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Water and Energy Management in India pp 199–219Cite as

Impact Analysis of Water, Energy, and Climatic Variables on Performance of Surface Water Treatment Plants

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Abstract

In India, water shortages and poor water quality continue to be major challenges in both domestic and industrial sectors. The methods that aim to optimize Water Treatment Systems can go some way towards addressing these pressing challenges. The change in climate and large scale urbanization has imbibed vulnerabilities in water and energy use of Surface Water Treatment Plants (SWTP). As SWTPs take water from surface sources like rivers, lakes, etc., the change in the climate of a location can also impact the plant’s performance. The external factors like climate change and urbanization have created an imbalance in the hydrologic cycle, which in turn compromised both the quality and quantity of surface water used in the SWTP for processing and treatment. The imbalance has also influenced the time taken for the completion of the treatment operations in an SWTP. The overuse of the electrical machinery is another effect of this natural in-equilibrium. That is why; there is a necessity for the analysis of the impact of climatic changes, and because of the change, the impact of variation in water, and energy used by the SWTP and duty cycle of the treatment processes followed in an SWTP. As such, factors will directly influence the performance of SWTP and the quality of the treated water. The present study tries to apply cognitive and objective decision-making tools to develop a framework for identifying the most significant factor which will be most affected by the uncertainty and its impact on the performance efficiency of the SWTP. Data derived from water treatment plants in Tripura of North Eastern India has been utilized to demonstrate the reliability of the proposed method.

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References

  • Ahmetović, E., & Grossmann, I. E. (2011). Global superstructure optimization for the design of integrated process water networks. AIChE Journal, 57(2), 434–457.

    Article  Google Scholar 

  • Akbari, N., Irawan, C. A., Jones, D. F., & Menachof, D. (2017). A multi-criteria port suitability assessment for developments in the offshore wind industry. Renewable Energy, 102, 118–133.

    Article  Google Scholar 

  • Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali, A., & Jamali, A. J. E. C. (2007). Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Conversion and Management, 48(3), 1029–1041.

    Article  Google Scholar 

  • Bagatin, R., Klemeš, J. J., Reverberi, A. P., & Huisingh, D. (2014). Conservation and improvements in water resource management: A global challenge. Journal of Cleaner Production, 77, 1–9.

    Article  Google Scholar 

  • Cambron, K. E., & Evans, G. W. (1991). Layout design using the analytic hierarchy process. Computers & IE, 20, 221–229.

    Google Scholar 

  • Castro, P. M., & Teles, J. P. (2013). Comparison of global optimization algorithms for the design of water-using networks. Computers & Chemical Engineering, 52, 249–261.

    Article  Google Scholar 

  • Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., & Zhang, Y. (2020). Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171, 115454.

    Article  Google Scholar 

  • Debnath, A., Majumder, M., & Pal, M. (2015). A cognitive approach in selection of source for water treatment plant based on climatic impact. Water Resources Management, 29(6), 1907–1919.

    Article  Google Scholar 

  • De, P., & Majumder, M. (2019). Allocation of energy in surface water treatment plants for maximum energy conservation (pp. 1–24). Development and Sustainability: Environment.

    Google Scholar 

  • Deng, W., Lai, Z., Hu, M., Han, X., & Su, Y. (2020). Effects of frequency and duty cycle of pulsating direct current on the electro-dewatering performance of sewage sludge. Chemosphere, 243, 125372.

    Article  Google Scholar 

  • Dong, H. G., Lin, C. Y., & Chang, C. T. (2008). Simultaneous optimization approach for integrated water-allocation and heat-exchange networks. Chemical Engineering Science, 63(14), 3664–3678.

    Article  Google Scholar 

  • Dorussen, H. L., & Wassenberg, W. B. A. (1997). Feasibility of treatment of low polluted wastewater in municipal waste water treatment plants. Water Science and Technology, 35, 73–78.

    Article  Google Scholar 

  • Dutta, N., Ghosh, A., Debnath, B., & Ghosh, S. K. (2020). Climate change in hilly regions of India: Issues and challenges in waste management. In Sustainable Waste Management: Policies and Case Studies (pp. 657–669). Springer, Singapore.

    Google Scholar 

  • Fant, C., et al. (2017). Climate change impacts on US water quality using two models: HAWQS and US basins. Water, 9(2), 118.

    Article  Google Scholar 

  • Galán, B., & Grossmann, I. E. (2011). Optimal design of real world industrial wastewater treatment networks. In Computer Aided Chemical Engineering (Vol. 29, pp. 1251–1255). Elsevier.

    Google Scholar 

  • Ghodsi, H., & Khanjani, M. J. (2020). Application of improved GMDH models to predict local scour depth at complex bridge piers. Civil Engineering Journal, 6(1), 69–84.

    Article  Google Scholar 

  • Hernández, C. T., Marins, F. A. S., Rocha, P., & Duran, J. A. R. (2010). Using AHP and ANP to evaluate the relation between reverse logistics and corporate performance in Brazilian industry. Brazilian Journal of Operations & Production Management, 7(2), 47–62.

    Google Scholar 

  • Ibrić, N., Ahmetović, E., & Kravanja, Z. (2014). Two-step mathematical programming synthesis of pinched and threshold heat-integrated water networks. Journal of Cleaner Production, 77, 116–139.

    Article  Google Scholar 

  • Ivakhnenko, A. G., & Ivakhnenko, G. A. (1995). The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 5, 527–535.

    Google Scholar 

  • Jiang, B., & Guo, H. (2013). Permutation invariant polynomial neural network approach to fitting potential energy surfaces. The Journal of Chemical Physics, 139(5), 054112.

    Article  Google Scholar 

  • Khan, Z. I., et al. (2017). Health risk assessment of heavy metals in wheat using different water qualities: Implication for human health. Environmental Science and Pollution Research, 24(1), 947–955.

    Article  Google Scholar 

  • Sundara, K. K., et al. (2010). Performance evaluation of waste water treatment plant. International Journal of Engineering Science and Technology, 2(12), 7785–7796.

    Google Scholar 

  • Li, L., Rong, S., Wang, R., & Yu, S. (2020). Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review. Chemical Engineering Journal, 405, 126673.

    Article  Google Scholar 

  • Majumder, P., Majumder, M., & Saha, A. K. (2020). Real-time monitoring of power production in modular hydropower plant: Most significant parameter approach. Environment, Development and Sustainability, 22(5), 4025–4042.

    Article  Google Scholar 

  • Mohammadi, D., Mikaeil, R., & Abdollahi-Sharif, J. (2020). Implementation of an optimized binary classification by GMDH-type neural network algorithm for predicting the blast produced ground vibration. Expert Systems, 37(5), e12563.

    Article  Google Scholar 

  • Oh, S. K., & Pedrycz, W. (2002). The design of self-organizing polynomial neural networks. Information Sciences, 141(3–4), 237–258.

    Article  Google Scholar 

  • Oh, S. K., Pedrycz, W., & Park, B. J. (2003). Polynomial neural networks architecture: Analysis and design. Computers & Electrical Engineering, 29(6), 703–725.

    Article  Google Scholar 

  • Özdemir, A., Özkan, A., Günkaya, Z., & Banar, M. (2020). Decision-making for the selection of different leachate treatment/management methods: The ANP and PROMETHEE approaches. Environmental Science and Pollution Research, 1–12.

    Google Scholar 

  • Pusat, S., & Akkaya, A. V. (2020). Explicit equation derivation for predicting coal moisture content in convective drying process by GMDH-type neural network. International Journal of Coal Preparation and Utilization, 1–13.

    Google Scholar 

  • Raseman, W. J., et al. (2017). Emerging Investigators series: A critical review of decision support systems for water treatment: Making the case for incorporating climate change and climate extremes. Environmental Science: Water Research & Technology, 3(1), 18–36.

    Google Scholar 

  • Rodríguez, N. H., Ramírez, S. M., Varela, M. B., Guillem, M., Puig, J., Larrotcha, E., & Flores, J. (2010). Re-use of drinking water treatment plant (DWTP) sludge: Characterization and technological behaviour of cement mortars with atomized sludge additions. Cement and Concrete Research, 40(5), 778–786.

    Article  Google Scholar 

  • Rojas-Torres, M. G., Ponce-Ortega, J. M., Serna-González, M., Nápoles-Rivera, F., & El-Halwagi, M. M. (2013). Synthesis of water networks involving temperature-based property operators and thermal effects. Industrial & Engineering Chemistry Research, 52(1), 442–461.

    Article  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. McGraw Hill International (1980).

    Google Scholar 

  • Saaty, T. L. (2005). Theory and applications of the analytic network process, Pittsburgh. PA: RWS Publications.

    Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process: Economic, political, social and technological applications with benefits, opportunities, costs and risks. New York: Springer.

    MATH  Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (2013). The analytic network process. In Decision Making with the Analytic Network Process (pp. 1–40). Springer, Boston, MA.

    Google Scholar 

  • Skiborowski, M., Mhamdi, A., Kraemer, K., & Marquardt, W. (2012). Model-based structural optimization of seawater desalination plants. Desalination, 292, 30–44.

    Article  Google Scholar 

  • Tsiakis, P., & Papageorgiou, L. G. (2005). Optimal design of an electrodialysis brackish water desalination plant. Desalination, 173(2), 173–186.

    Article  Google Scholar 

  • Vasquez, V. M. (2017). Critical literacy. In Oxford Research Encyclopedia of Education.

    Google Scholar 

  • Xu, L., Wang, X., Bai, L., Xiao, J., Liu, Q., Chen, E., & Luo, B. (2020). Probabilistic SVM classifier ensemble selection based on GMDH-type neural network. Pattern Recognition, 106, 107373.

    Article  Google Scholar 

  • Yan, Y., Xia, B. Y., Zhao, B., & Wang, X. (2016). A review on noble-metal-free bifunctional heterogeneous catalysts for overall electrochemical water splitting. Journal of Materials Chemistry A, 4(45), 17587–17603.

    Article  Google Scholar 

  • Yang, L., & Grossmann, I. E. (2013). Water targeting models for simultaneous flowsheet optimization. Industrial & Engineering Chemistry Research, 52(9), 3209–3224.

    Article  Google Scholar 

  • Zolfaghary, P., Zakerinia, M., & Kazemi, H. A. Model for the use of urban treated wastewater in agriculture using multiple criteria decision making (MCDM) and geographic information system (GIS). Agricultural Water Management, 243, 106490.

    Google Scholar 

  • Kumar, N., & Sinha, D. K. (2010). An approach to river water quality management through correlation study among various water quality parameters. Int J Environ Sci, 1(2), 253-259

    Google Scholar 

  • Pham, B. T., Singhº, S. K., & Ly, H. B. (2020). Using Artificial Neural Network (ANN) for prediction of soil. Vietnam Journal of Earth Sciences, 42(4), 311-319.

    Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology, Agartala, Tripura, India

    Sudipa Choudhury & Apu K. Saha

Authors
  1. Sudipa Choudhury
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  2. Apu K. Saha
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Correspondence to Sudipa Choudhury .

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Editors and Affiliations

  1. Hydro-Informatics Engineering (under Department of Civil Engineering), National Institute of Technology Agartala, Jirania, Agartala, India

    Mrinmoy Majumder

  2. Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Ganesh D. Kale

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Choudhury, S., Saha, A.K. (2021). Impact Analysis of Water, Energy, and Climatic Variables on Performance of Surface Water Treatment Plants. In: Majumder, M., Kale, G.D. (eds) Water and Energy Management in India. Springer, Cham. https://doi.org/10.1007/978-3-030-66683-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-66683-5_10

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  • Print ISBN: 978-3-030-66682-8

  • Online ISBN: 978-3-030-66683-5

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