Predicting BOD of Greywater using Artificial Neural Networks

Predicting BOD of Greywater using Artificial Neural Networks

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Samir Sadik Shaikh, Rekha Shahapurkar
https://doi.org/10.14445/22315381/IJETT-V70I3P222

How to Cite?

Samir Sadik Shaikh, Rekha Shahapurkar, "Predicting BOD of Greywater using Artificial Neural Networks," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 195-200, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P222

Abstract
The performance of an artificial neural network (ANN) model in evaluating the quality of water measures, such as BOD for greywater, is investigated in this article. Representative criteria for greywater quality include chemical oxygen demand (COD) and biochemical oxygen demand (BOD), along with indirect organic matter indicators. Mean square error (MSE) measurements were used to assess the ANN models` performance. The ANN model outperformed with MLR model in terms of performance, according to the results. MSE = 0.1299. Comparative indices of the improved ANN using temperature (T), pH, total suspended solids (TSS), chemical oxygen demand (COD), also total solids (TS) as input variables for BOD prediction were MSE = 0.1299. The ANN model was shown to be effective in predicting greywater BOD and COD levels. Furthermore, sensitive research findings revealed that the pH parameter has a greater impact on BOD and COD when compared to other factors.

Keywords
ANN, BOD, Greywater, Keras model, MSE.

Reference
[1] Antonopoulos V.Z, Papamichail D.M, and Mitsiou K.A, Statistical and Trend Analysis of Water Quality and Quantity Data for the Strymon River in Greece, Hydrology and Earth System Sciences, ASMA 2012, River Water Quality Monitoring. 5(4) (2012) 679–692.
[2] Dhalla P, et al., Quick and Reliable Estimation of BOD Load of Industrial Beverage Wastewater by Developing BOD Biosensor, Sensors and Actuators B: Chemical. 133 (2008) 478–483.
[3] Boyacioglu H, Surface Water Quality Assessment Using Factor Analysis, Water South Africa. 32(3) (2006) 389–39.
[4] Li M, and Hassan R, Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools, Air Pollution. (2006) 195–220
[5] Khuan L.Y, Hamzah N, and Jailani R, Prediction of Water Quality Index (WQI) Based on Artificial Neural Network (ANN), In 2002 Student Conference on Research and Development Proceedings. Shah Alam, Malaysia. (2002) 157–161
[6] Cho K.H, et al., Prediction of Contamination Potential of Groundwater Arsenic in Cambodia, Laos, and Thailand using Artificial Neural Network, Water Research. 45 (2011) 5535–5544.
[7] Khan R.A, et al., Using Principal Component Scores and Artificial Neural Networks in Predicting Water Quality Index, Chemometrics in Practical Applications. (2001) 271–288.
[8] Faruk O.D, A Hybrid Neural Network and ARIMA Model for Water Quality Time Series Prediction. Engineering Applications of Artificial Intelligence. 23(4) (2010) 586–594.
[9] Han H.G, Chen Q.L, and Qiao J.F, An Efficient Self-Organizing RBF Neural Network for Water Quality Prediction, Neural Networks. 24 (7) (2011) 717–725.
[10] Xu L, and Liu S, Study of Short-Term Water Quality Prediction Model Based on Wavelet Neural Network. Mathematical and Comput Modelling. 58(3–4) (2013) 807–813.
[11] Rabiatul M.N, and Zainal A, Optimum Numbers of a Single Network for Combination in Multiple Neural Networks Modelling Approach for Modelling Nonlinear System Optimum, IIUM Eng Journal. 12(6) (2012) 45–58.
[12] (2010). Al-Beiruti S, Jordan: Greywater Treatment and Use for Poverty Reduction in Jordan (English) - Multiple-Use Water Services Group. [Online]. Available: http://www.musgroup.net/page/553.
[13] Al-Hamaiedeh H.D, and Bino M, Effect of Treated Greywater Reuse in Irrigation on Soil and Plants, Desalination. 256(1-3) (2010) 115-119. doi: 10.1016/j.desal.2010.02.004.
[14] Al-Jayyousi O.R, Greywater Use: Islamic Perspectives, In: Greywater Use in the Middle East, Mcilwaine and Redwood (Eds), IDRC. (10) (2010).
[15] X. Wang, L. Ma, and X. Wang, ?Apply Semi-Supervised Support Vector Regression for Remote Sensing Water Quality Retrieving, Int. Geosci. Remote Sens. Symp. (2010) 2757–2760.
[16] Shaikh SS, Shahapurkar R, Machine Learning-Based Quality Prediction of Greywater: A Review Information and Communication Technology for Competitive Strategies (ICTCS 2020), Lecture Notes in Networks and Systems, Springer, Singapore. 190 (2021) 337-347.
[17] Lin Zhao, Tianjiao Dai, Zhi Qiao, Peizhe Sun, Jianye Hao, Yongkui Yang, Application of Artificial Intelligence to Wastewater Treatment: A Bibliometric Analysis and Systematic Review of Technology, Economy, Management, and Wastewater Reuse, Process Safety and Environmental Protection. 133 (2020) 169-182.
[18] Shaikh SS, Shahapurkar R, Predicting COD and BOD Parameters of Greywater Using Multivariate Linear Regression, Advances in Parallel Computing, IOS Press. 39 (2021) 228-238.
[19] Güçlü D, Dursun ?, Artificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operation. Bioprocess Biosyst Eng, Springer, Singapore. (33) (2010) 1051–1058.
[20] Matheri AN, Ntuli F, Ngila JC, Seodigeng T, Zvinowanda C, Performance Prediction of Trace Metals and COD in Wastewater Treatment Using Artificial Neural Network. Computers & Chemical Engineering. 149 (2021) 107308.
[21] Palani S, Liong SY, Tkalich P. An ANN Application for Water Quality Forecasting. Mar Pollut Bull. 56(9) (2008) 1586-1597.
[22] S. Khadijah, H. Lokman, A. Mohd, S. Mohd, G. Rozaida, Water Quality Classification Using an Artificial Neural Network, Conference Series: Materials Science and Engineering, IOP Publishing. 601(1) (2019) 1-5.
[23] Singh T.N, Sinha S, Singh V.K, Prediction of Thermal Conductivity of Rock through Physico-Mechanical Properties, Building and Environment. 42 (2007) 146-155.