Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.

Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.

Optimal Control of Combined Sewer Overflows

Combined sewer networks carry wastewater and stormwater together.Capacity limitation of these sewer networks results in combined sewer overflows(CSOs)during high-intensity storms.Untreated CSOs when directly discharged to the nearby natural water bodies cause many environmental problems.Controlling existing urban sewer networks is one possible way of addressing the issues in urban wastewater systems.However,it is still a challenge,when considering the receiving water quality effects.This paper presents an evolutionary constrained multi-objective optimization approach to control the existing combined sewer networks.The control of online storage tanks was taken into account when controlling the combined sewer network.The developed multi-objective approach considers two important objectives,i.e.the pollution load to the receiving water from CSOs and the cost of the wastewater treatment.The proposed optimization algorithm is applied here to a realistic interceptor sewer system to demonstrate its effectiveness.

Performance of Submerged Membrane Bioreactor for Domestic Wastewater Treatment

In the present research, a submerged membrane bioreactor was tested to treat domestic wastewater. Three experimental runs were conducted all with a hydraulic retention time of 5h and sludge retention times (SRTs) of 5, 10, and 20 d. The pollutant removal performance of the membrane bioreactor, the membrane effluent quality, and a kinetic model for sludge growth in the bioreactor were investigated. The combined process was capable of removing over 90% of both COD (chemical oxygen demand) and NH 3 N on the average. The total removal for COD was almost independent of SRT, but that for NH 3 N improved with increasing SRT. Membrane effluent quality meets the water quality standard for reuse issued by the Ministry of Construction of China. Increasing SRT causes the concentrations of suspended solids (SS) and volatile suspended solids (VSS) in the bioreactor to increase. However, the ratio of VSS/SS did not change much. Kinetic analysis showed that the sludge yield coefficient (kg VSS·kg COD -1 ) and the endogenous coefficient of microorganisms were 0.25 and 0.04d -1 , which are similar to those of the conventional activated sludge process.

Wastewater treatment plants and release:The vase of Odin for emerging bacterial contaminants,resistance and determinant of environmental wellness

Municipal wastewater consists of a downstream collection of flushed sewage(without solid waste),other household runoffs,industrial runoffs,hospital runoffs and agricultural runoffs through an underground pipe before treatment.A runoff collection system called the wastewater treatment plant(WWTPs)treats such wastewater before release into environment following specific regulatory standards.This years-long practice has been improved upon by adding end-to-end pipe technologies with a view to enhancing the quality of effluent released.However,effluents released into the environment from design/application of WWTPs appear to contain emerging contaminants of both biotic and abiotic nature.The observation of chemical contaminants,antibiotic resistant bacteria(ARB),antibiotic resistant genes(ARGs)and diverse pathogenic bacteria genera in wastewater works release further affirm the abundance of such emerging contaminants.As a result,the government and water regulatory organizations in various part of the world are considering the removal of water reuse act from recycling policy/process.Current global debate is focused on questions about sustenance of any improved additional treatment level;effect of energy consumption by added treatment stage and its impact on the environmental wellness as contaminants borne wastewater is consistently released.Technological advancement/research suggests implementation of newer innovative infrastructural systems(NIIS)such as Mobbing Bed Biofilm Rector(MBBR),for wastewater effluent management which involve addition of newer wastewater treatment stages.This review addressed current pitfalls including wastewater microbiota of high epidemiological/public health relevance and affirms the need for such improvement which requires modification of ongoing institutional framework with a view to encourage implementation of NIIS for an improved effluent release.Exploiting the advances of microbial biofilming and the potentials of microbial biofueling as discussed in various section promises a future of robust environmental system,stable operational standard,release of quality effluent and sustainable management of wastewater works.Application of the aforementioned would enhance qualityWWTPs release and in-defacto reduces spread of ARB/ARGs as well as impacts both the environment wellness and public health.