Abstract
Surface water treatment plants are major energy consumers in all countries. In recent years, the increasing scarcity of fossil fuels and the growth in demand for energy resulting from the needs of development have prompted optimisation of the use of energy. SWTPs are responsible for the supply of treated water to consumers. Although a significant amount of the total energy produced is consumed by WTPs, the utilisation of this resource is variable, and sufficient amounts of it remain unutilised or wasted in the treatment process. Energy resources supplied to a WTP must be optimally allocated. At present, no mechanism exists to ensure this, and allocation is performed as and when it is needed, with no regulation or control. As a result, much energy is returned unutilised. This results in excess expenditures and affects carbon emissions from the plant because both too much and too little utilisation of energy in running water treatment equipment can result in the generation of greenhouse gases to the atmosphere. Unnecessary consumption of energy reduces its availability for other users. Thus, the economy, the environment, and social well-being are affected by the non-optimal utilisation of energy. This problem is common to all parts of the world but is especially acute in developing countries. Lack of intelligent allocation methods compromises the sustainability not only of the plant but also of the dependent population. Here, nature-based optimisation algorithms (OAs) and a modified analytical hierarchy process (mAHP), an objective multi-criteria decision-making method, were utilised to conduct intelligent, automatic allocation of energy among elements of wastewater treatment plants (WTPs). OAs are used to weight elements according to their relative capacity to ensure reliability and restrict risk to plants (resulting in a reliability–risk index); energy is allocated accordingly using mAHP. Tested at a working WTP in India, it minimised energy wastage down to 0.037% of total energy. This is the first attempt to combine mAHP and aggregated output from two OAs to optimise energy use in a WTP (based on the novel reliability–risk index). Our method builds on the concepts of multi-criteria decision making and metaheuristics optimisation algorithms to develop a new procedure for cognitive allocation of energy ensuring optimal performance while minimising the use of energy. Our decision support system can help maximise productivity, and safeguard sustainability, of plants and their stakeholders. However, time-dependent, nonlinear dynamics in continuously operating WTPs should be tested in future work.
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Abd-Elazim, S. M., & Ali, E. S. (2018). Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Computing and Applications,30(2), 607–616.
Ali, N., Othman, M. A., Husain, M., & Misran, M. H. (2014). A review of firefly algorithm. ARPN Journal of Engineering and Applied Sciences, 9(10), 1732–1736.
Asl, P. F., Monjezi, M., Hamidi, J. K., & Armaghani, D. J. (2018). Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Engineering with Computers,34(2), 241–251.
Bertanza, G., Canato, M., & Laera, G. (2018). Towards energy self-sufficiency and integral material recovery in waste water treatment plants: Assessment of upgrading options. Journal of Cleaner Production,170, 1206–1218.
Castellet-Viciano, L., Hernández-Chover, V., & Hernández-Sancho, F. (2018). Modelling the energy costs of the wastewater treatment process: The influence of the aging factor. Science of the Total Environment,625, 363–372.
Chae, K. J., & Kang, J. (2013). Estimating the energy independence of a municipal wastewater treatment plant incorporating green energy resources. Energy Conversion and Management,75, 664–672.
Chakraborty, P., Baeyens, E., & Khargonekar, P. P. (2018). Cost causation-based allocations of costs for market integration of renewable energy. IEEE Transactions on Power Systems,33(1), 70–83.
Copeland, C., & Carter, N. T. (2017). Energy-water Nexus: The water sector’s energy use. CRS Report, https://fas.org/sgp/crs/misc/R43200.pdf. Accessed 24 June 2018
D’Inverno, G., Carosi, L., Romano, G., & Guerrini, A. (2018). Water pollution in wastewater treatment plants: An efficiency analysis with undesirable output. European Journal of Operational Research,269(1), 24–34.
Das, S., Biswas, A., Dasgupta, S., & Abraham, A. (2009). Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. In Foundations of computational intelligence (Vol. 3, pp. 23–55). Berlin: Springer
Deaconu, S. I., Babău, R., Popa, G. N., & Gherman, P. L. (2018, January). Hydroelectric power plant with variable flow on drinking water adduction. In IOP conference series: Materials science and engineering (Vol. 294, No. 1, p. 012023). IOP Publishing.
Department of Information Engineering and Mathematical Sciences (DIEMS) (2018). University Of Siena,Notes on AHP, http://www.dii.unisi.it/~mocenni/Note_AHP.pdf. Accessed 26 June 2018
Dinar, A., Rosegrant, M. W., & Meinzen-Dick, R. S. (1997). Water allocation mechanisms: Principles and examples (No 1779). Washington: World Bank Publications.
Faradonbeh, R. S., Armaghani, D. J., Amnieh, H. B., & Mohamad, E. T. (2018). Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Computing and Applications,29(6), 269–281.
Forman, E. H., & Gass, S. I. (2001). The analytic hierarchy process—An exposition. Operations Research,49(4), 469–486.
Fu, Y., Zhang, J., Zhang, C., Zang, W., Guo, W., Qian, Z., et al. (2018). Payments for ecosystem services for watershed water resource allocations. Journal of Hydrology,556, 689–700.
Gémar, G., Gómez, T., Molinos-Senante, M., Caballero, R., & Sala-Garrido, R. (2018). Assessing changes in eco-productivity of wastewater treatment plants: The role of costs, pollutant removal efficiency, and greenhouse gas emissions. Environmental Impact Assessment Review,69, 24–31.
Goepel, K. D. (2017). Comparison of judgment scales of the analytical hierarchy process: A new approach. https://www.researchgate.net/profile/Klaus_Goepel/publication/317672155_Comparison_of_Judgment_Scales_of_the_Analytical_Hierarchy_Process_-_A_New_Approach/links/59662412aca27227d792b610/Comparison-of-Judgment-Scales-of-the-Analytical-Hierarchy-Process-A-New-Approach.pdf. Accessed 3 July 2018.
Golden, B. L., Wasil, E. A., & Harker, P. T. (1989). The analytic hierarchy process. Berlin: Applications and Studies.
Hilbig, J., & Rudolph, K. U. (2018). Sustainable water financing and lean cost approaches as essentials for integrated water resources management and water governance: Lessons learnt from the Southern African context. Water Science and Technology: Water Supply, ws2018099.
Hooper, V., & Lankford, B. (2018). Unintended water allocation. The Oxford Handbook of Water Politics and Policy, 248.
Huber, S. (2018). Energy consumption of wastewater treatment plants. http://www.huber.de/solutions/energy-efficiency/general/wastewater-treatment-plants.html. Accessed 24 June 2018.
Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert systems with applications, 38(11), 14336–14345.
Ishizaka, A., Balkenborg, D., & Kaplan, T. (2010). Influence of aggregation and measurement scale on ranking a compromise alternative in AHP. Journal of the Operational Research Society,62, 700–710.
Jiang, M. (2018). Alternative water governance mechanisms in China: Examination of current practices. In Towards tradable water rights (pp. 69–108). Cham: Springer
Jing, M., Jie, Y., Shou-yi, L., & Lu, W. (2018). Application of fuzzy analytic hierarchy process in the risk assessment of dangerous small-sized reservoirs. International Journal of Machine Learning and Cybernetics,9(1), 113–123.
Le, Q. H., Van Nguyen, T. H., Do, M. D., Le, T. C. H., Nguyen, H. K., & Luu, T. B. (2018). TXT-tool 1.084–3.1: Landslide susceptibility mapping at a regional scale in Vietnam. In Landslide dynamics: ISDR-ICL landslide interactive teaching tools (pp. 161–174). Springer, Cham.
Liu, H., Hu, M., & Zhang, X. (2018). Energy costs hosting model: The most suitable business model in the developing stage of energy performance contracting. Journal of Cleaner Production,172, 2553–2566.
Longo, S., d’Antoni, B. M., Bongards, M., Chaparro, A., Cronrath, A., Fatone, F., et al. (2016). Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement. Applied Energy,179, 1251–1268.
Matthews, H. L., & Nagel, R. (2018). In search of infrastructure optimization: A formalized, data-centric approach to long-term capital planning and asset management. Proceedings of the Water Environment Federation,2018(1), 982–1001.
Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L., & Weinhardt, C. (2018). Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Applied Energy,210, 870–880.
Moe, C. L., & Rheingans, R. D. (2006). Global challenges in water, sanitation and health. Journal of Water and Health,4(S1), 41–57.
Nanda, J., Mishra, S., & Saikia, L. C. (2009). Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Transactions on Power Systems,24(2), 602–609.
Nawaz, S., & Ali, Y. (2018). Factors affecting the performance of water treatment plants in Pakistan. Water Conservation Science and Engineering, 3(3), 191–203.
Palomero-González, J. A., & Hernández-Sancho, F. (2018). Improving drinking water treatment without tariff impact: The Spanish case study. Water Science and Technology: Water Supply,18(4), 1357–1364.
Panwar, A., Sharma, G., Nasiruddin, I., & Bansal, R. C. (2018). Frequency stabilization of hydro–hydro power system using hybrid bacteria foraging PSO with UPFC and HAE. Electric Power Systems Research,161, 74–85.
Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22(3), 52–67.
Racoviceanu, A. I., Karney, B. W., Kennedy, C. A., & Colombo, A. F. (2007). Life-cycle energy use and greenhouse gas emissions inventory for water treatment systems. Journal of Infrastructure Systems,13(4), 261–270.
Rodriguez-Roda, I. R., Sànchez-Marrè, M., Comas, J., Baeza, J., Colprim, J., Lafuente, J., et al. (2002). A hybrid supervisory system to support WWTP operation: Implementation and validation. Water Science and Technology,45(4–5), 289–297.
Roy, K., Mandal, K. K., Mandal, A. C., & Patra, S. N. (2018). Analysis of energy management in micro grid—A hybrid BFOA and ANN approach. Renewable and Sustainable Energy Reviews,82, 4296–4308.
Rufuss, D. D. W., Kumar, V. R., Suganthi, L., Iniyan, S., & Davies, P. A. (2018). Techno-economic analysis of solar stills using integrated fuzzy analytical hierarchy process and data envelopment analysis. Solar Energy,159, 820–833.
Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Singh, P., & Kansal, A. (2018). Energy and GHG accounting for wastewater infrastructure. Resources, Conservation and Recycling,128, 499–507.
Soares, R. B., Memelli, M. S., Roque, R. P., & Gonçalves, R. F. (2017). Comparative analysis of the energy consumption of different wastewater treatment plants. International Journal of Architecture, Arts and Applications,3(6), 79.
Spellman, F. R. (2013). Handbook of water and wastewater treatment plant operations. Boca Raton: CRC Press.
Stewardson, M. J., & Guarino, F. (2018). Basin-scale environmental water delivery in the Murray-Darling, Australia: A hydrological perspective. Freshwater Biology,63, 969–985.
Tao, X., & Chengwen, W. (2012). Energy consumption in wastewater treatment plants in China. http://www.researchgate.net/profile/Tao_Xie11/publication/266146909_Energy_Consumption_in_Wastewater_Treatment_plants_in_China/links/5428ce520cf238c6ea7cde91.pdf.
Taseli, B. K. (2018). Point source pollution and climate change impact from sequential batch reactor wastewater treatment plant. Global NEST Journal,20(1), 33–41.
Thomas, M. K. (2013). Survey of bacterial foraging optimization algorithm Riya.
Trapote, A., Albaladejo, A., & Simón, P. (2014). Energy consumption in an urban wastewater treatment plant: The case of Murcia Region (Spain). Civil Engineering and Environmental Systems,31(4), 304–310.
Venkata, S. G., Ganesh, V., & Madichetty, S. (2018). Application of bacteria foraging algorithm for modular multilevel converter-based microgrid with effect of wind power. Electrical Engineering,100, 1–14.
Venkatesh, G., & Brattebø, H. (2011). Energy consumption, costs and environmental impacts for urban water cycle services: Case study of Oslo (Norway). Energy,36(2), 792–800.
Venkitapathi, P. R. S., Annamalai, V. K., Singh, J., & Kumar, N. (2018). U.S. Patent Application No. 15/443,098.
Wakeel, M., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2016). Energy consumption for water use cycles in different countries: A review. Applied Energy,178, 868–885.
Wang, H., Yang, Y., Keller, A. A., Li, X., Feng, S., Dong, Y. N., et al. (2016). Comparative analysis of energy intensity and carbon emissions in wastewater treatment in USA, Germany, China and South Africa. Applied Energy,184, 873–881.
Wu, G. (2013). Application of adaptive PID controller based on bacterial foraging optimization algorithm. In 25th Chinese control and decision conference (CCDC) (pp. 2353–2356).
Yang, X. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Frome: Luniver Press.
Zamani, M. K. M., Musirin, I., Hassan, H., Shaaya, S. A., Sulaiman, S. I., Ghani, N. A. M., et al. (2018). Active and reactive power scheduling optimization using firefly algorithm to improve voltage stability under load demand variation. Indonesian Journal of Electrical Engineering and Computer Science,9(2), 365–372.
Zarghami, E., Azemati, H., Fatourehchi, D., & Karamloo, M. (2018). Customizing well-known sustainability assessment tools for Iranian residential buildings using Fuzzy Analytic Hierarchy Process. Building and Environment,128, 107–128.
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De, P., Majumder, M. Allocation of energy in surface water treatment plants for maximum energy conservation. Environ Dev Sustain 22, 3347–3370 (2020). https://doi.org/10.1007/s10668-019-00349-w
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DOI: https://doi.org/10.1007/s10668-019-00349-w