treatment process. Various potential advantages based on such an artificial intelligencebased model for real-time evaluation of effluent quality and biogas production rates would be fully demonstrated, such as withstanding various shock loads caused by substantial influent fluctuations, optimizing operational parameters of the process for controlling operational cost, providing an online evaluation and estimation of emissions on an energetic basis, and building a continuous early-warning strategy without requiring a complicated model structure. Based on the relationship between the effluent COD and the biogas flow rate under various operating parameters such as influent COD (CODinf ), HRT, OLR, pH in the reactor (pH), and alkalinity in the reactor (ALK), an FWNN model is developed to predict and estimate the effluent quality and biogas production rates based on the existing historical data. e key objective of this study was to develop a novel hybrid genetic algorithm evolving FWNN model for simulating the functioning problem of a full-scale internal circulation (IC) anaerobic wastewater treatment plant. e proposed hybrid model may be used for analyzing the biogas production rate and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system
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