Modeling a Paper-Making Wastewater Treatment Process by Means of an Adaptive Network-Based Fuzzy Inference System and Principal Component Analysis

Statistical monitoring of a wastewater treatment plant: A case study

The efficient operation of wastewater treatment plants (WWTPs) is key to ensuring a sustainable and friendly green environment. Monitoring wastewater processes is helpful not only for evaluating the process operating conditions but also for inspecting product quality. This paper presents a flexible and efficient fault detection approach based on unsupervised deep learning to monitor the operating conditions of WWTPs. Specifically, this approach integrates a deep belief networks (DBN) model and a one-class support vector machine (OCSVM) to separate normal from abnormal features by simultaneously taking advantage of the feature-extraction capability of DBNs and the superior predicting capacity of OCSVM. Here, the DBN model, which is a powerful tool with greedy learning features, accounts for the nonlinear aspects of WWTPs, while OCSVM is used to reliably detect the faults. The developed DBN-OCSVM approach is tested through a practical application on data from a decentralized WWTP in Golden, CO, USA. The results from the DBN-OCSVM are compared with two other detectors: DBN-based K-nearest neighbor and K-means algorithms. The results show the capability of the developed strategy to monitor the WWTP, suggesting that it can raise an early alert to the abnormal conditions.

Statistical monitoring and dynamic simulation of a wastewater treatment plant: A combined approach to achieve model predictive control

The on-line monitoring of Chemical oxygen demand (COD) and total phosphorus (TP) restrains wastewater treatment plants to achieve better control of aeration and chemical dosing. In this study, we applied principal components analysis (PCA) to find out significant variables for COD and TP prediction. Multiple regression method applied the variables suggested by PCA to predict influent COD and TP. Moreover, a model of full-scale wastewater treatment plant with moving bed bioreactor (MBBR) and ballasted separation process was developed to simulate the performance of wastewater treatment. The predicted COD and TP data by multiple regression served as model input for dynamic simulation. Besides, the wastewater characteristic of the wastewater treatment plant and MBBR model parameters were given for model calibration. As a result, R² of predicted COD and TP versus measured data are 81.6% and 77.2%, respectively. The model output in terms of sludge production and effluent COD based on predicted input data fitted measured data well, which provides possibility to enabled model predictive control of aeration and coagulant dosing in practice. This study provide a feasible and economical approach to overcome monitoring and modelling restrictions that limits model predictive control of wastewater treatment plant.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

Analysis of point source pollution and water environmental quality variation trends in the Nansi Lake basin from 2002 to 2012

Based on the data analysis of the water environmental quality and economic development from 2002 to 2012 in the Nansi Lake basin, the correlation and change between the water environmental quality and economic development were studied. Results showed that the GDP and wastewater emissions of point source in the Nansi Lake basin had an average annual growth of 7.30 and 7.68 %, respectively, from 2002 to 2012. The emissions of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) had the average annual decrease of 7.69 and 6.79 % in 2012, respectively, compared to 2002. Basin water quality overall improved, reaching the Class III of the "Environmental quality standards for surface water (GB3838-2002)," in which the main reason was that sewage treatment rate increased gradually and was above 90 % in 2012 (an increase of 10 % compared to 2002) with the progress of pollution abatement technology and the implementation of relevant policies and regulations. The contribution of water environmental pollution was analyzed from related cities (Ji'ning, Zaozhuang, Heze). Results indicated that Ji'ning had the largest contribution to water pollution of the Nansi Lake basin, and the pollutant from domestic sources accounted for a higher percentage compared to industrial sources. The wastewater, COD, and NH3-N mainly came from mining and washing of coal, manufacture of raw chemical materials and chemical products, papermaking industry, and food processing industry. According to the water pollution characteristics of the Nansi Lake basin, the basin pollution treatment strategy and prevention and treatment system were dissected to provide a scientific basis for prevention and control of lakeside point source pollution along the Nansi Lake.