Design and Performance Evaluation of a Programmable Logic Controller-Based Water Treatment Automation System
DOI:
https://doi.org/10.21831/jraee.v3i1.673Keywords:
Programmable Logic Controller, SCADA, Water Treatment Automation, Human-Machine Interface, Chlorine DosingAbstract
Small-scale water-treatment units often rely on manually sequenced pumps, mixers, and chemical dosing, which can cause inconsistent operation and limited process visibility. This study develops and evaluates a laboratory-scale automation system based on an Omron CP1H-XA40DR-A programmable logic controller (PLC) integrated with a Wonderware InTouch supervisory control and data acquisition (SCADA) interface. The main contribution is a low-complexity PLC-SCADA architecture that combines one-button sequential operation with quantitative evaluation of functional logic, HMI response, batch-volume repeatability, and chlorine-dose screening. The system was developed through requirement analysis, electrical and mechanical design, ladder-program implementation, SCADA visualization, fabrication, and experimental testing. Functional testing covered six command-state combinations, HMI response time was measured for seven components, and ten filling cycles were analyzed for water-height and volume consistency. Calcium hypochlorite was screened at calculated masses of 4.44 mg and 13.33 mg for 2 L batches. All six control-state tests were successful. Mean HMI response time was 0.543 ± 0.282 s (0.2-1.0 s). Mean water height and volume were 10.72 ± 0.13 cm and 2.236 ± 0.027 L, with coefficients of variation of 1.23% and 1.22%. At 4.44 mg, all ten samples were reported colorless and without detectable odor or taste; at 13.33 mg, slight chlorine odor and slight bitter taste were each reported in five samples. The prototype demonstrates reliable sequential control and repeatable laboratory-scale filling; however, the water-quality results are only organoleptic screening because residual chlorine, turbidity, pH, and microbiological safety were not measured.
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References
[1] K. H. Kaittan and S. J. Mohammed, "PLC-SCADA automation of inlet wastewater treatment processes: Design, implementation, and evaluation," Journal Europeen des Systemes Automatises, vol. 57, no. 3, pp. 787-796, 2024, doi: 10.18280/jesa.570317.
[2] S. Cairone et al., "Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies," Journal of Water Process Engineering, vol. 63, Art. no. 105486, 2024, doi: 10.1016/j.jwpe.2024.105486.
[3] A. Moretti, H. L. Ivan, and J. Skvaril, "A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?," Journal of Water Process Engineering, vol. 60, Art. no. 105061, 2024, doi: 10.1016/j.jwpe.2024.105061.
[4] H. M. Forhad et al., "IoT based real-time water quality monitoring system in water treatment plants (WTPs)," Heliyon, vol. 10, no. 23, Art. no. e40746, 2024, doi: 10.1016/j.heliyon.2024.e40746.
[5] M. A. Murti et al., "Smart system for water quality monitoring utilizing long-range-based Internet of Things," Applied Water Science, vol. 14, Art. no. 69, 2024, doi: 10.1007/s13201-024-02128-z.
[6] C. Rodriguez-Alonso, I. Pena-Regueiro, and O. Garcia, "Digital twin platform for water treatment plants using microservices architecture," Sensors, vol. 24, no. 5, Art. no. 1568, 2024, doi: 10.3390/s24051568.
[7] S. Daneshgar et al., "A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility," Water Environment Research, vol. 96, no. 3, Art. no. e11016, 2024, doi: 10.1002/wer.11016.
[8] Y. Wei, A. W.-K. Law, and C. Yang, "Real-time data-processing framework with model updating for digital twins of water treatment facilities," Water, vol. 14, no. 22, Art. no. 3591, 2022, doi: 10.3390/w14223591.
[9] Y. Wei, A. W.-K. Law, C. Yang, and D. Tang, "Combined anomaly detection framework for digital twins of water treatment facilities," Water, vol. 14, no. 7, Art. no. 1001, 2022, doi: 10.3390/w14071001.
[10] Z. Ma et al., "Towards the digitalization of water treatment facilities: A case study on machine learning-enabled digital twins," Journal of Water Process Engineering, vol. 77, Art. no. 108316, 2025, doi: 10.1016/j.jwpe.2025.108316.
[11] J.-H. Wang et al., "An online intelligent management method for wastewater treatment supported by coupling data-driven and mechanism models," Journal of Water Process Engineering, vol. 53, Art. no. 103653, 2023, doi: 10.1016/j.jwpe.2023.103653.
[12] K. J. Nam, S. K. Heo, S. Y. Kim, and C. K. Yoo, "A multi-agent AI reinforcement-based digital multi-solution for optimal operation of a full-scale wastewater treatment plant under various influent conditions," Journal of Water Process Engineering, vol. 52, Art. no. 103533, 2023, doi: 10.1016/j.jwpe.2023.103533.
[13] M. V. Ruano, J. Ribes, A. Ruiz-Martinez, A. Seco, and A. Robles, "An advanced control system for nitrogen removal and energy consumption optimization in full-scale wastewater treatment plants," Journal of Water Process Engineering, vol. 57, Art. no. 104705, 2024, doi: 10.1016/j.jwpe.2023.104705.
[14] H. Ding et al., "Soft sensor enabled real-time chemical dosing control systems for wastewater treatment: From hybrid model to full-scale application," Journal of Water Process Engineering, vol. 63, Art. no. 105431, 2024, doi: 10.1016/j.jwpe.2024.105431.
[15] H. Daraei et al., "DOC signal-based alum dose control for drinking water treatment plants," Journal of Water Process Engineering, vol. 54, Art. no. 103934, 2023, doi: 10.1016/j.jwpe.2023.103934.
[16] S. Lin, J. Kim, C. Hua, M.-H. Park, and S. Kang, "Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model," Water Research, vol. 232, Art. no. 119665, 2023, doi: 10.1016/j.watres.2023.119665.
[17] J. Kim et al., "Optimizing coagulant dosage using deep learning models with large-scale data," Chemosphere, vol. 350, Art. no. 140989, 2024, doi: 10.1016/j.chemosphere.2023.140989.
[18] M. Moeini and A. Abokifa, "Chlorine dosage management in drinking water systems: Comparing Bayesian optimization to evolutionary algorithms," Journal of Hydroinformatics, vol. 26, no. 11, pp. 2720-2738, 2024, doi: 10.2166/hydro.2024.090.
[19] Y. Khor, A. R. Abdul Aziz, and S. S. Chong, "Recent developments and sustainability in monitoring chlorine residuals for water quality control: A critical review," RSC Sustainability, vol. 2, no. 9, pp. 2468-2485, 2024, doi: 10.1039/D4SU00188E.
[20] M. Han, J. Yao, A. W.-K. Law, and X. Yin, "Efficient economic model predictive control of water treatment process with learning-based Koopman operator," Control Engineering Practice, vol. 149, Art. no. 105975, 2024, doi: 10.1016/j.conengprac.2024.105975.
