scholarly journals Performance comparison of SVM and ANN for aerobic granular sludge

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Nur Sakinah Ahmad Yasmin ◽  
Norhaliza Abdul Wahab ◽  
Aznah Nor Anuar ◽  
Mustafa Bob
Keyword(s):  
Wastewater Treatment ◽  
Batch Reactor ◽  
Treatment Plant ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Treatment Technology ◽  
Effluent Quality

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.

scholarly journals WASTEWATER CHARACTERIZATION AND SEQUENCING BATCH REACTOR OPERATION FOR AEROBIC GRANULAR SLUDGE CULTIVATION

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Hazlami Fikri Basri ◽  
Aznah Nor Anuar ◽  
Mohd Hakim Ab Halim
Keyword(s):  
Wastewater Treatment ◽  
Cycle Time ◽  
Wastewater Treatment Plant ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Treatment Plant ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Operation Cycle ◽  
Influent Wastewater

Studying the possibility of forming aerobic granules on real domestic sewage was a logical step in the scaling-up process and development of Aerobic Granular Sludge (AGS) technology. It was noted that influent wastewater composition and Sequencing Batch Reactor (SBR) operation cycle time are important factors that can influence the formation of AGS. Therefore, this study aims to determine the suitability of influent wastewater from Bunus Wastewater Treatment Plant (WWTP) for AGS cultivation and then propose a proper SBR operation cycle time. In this study, wastewater characterization was done for the influent of wastewater treatment plant located in Bunus, Kuala Lumpur. The result was then analysed and compared with previous research to determine the suitability of AGS cultivation. The information on SBR from previous studies were also gathered to propose SBR operation cycle time that suit the Bunus WWTP influent. The findings indicate that the wastewater can be characterized as low strength domestic wastewater with low organic and nutrients content. The values of related parameters in this study have shown that influent wastewater of Bunus WWTP is suitable for cultivating AGS. For the proposed SBR operation, the cycle time is 3h, which consist of 60 min (fill), 110 min (aerate), 5 min (settle), and 5 min (discharge), respectively.

scholarly journals Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study)

2021 ◽  
Vol 83 (7) ◽  
pp. 1633-1648
Author(s):  
Nasim Hejabi ◽  
Seyed Mahdi Saghebian ◽  
Mohammad Taghi Aalami ◽  
Vahid Nourani
Keyword(s):  
Wastewater Treatment ◽  
Uncertainty Analysis ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Ensemble Methods ◽  
Quality Parameters ◽  
Support Vector ◽  
Effluent Quality ◽  
Applied Model

Abstract Wastewater treatment plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, artificial intelligence approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz WWTP was assessed using two intelligence models, namely support Vector Machine (SVM) and artificial neural network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three time scales, daily, weekly, and monthly, were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, the Monte Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.

scholarly journals Improved support vector machine using optimization techniques for an aerobic granular sludge

2020 ◽  
Vol 9 (5) ◽  
pp. 1835-1843
Author(s):  
Nur Sakinah Ahmad Yasmin ◽  
Norhaliza Abdul Wahab ◽  
Aznah Nor Anuar
Keyword(s):  
Support Vector Machine ◽  
Batch Reactor ◽  
Granular Sludge ◽  
Optimization Techniques ◽  
Aerobic Granular Sludge ◽  
Support Vector ◽  
Good Prediction ◽  
Grid Search ◽  
Limited Data ◽  
Data Set

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

scholarly journals Estudio de adsorción, retención y difusión de pesticidas piretroides como la permetrina en suelo agrícola y su capacidad para actuar como filtro

2019 ◽  
pp. 27-37
Author(s):  
Karina Miranda-Hernandez ◽  
Joselyn Loa-Arjona ◽  
Rodolfo Guadalupe Alcantara-Rosales ◽  
Ignacio Lagunas-Bernabe
Keyword(s):  
Qualitative Research ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Research Methodology ◽  
Research Method ◽  
Treatment Plant ◽  
Effluent Quality ◽  
Aerobic Reactor ◽  
Wastewater Use

As a study of the proposal of a tertiary system for the improvement of the effluent quality of the wastewater treatment plant of the municipality of Jilotepec, the implementation of strategies has been sought, in order to achieve a qualitative research method, focusing on design and mathematical calculations, of wastewater use. To this end, the objectives are to carry out a research methodology proposed to analyze the arrangement of the aerators within the position of the Imhoff-type aerobic reactor, thereby achieving an acceptable sedimentation process, and subsequently to change the position of the aerators within of the aerobic reactor achieving a more complete cleaning to allocate the effluent for irrigation under the parameters of NOM-001-SEMARNAT-1996, looking for a better quality of effluent, with destination of urban use and as a calculation strategy it was proposed to implement a process of phytoremediation to avoid possible damage to the effluent

scholarly journals Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 85
Author(s):  
Barbara Wodecka ◽  
Jakub Drewnowski ◽  
Anita Białek ◽  
Ewa Łazuka ◽  
Joanna Szulżyk-Cieplak
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Ammonium Nitrogen ◽  
Support Vector ◽  
Process Conditions ◽  
Wastewater Quality

One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.

Engineering Design of CAST Process for a Small Town Wastewater Treatment

2013 ◽  
Vol 807-809 ◽  
pp. 1245-1250
Author(s):  
Jing Li Gu ◽  
Jun Hong ◽  
Ling Wan ◽  
Fan Zhang ◽  
Nan Nan Yuan
Keyword(s):  
Wastewater Treatment ◽  
Water Treatment ◽  
Municipal Wastewater ◽  
Small Town ◽  
Sewage Treatment ◽  
Industrial Effluent ◽  
Treatment Plant ◽  
Municipal Wastewater Treatment ◽  
Treatment Technology ◽  
Effluent Quality

A process of CAST, designing parameters and characteristics in the water treatment technology was introduced in this paper. The CAST was adopted to treat a scale of 3300m3/d of municipal wastewater and industrial effluent coming from a small town of Huojia county in Henan province. It is an innovative attempt to applied the CAST to a rural sewage treatment in the traditional water treatment field. What is more , after the chemical phosphorus removal and a sufficient reaction in the CAST tank, the effluent quality would ultimately meet level A while others could only meet level B in the state standard discharge standard of pollutants for municipal wastewater treatment plant (GB 18919-2002).

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