Optimization of Coagulation Tank Processes through Interval Fuzzy Type 2 Logic System: A Study of Turbidity Reduction
Keywords:
Wastewater; Coagulation; Fuzzy Logic; Genetic Algorithm.Abstract
This research optimizes wastewater treatment's coagulation process through a Genetic Algorithm
and an Interval Type-2 Fuzzy Logic System (IT2FLS). It focuses on enhancing key parameters such
as coagulant dosage, mixing speed and time, pH, and temperature. Comparison with traditional jar
test results under specific conditions validates the effectiveness of these innovative approaches.
Although the final turbidity was marginally higher using the Genetic Algorithm, the IT2FLS closely
mirrored the trend of the jar test results, showing remarkable accuracy in predicting final turbidity.
This predictive accuracy was quantified using Mean Absolute Error, Mean Squared Error, and Root
Mean Squared Error measurements. The research has significant implications for public health,
safety, environmental sustainability, and economic concerns by improving wastewater treatment
efficiency. It recommends further studies and validations with varied datasets for robust real-world application of the IT2FLS model. This work's novelty lies by focusing on optimization techniques
for the wastewater coagulation process. Through the application of Genetic Algorithm and IT2FLS
to observe key parameters, outcome and comparison with traditional methods, the study contributes to a more sustainable and efficient future in wastewater treatment.
References
B. Shi, Q. Wei, D. Wang, Z. Zhu, and H. Tang, “Coagulation of Humic Acid: The
Performance of Preformed and Non-Preformed Al Species,” Colloids and Surfaces a Physicochemical and Engineering Aspects. 2007. doi: 10.1016/j.colsurfa.2006.09.037.
J. Qian et al., “Enhancing Algal Growth and Nutrient Recovery From Anaerobic Digestion Piggery Effluent by an Integrated Pretreatment Strategy of Ammonia Stripping and Flocculation,” Frontiers in Bioengineering and Biotechnology. 2023. doi:
10.3389/fbioe.2023.1219103.
C. S. B. Fitzpatrick, E. Fradin, and J. Gregory, “Temperature effects on flocculation, using different coagulants,” Water Science and Technology, vol. 50, no. 12, pp. 171–175, 2004, doi:
10.2166/wst.2004.0710.
C. Kan and J. Pan, “Time requirement for rapid-mixing in coagulation,” Colloids Surf A Physicochem Eng Asp, vol. 203, pp. 1–9, Jul. 2002, doi: 10.1016/S0927-7757(01)01095-0.
S. Ramphal and S. Muzi Sibiya, “Optimization of time requirement for rapid mixing during coagulation using a photometric dispersion analyzer,” in Procedia Engineering, Elsevier Ltd, 2014, pp. 1401–1410. doi: 10.1016/j.proeng.2014.02.155.
M. Kumar Karnena, B. Kavitha Dwarapureddi, and V. Saritha, “Alum, Chitin and Sago as coagulants for the optimization of process parameters focussing on coagulant dose and mixing speed,” Watershed Ecology and the Environment, vol. 4, pp. 112–124, 2022, doi:
10.1016/j.wsee.2022.10.001.
A. Gnida, J. Wiszniowski, E. Felis, J. Sikora, J. Surmacz-Górska, and K. Miksch, “The effect of temperature on the efficiency of industrial wastewater nitrification and its (geno)toxicity,” Archives of Environmental Protection, vol. 42, no. 1, pp. 27–34, Mar. 2016, doi: 10.1515/aep2016-0003.
J. T. Starczewski, K. Przybyszewski, A. Byrski, E. Szmidt, and C. Napoli, “A Novel Approach to Type-Reduction and Design of Interval Type-2 Fuzzy Logic Systems,” Journal of Artificial Intelligence and Soft Computing Research. 2022. doi: 10.2478/jaiscr-2022-0013.
R. Sepúlveda, O. Castillo, P. Melin, A. Rodríguez-Díaz, and O. Montiel, “Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic,” Inf Sci (N Y), vol. 177, no. 10, pp. 2023–2048, 2007, doi: https://doi.org/10.1016/j.ins.2006.10.004.
W. Cai, M. Sen, K. T. Yang, and A. Pacheco-Vega, “Genetic-programming-based symbolic regression for heat transfer correlations of a compact heat exchanger,” in Proceedings of the
ASME Summer Heat Transfer Conference, 2005, pp. 367–374. doi: 10.1115/HT2005-72293.
D. Kucak, V. Juričić, and G. Dambic, “Application of Genetic Algorithms in Higher Education Area.” 2019. doi: 10.2507/30th.daaam.proceedings.045.
S. K. V. Katoch, S. Chauhan, and V. Kumar, “A review on genetic algorithm: past, present, and future.,” Multimedia tools and applications, 80(5), 8091–8126., 2021.
A. Bajaj and O. P. Sangwan, “A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms,” Ieee Access. 2019. doi: 10.1109/access.2019.2938260.
S. Hamblin, “On the Practical Usage of Genetic Algorithms in Ecology and Evolution,” Methods in Ecology and Evolution. 2012. doi: 10.1111/2041-210x.12000.
A. T. Azar, “Overview of type-2 fuzzy logic systems,” International Journal of Fuzzy System Applications, vol. 2, no. 4, pp. 1–28, 2012, doi: 10.4018/ijfsa.2012100101.
M. B. Özek and Z. H. Akpolat, “A Software Tool: Type-2 Fuzzy Logic Toolbox,” Computer Applications in Engineering Education. 2008. doi: 10.1002/cae.20138.
N. M. Q. Ibrahim and N. N. K. H. Al-Dikhil, “Optimization of Interval Type-2 Fuzzy Logic System by Using a New Hybrid Method of Whale Optimization Algorithm and Extreme Learning Machine,” Tikrit Journal of Pure Science. 2022. doi: 10.25130/tjps.v26i2.129.
W. Febrina, T. Mesra, and H. Hendra, “Optimum Dosage of Coagulant and Flocculant on Sea Water Purification Process,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Apr. 2020. doi: 10.1088/1755-1315/469/1/012023.
S. W. Bin Ahmed, G. M. Ayoub, M. Al-Hindi, and F. Azizi, “The Effect of Fast Mixing Conditions on the Coagulation-Flocculation of Highly Turbid Suspensions Using Magnesium Hydroxide Coagulant,” 2015.
D. Fitria, M. Scholz, G. M. Swift, and F. A. M. Al-Faraj, “Impact of Temperature and
Coagulants on Sludge Dewaterability,” International Journal of Technology. 2022. doi:
10.14716/ijtech.v13i3.4886.
N. Wei, Z. Zhang, D. Liu, Y. Wu, J. Wang, and Q. Wang, “Coagulation behavior of polyaluminum chloride: Effects of pH and coagulant dosage,” Chin J Chem Eng, vol. 23, no. 6, pp. 1041–1046, Jun. 2015, doi: 10.1016/j.cjche.2015.02.003.
G. De Wu and S. L. Lo, “Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system,” Eng Appl Artif Intell, vol. 21, no. 8, pp. 1189–1195, Dec. 2008, doi: 10.1016/J.ENGAPPAI.2008.03.015.
O. Castillo, L. Amador-Angulo, J. R. Castro, and M. García-Valdez, “A Comparative Study of Type-1 Fuzzy Logic Systems, Interval Type-2 Fuzzy Logic Systems and Generalized Type-2 Fuzzy Logic Systems in Control Problems,” Information Sciences. 2016. doi:
10.1016/j.ins.2016.03.026.
D. S. Mai, K.-T. T. Bui, and C. Van Doan, “Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting,” International Journal of Fuzzy Systems. 2023. doi: 10.1007/s40815-022-01452-3.
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