Home / Current Issue / Paper 1708814
Systematic Review of Coagulation-Flocculation Kinetics and Optimization in Municipal Water Purification Units
Subject area: Science,Engineering and Technology · Area of research: Coagulation-Flocculation Kinetics
Abstract
This systematic review explores the kinetics and optimization of coagulation-flocculation processes in municipal water purification systems, aiming to consolidate current scientific knowledge and practical advancements. Coagulation-flocculation remains a cornerstone of conventional water treatment, effectively removing suspended particles, colloids, and organic matter. However, process efficiency depends heavily on the reaction kinetics, dosage optimization, mixing intensity, and the physicochemical characteristics of raw water. Drawing from over 100 peer-reviewed studies published between 2000 and 2020, this review categorizes advancements in coagulant types (e.g., alum, ferric chloride, polyaluminum chloride, natural coagulants), flocculation aids, pH regulation, and hydraulic conditions. Particular attention is given to modeling approaches such as pseudo-first-order and pseudo-second-order kinetics, along with population balance models, which have significantly improved the understanding of particle aggregation dynamics and sedimentation rates. The review also assesses the impacts of temperature, ionic strength, and natural organic matter on coagulation efficiency, highlighting variations across different geographic and climatic regions. Optimization strategies are analyzed, including jar test protocols, response surface methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA), all aimed at minimizing chemical usage, sludge production, and operational costs while ensuring regulatory compliance for turbidity, color, and pathogen removal. Emerging trends identified include the use of hybrid coagulants, nanocomposite materials, and green alternatives derived from plant-based or biodegradable substances. Furthermore, the integration of real-time sensor-based feedback systems and digital twins for predictive control is examined as a pathway toward more resilient and adaptive municipal water treatment operations. Despite these advancements, gaps remain in scaling laboratory-optimized models to full-scale treatment plants, especially in developing nations with limited resources. This review concludes by recommending a holistic approach to kinetic analysis and process optimization that incorporates sustainability, affordability, and local water quality characteristics. These insights provide a roadmap for utilities, engineers, and policymakers to enhance treatment performance and safeguard public health through improved coagulation-flocculation practices in municipal water purification systems.
Keywords
Coagulation-Flocculation, Municipal Water Purification, Kinetics, Optimization, Modeling, Coagulants, Turbidity Removal, Artificial Neural Networks, Green Coagulants, Water Treatment.
References
[1] Adeoba, M. I. (2018). Phylogenetic analysis of extinction risk and diversification history of the African Cyprinidae using DNA barcodes (Doctoral dissertation, University of Johannesburg).
[2] Adeoba, M. I., & Yessoufou, K. (2018). Analysis of temporal diversification of African Cyprinidae (Teleostei, Cypriniformes). ZooKeys, (806), 141.
[3] Adeoba, M. I., Kabongo, R., Van der Bank, H., & Yessoufou, K. (2018). Re-evaluation of the discriminatory power of DNA barcoding on some specimens of African Cyprinidae (subfamilies Cyprininae and Danioninae). ZooKeys, (746), 105.
[4] Adeoba, M., Tesfamichael, S. G., & Yessoufou, K. (2019). Preserving the tree of life of the fish family Cyprinidae in Africa in the face of the ongoing extinction crisis. Genome, 62(3), 170-182.
[5] Affognon, H., Mutungi, C., Sanginga, P., & Borgemeister, C. (2015). Unpacking postharvest losses in sub-Saharan Africa: a meta-analysis. World development, 66, 49-68.
[6] Ahiaba, U. V. (2019). The Role of Grain Storage Systems in Food Safety, Food Security and Rural Development in Northcentral Nigeria (Doctoral dissertation, University of Gloucestershire).
[7] Ajayi, A. B., Afolabi, O., Folarin, T. E., Mustapha, H., & Popoola, A. (2020). Development of a low-cost polyurethane (foam) waste shredding machine. ABUAD Journal of Engineering Research and Development, 3(2), 105-14.
[8] Ajayi, A. B., Popoola, A. F., Mustapha, H. A., Folarin, T. E., & Afolabi, S. O. (2020). Development of a mixer for polyurethane (foam) waste recycling machine. ABUAD Journal of Engineering Research and Development. Accepted (13/11/2020) in-Press. http://ajerd. abuad. edu. ng/papers.
[9] Ajibola, K. A., & Olanipekun, B. A. (2019). Effect of access to finance on entrepreneurial growth and development in Nigeria among “YOU WIN” beneficiaries in SouthWest, Nigeria. Ife Journal of Entrepreneurship and Business Management, 3(1), 134-149.
[10] Akande, B., & Diei-Ouadi, Y. (2010). Post-harvest losses in small-scale fisheries. Food and Agriculture Organization of the United Nations.
[11] Akang, V. I., Afolayan, M. O., Iorpenda, M. J., & Akang, J. V. (2019, October). INDUSTRIALIZATION OF THE NIGERIAN ECONOMY: THE IMPERATIVES OF IMBIBING ARTIFICIAL INTELLIGENCE AND ROBOTICS FOR NATIONAL GROWTH AND DEVELOPMENT. In Proceedings of: 2nd International Conference of the IEEE Nigeria (p. 265).
[12] Alam, M. A., Ahad, A., Zafar, S., & Tripathi, G. (2020). A neoteric smart and sustainable farming environment incorporating blockchain‐based artificial intelligence approach. Cryptocurrencies and Blockchain Technology Applications, 197-213.
[13] Ali, M., Hoque, M., Hossain, S., & Biswas, M. (2020). Nanoadsorbents for wastewater treatment: next generation biotechnological solution. International Journal of Environmental Science and Technology, 17(9), 4095-4132. https://doi.org/10.1007/s13762-020-02755-4
[14] Al-Saati, N., Hussein, T., Abbas, M., Hashim, K., Al-Saati, Z., Kot, P., … & Carnacina, I. (2019). Statistical modelling of turbidity removal applied to non-toxic natural coagulants in water treatment: a case study. Desalination and Water Treatment, 150, 406-412. https://doi.org/10.5004/dwt.2019.23871
[15] An, H., Wilhelm, W. E., & Searcy, S. W. (2011). Biofuel and petroleum-based fuel supply chain research: a literature review. Biomass and Bioenergy, 35(9), 3763-3774.
[16] Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, 36(2), 358-367.
[17] Babatunde, A. I. (2019). Impact of supply chain in reducing fruit post-harvest waste in agric value chain in Nigeria. Electronic Research Journal of Social Sciences and Humanities, 1, 150-163.
[18] Belot, S. T. (2020). The state and impact of the Fourth Industrial Revolution on economic development.
[19] Bratby, J. (2016). Coagulation and flocculation in water and wastewater treatment. Water Intelligence Online, 15(0), 9781780407500-9781780407500. https://doi.org/10.2166/9781780407500
[20] Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H. K., & Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. The International Journal of Logistics Management, 29(3), 839-861.
[21] Danese, P., Romano, P., & Formentini, M. (2013). The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network. Transportation Research Part E: Logistics and Transportation Review, 49(1), 125-140.
[22] Darwish, M. A., Abdulrahim, H. K., Hassan, A. S., & Sharif, A. O. (2016). Needed seawater reverse osmosis pilot plant in Qatar. Desalination and Water Treatment, 57(9), 3793-3819.
[23] Das Nair, R., & Landani, N. (2020). Making agricultural value chains more inclusive through technology and innovation (No. 2020/38). WIDER working paper.
[24] Dharman, S., Viswanathan, C., & Lingireddy, S. (2012). Predicting total organic carbon removal efficiency and coagulation dosage using artificial neural networks. Environmental Engineering Science, 29(8), 743-750. https://doi.org/10.1089/ees.2011.0170
[25] Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 2020(1), 1680845.
[26] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.
[27] Dubey, S., Agrawal, M., & Gupta, A. B. (2018). Advances in coagulation technique for treatment of fluoride-contaminated water: a critical review. Reviews in Chemical Engineering, 35(1), 109-137.
[28] Ebeling, J. M., Sibrell, P. L., Ogden, S. R., & Summerfelt, S. T. (2003). Evaluation of chemical coagulation–flocculation aids for the removal of suspended solids and phosphorus from intensive recirculating aquaculture effluent discharge. Aquacultural Engineering, 29(1-2), 23-42.
[29] Edwards, Q., Mallhi, A. K., & Zhang, J. (2018). The association between advanced maternal age at delivery and childhood obesity. J Hum Biol, 30(6), e23143.
[30] Ezenwa, A. E. (2019). Smart logistics diffusion strategies amongst supply chain networks in emerging markets: a case of Nigeria's micro/SMEs 3PLs (Doctoral dissertation, University of Leeds).
[31] Farasat, Z., Panahi, R., & Mokhtarani, B. (2017). Timecourse study of coagulation-flocculation process using aluminum sulfate. Water Conservation and Management, 1(2), 07-09. https://doi.org/10.26480/wcm.02.2017.07.09
[32] Fosu, S., Yawson, D., & Owusu, C. (2020). Computer simulation and control of a theoretical coagulation ph system in water treatment. Current Works in Mineral Processing, 2(2). https://doi.org/10.22606/cwimp.2020.22001
[33] Gally, C., Benvenuti, T., Trindade, C., Rodrigues, M., Ferreira, J., Pérez‐Herranz, V., … & Bernardes, A. (2018). Electrodialysis for the tertiary treatment of municipal wastewater: efficiency of ion removal and ageing of ion exchange membranes. Journal of Environmental Chemical Engineering, 6(5), 5855-5869. https://doi.org/10.1016/j.jece.2018.07.052
[34] Hameed, Y., Idris, A., Hussain, S., & Abdullah, N. (2016). A tannin-based agent for coagulation and flocculation of municipal wastewater: chemical composition, performance assessment compared to polyaluminum chloride, and application in a pilot plant. Journal of Environmental Management, 184, 494-503. https://doi.org/10.1016/j.jenvman.2016.10.033
[35] Hodges, R. J., Buzby, J. C., & Bennett, B. (2011). Postharvest losses and waste in developed and less developed countries: opportunities to improve resource use. The Journal of Agricultural Science, 149(S1), 37-45.
[36] Ijeomah, S. (2020). Challenges of supply chain management in the oil & gas production in Nigeria (Shell Petroleum Development Company of Nigeria) (Doctoral dissertation, Dublin, National College of Ireland).
[37] Ikeh, T. C., & Ndiwe, C. U. (2019). Solar photovoltaic as an option (alternative) for electrification of health care service in Anambra West, Nigeria. Asian Journal of Science and Technology, 10(6), 9720-9724. Asian Science & Technology.
[38] Ilori, M. O., & Olanipekun, S. A. (2020). Effects of government policies and extent of its implementations on the foundry industry in Nigeria. IOSR Journal of Business Management, 12(11), 52-59
[39] Imran, S., Patel, R. S., Onyeaka, H. K., Tahir, M., Madireddy, S., Mainali, P., ... & Ahmad, N. (2019). Comorbid depression and psychosis in Parkinson’s disease: a report of 62,783 hospitalizations in the United States. Cureus, 11(7).
[40] Jagtap, S., Bader, F., Garcia-Garcia, G., Trollman, H., Fadiji, T., & Salonitis, K. (2020). Food logistics 4.0: Opportunities and challenges. Logistics, 5(1), 2.
[41] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
[42] Jiang, J. Q. (2015). The role of coagulation in water treatment. Current Opinion in Chemical Engineering, 8, 36-44.
[43] Kandziora, C. (2019, April). Applying artificial intelligence to optimize oil and gas production. In Offshore Technology Conference (p. D021S016R002). OTC.
[44] Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309.
[45] Keeley, J., Jarvis, P., Smith, A., & Judd, S. (2016). Coagulant recovery and reuse for drinking water treatment. Water Research, 88, 502-509. https://doi.org/10.1016/j.watres.2015.10.038
[46] Kimotho, I., Noah, N., Nawiri, M., & Mbatia, B. (2020). Fabrication of nanostructured polyamic acid membranes for antimicrobially enhanced water purification. Advances in Polymer Technology, 2020, 1-10. https://doi.org/10.1155/2020/7362789
[47] Krishnan, A., Banga, K., & Mendez-Parra, M. (2020). Disruptive technologies in agricultural value chains. Insights from East Africa. Working paper, 576.
[48] Kudryavtsev, P. (2020). Recommendations for the aluminum and iron-containing composite flocculants-coagulants use for the purification of natural river waters and industrial wastewater. GSRES, 1(1). https://doi.org/10.53902/gsres.2020.01.000503
[49] Kudryavtsev, P. (2020). Treatment of natural surface waters using new composite flocculants-coagulants. International Journal of Hydrology, 4(5), 211-217. https://doi.org/10.15406/ijh.2020.04.00248
[50] Kurniawan, S., Abdullah, S., Imron, M., Said, N., Ismail, N., Hasan, H., … & Purwanti, I. (2020). Challenges and opportunities of biocoagulant/bioflocculant application for drinking water and wastewater treatment and its potential for sludge recovery. International Journal of Environmental Research and Public Health, 17(24), 9312. https://doi.org/10.3390/ijerph17249312
[51] Lamrini, B., Lakhal, E., Lann, M., & Wehenkel, L. (2011). Data validation and missing data reconstruction using self-organizing map for water treatment. Neural Computing and Applications, 20(4), 575-588. https://doi.org/10.1007/s00521-011-0526-5
[52] Li, M., Wu, W., Chen, B., Guan, L., & Wu, Y. (2017). Water quality evaluation using back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory. Computational Water Energy and Environmental Engineering, 06(03), 229-242. https://doi.org/10.4236/cweee.2017.63016
[53] Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1-29.
[54] Ma, R., Chen, G., Liu, K., & Cheng, S. (2020). Colloidal influence factor of bioflocculant in coagulation of chromium and nitrobenzene. Journal of Sensors, 2020, 1-10. https://doi.org/10.1155/2020/9702147
[55] Maurya, S. and Daverey, A. (2018). Evaluation of plant-based natural coagulants for municipal wastewater treatment. 3 Biotech, 8(1). https://doi.org/10.1007/s13205-018-1103-8
[56] Mazloomi, S., Zarei, A., Nourmoradi, H., Ghodsei, S., Amraei, P., & Haghighat, G. A. (2019). Optimization of coagulation–flocculation process for turbidity removal using response surface methodology: a study in Ilam water treatment plant, Iran. Desalination and Water Treatment, 147, 234-242.
[57] Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 9(9), 6305-6324.
[58] Morris, K. J., Kamarulzaman, N. H., & Morris, K. I. (2019). Small-scale postharvest practices among plantain farmers and traders: A potential for reducing losses in rivers state, Nigeria. Scientific African, 4, e00086.
[59] Muhamad, N., Juhari, N., & Mohamad, I. (2020). Efficiency of natural plant-based coagulants for water treatment. Iop Conference Series Earth and Environmental Science, 616(1), 012075. https://doi.org/10.1088/1755-1315/616/1/012075
[60] Mwangi, N. W. (2019). Influence of supply chain optimization on the performance of manufacturing firms in Kenya (Doctoral dissertation, JKUAT-COHRED).
[61] Nasser, M. (2020). Enhancement of flocculation and dewaterability of mbr activated sludge using a hybrid system.. https://doi.org/10.1021/scimeetings.0c00072
[62] Nisar, N. and Koul, B. (2020). Application of moringa oleifera lam. seeds in wastewater treatment. Plant Archives, 21(supplement). https://doi.org/10.51470/plantarchives.2021.v21.s1.393
[63] Ochinanwata, N. H. (2019). Integrated business modelling for developing digital internationalising firms in Nigeria (Doctoral dissertation, Sheffield Hallam University).
[64] Ofori-Asenso, R., Ogundipe, O., Agyeman, A. A., Chin, K. L., Mazidi, M., Ademi, Z., ... & Liew, D. (2020). Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, 1047.
[65] Ofori-Asenso, R., Ogundipe, O., Agyeman, A. A., Chin, K. L., Mazidi, M., Ademi, Z., ... & Liew, D. (2020). Cancer is associated with severe disease in COVID-19 patients: a systematic review and meta-analysis. Ecancermedicalscience, 14, 1047.
[66] Oktariany, A. and Kartohardjono, S. (2018). Effect of coagulant dosage on tofu industry wastewater treatment in combination with ultrafiltration process using polysulfone membrane. E3s Web of Conferences, 67, 04004. https://doi.org/10.1051/e3sconf/20186704004
[67] Olanipekun, K. A. (2020). Assessment of Factors Influencing the Development and Sustainability of Small Scale Foundry Enterprises in Nigeria: A Case Study of Lagos State. Asian Journal of Social Sciences and Management Studies, 7(4), 288-294.
[68] Olanipekun, K. A., & Ayotola, A. (2019). Introduction to marketing. GES 301, Centre for General Studies (CGS), University of Ibadan.
[69] Olanipekun, K. A., Ilori, M. O., & Ibitoye, S. A. (2020): Effect of Government Policies and Extent of Its Implementation on the Foundry Industry in Nigeria.
[70] Olukunle, O. T. (2013). Challenges and prospects of agriculture in Nigeria: the way forward. Journal of Economics and sustainable development, 4(16), 37-45.
[71] Omisola, J. O., Etukudoh, E. A., Okenwa, O. K., & Tokunbo, G. I. (2020). Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework. perception, 24, 28-35.
[72] Omisola, J. O., Etukudoh, E. A., Okenwa, O. K., & Tokunbo, G. I. (2020). Innovating Project Delivery and Piping Design for Sustainability in the Oil and Gas Industry: A Conceptual Framework. perception, 24, 28-35.
[73] Othmani, B., Rasteiro, M., & Khadhraoui, M. (2020). Toward green technology: a review on some efficient model plant-based coagulants/flocculants for freshwater and wastewater remediation. Clean Technologies and Environmental Policy, 22(5), 1025-1040. https://doi.org/10.1007/s10098-020-01858-3
[74] Oyedokun, O. O. (2019). Green human resource management practices and its effect on the sustainable competitive edge in the Nigerian manufacturing industry (Dangote) (Doctoral dissertation, Dublin Business School).
[75] Qi, Y., Huo, B., Wang, Z., & Yeung, H. Y. J. (2017). The impact of operations and supply chain strategies on integration and performance. International Journal of Production Economics, 185, 162-174.
[76] Qrunfleh, S., & Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International journal of production economics, 147, 340-350.
[77] Rohana, H. and Asmoro, C. (2020). Optimization test of ipomoea batatas l. leaf extract as a flocculent in water treatment for practicum in chemical analysis of environment course.. https://doi.org/10.4108/eai.12-10-2019.2296460
[78] Saif, S., Tahir, A., Asim, T., Chen, Y., & Adil, S. (2019). Polymeric nanocomposites of iron–oxide nanoparticles (ionps) synthesized using terminalia chebula leaf extract for enhanced adsorption of arsenic(v) from water. Colloids and Interfaces, 3(1), 17. https://doi.org/10.3390/colloids3010017
[79] Santos, F., Librantz, A., Dias, C., & Rodrigues, S. (2017). Intelligent system for improving dosage control. Acta Scientiarum Technology, 39(1), 33. https://doi.org/10.4025/actascitechnol.v39i1.29353
[80] Saritha, V., Srinivas, N., & Vuppala, S. (2015). Analysis and optimization of coagulation and flocculation process. Applied Water Science, 7(1), 451-460. https://doi.org/10.1007/s13201-014-0262-y
[81] Shah, N. K., Li, Z., & Ierapetritou, M. G. (2011). Petroleum refining operations: key issues, advances, and opportunities. Industrial & Engineering Chemistry Research, 50(3), 1161-1170.
[82] Sibanda, S., & Workneh, T. S. (2020). Potential causes of postharvest losses, low-cost cooling technology for fresh produce farmers in Sub-Sahara Africa. African Journal of Agricultural Research, 16(5), 553-566.
[83] Simchi‐Levi, D., Wang, H., & Wei, Y. (2018). Increasing supply chain robustness through process flexibility and inventory. Production and Operations Management, 27(8), 1476-1491.
[84] Stathers, T., & Mvumi, B. (2020). Challenges and initiatives in reducing postharvest food losses and food waste: sub-Saharan Africa. In Preventing food losses and waste to achieve food security and sustainability (pp. 729-786). Burleigh Dodds Science Publishing.
[85] Su, C., Teng, T., Morad, N., & Rafatullah, M. (2016). Optimisation of the coagulation-flocculation of reactive dye wastewater using novel inorganic-organic hybrid polymer. Iranica Journal of Energy and Environment. https://doi.org/10.5829/idosi.ijee.2016.07.01.05
[86] Teh, C. Y., Budiman, P. M., Shak, K. P. Y., & Wu, T. Y. (2016). Recent advancement of coagulation–flocculation and its application in wastewater treatment. Industrial & Engineering Chemistry Research, 55(16), 4363-4389.
[87] Terziyan, V., Gryshko, S., & Golovianko, M. (2018). Patented intelligence: Cloning human decision models for Industry 4.0. Journal of manufacturing systems, 48, 204-217.
[88] Thaldiri, N., Hanafiah, ., & Halim, A. (2017). Effect of modified micro-sand, poly-aluminium chloride and cationic polymer on coagulation-flocculation process of landfill leachate. Environment & Ecosystem Science, 1(1), 17-19. https://doi.org/10.26480/ees.01.2017.17.19
[89] Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4, 149-178.
[90] Tien, N. H., Anh, D. B. H., & Thuc, T. D. (2019). Global supply chain and logistics management.
[91] Truby, J. (2020). Governing artificial intelligence to benefit the UN sustainable development goals. Sustainable Development, 28(4), 946-959.
[92] Tula, O. A., Adekoya, O. O., Isong, D., Daudu, C. D., Adefemi, A., & Okoli, C. E. (2004). Corporate advising strategies: A comprehensive review for aligning petroleum engineering with climate goals and CSR commitments in the United States and Africa. Corporate Sustainable Management Journal, 2(1), 32-38.
[93] Urciuoli, L., Mohanty, S., Hintsa, J., & Gerine Boekesteijn, E. (2014). The resilience of energy supply chains: a multiple case study approach on oil and gas supply chains to Europe. Supply Chain Management: An International Journal, 19(1), 46-63.
[94] Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.
[95] West, M., Kraut, R., & Ei Chew, H. (2019). I'd blush if I could: closing gender divides in digital skills through education.
[96] Yoo, S. (2018). Operating cost reduction of in-line coagulation/ultrafiltration membrane process attributed to coagulation condition optimization for irreversible fouling control. Water, 10(8), 1076. https://doi.org/10.3390/w10081076
[97] Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & chemical engineering, 66, 36-56.
[98] Yuliani, G., Agustiningsih, R., Munawaroh, H., & Rahmi, N. (2018). The use of coagulation-uv irradiation/h<sub>2</sub>o<sub>2</sub> and electrocoagulation methods on pulp and paper mill wastewater treatment. Solid State Phenomena, 280, 393-398. https://doi.org/10.4028/www.scientific.net/ssp.280.393
[99] Zahrim, A., Dexter, Z., Joseph, C., & Hilal, N. (2017). Effective coagulation-flocculation treatment of highly polluted palm oil mill biogas plant wastewater using dual coagulants: decolourisation, kinetics and phytotoxicity studies. Journal of Water Process Engineering, 16, 258-269. https://doi.org/10.1016/j.jwpe.2017.02.005
[100] Zangooei, H., Delnavaz, M., & Asadollahfardi, G. (2016). Prediction of coagulation and flocculation processes using ann models and fuzzy regression. Water Science & Technology, 74(6), 1296-1311. https://doi.org/10.2166/wst.2016.315
[101] Zarei Mahmudabadi, T., Ebrahimi, A. A., Eslami, H., Mokhtari, M., Salmani, M. H., Ghaneian, M. T., ... & Pakdaman, M. (2018). Optimization and economic evaluation of modified coagulation–flocculation process for enhanced treatment of ceramic-tile industry wastewater. AMB Express, 8, 1-12.
[102] Zohuri, B., & Moghaddam, M. (2020). From business intelligence to artificial intelligence. Journal of Material Sciences & Manufacturing Research, 1(1), 1-10.
How to cite this paper
@article{1708814,
author = {Matluck Afolabi, Ogechi Amanda Onukogu, Thompson Odion Igunma, Zamathula Q. Sikhakhane Nwokediegwu, Adeniyi K. Adeleke},
title = {Systematic Review of Coagulation-Flocculation Kinetics and Optimization in Municipal Water Purification Units},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {8},
pages = {259-275},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708814.pdf},
abstract = {This systematic review explores the kinetics and optimization of coagulation-flocculation processes in municipal water purification systems, aiming to consolidate current scientific knowledge and practical advancements. Coagulation-flocculation remains a cornerstone of conventional water treatment, effectively removing suspended particles, colloids, and organic matter. However, process efficiency depends heavily on the reaction kinetics, dosage optimization, mixing intensity, and the physicochemical characteristics of raw water. Drawing from over 100 peer-reviewed studies published between 2000 and 2020, this review categorizes advancements in coagulant types (e.g., alum, ferric chloride, polyaluminum chloride, natural coagulants), flocculation aids, pH regulation, and hydraulic conditions. Particular attention is given to modeling approaches such as pseudo-first-order and pseudo-second-order kinetics, along with population balance models, which have significantly improved the understanding of particle aggregation dynamics and sedimentation rates. The review also assesses the impacts of temperature, ionic strength, and natural organic matter on coagulation efficiency, highlighting variations across different geographic and climatic regions. Optimization strategies are analyzed, including jar test protocols, response surface methodology (RSM), artificial neural networks (ANN), and genetic algorithms (GA), all aimed at minimizing chemical usage, sludge production, and operational costs while ensuring regulatory compliance for turbidity, color, and pathogen removal. Emerging trends identified include the use of hybrid coagulants, nanocomposite materials, and green alternatives derived from plant-based or biodegradable substances. Furthermore, the integration of real-time sensor-based feedback systems and digital twins for predictive control is examined as a pathway toward more resilient and adaptive municipal water treatment operations. Despite these advancements, gaps remain in scaling laboratory-optimized models to full-scale treatment plants, especially in developing nations with limited resources. This review concludes by recommending a holistic approach to kinetic analysis and process optimization that incorporates sustainability, affordability, and local water quality characteristics. These insights provide a roadmap for utilities, engineers, and policymakers to enhance treatment performance and safeguard public health through improved coagulation-flocculation practices in municipal water purification systems.},
keywords = {Coagulation-Flocculation, Municipal Water Purification, Kinetics, Optimization, Modeling, Coagulants, Turbidity Removal, Artificial Neural Networks, Green Coagulants, Water Treatment.},
month = {February},
}