ORIGINAL PAPER
The Implementation of a Coefficient Diagram Method Based Polynomial Controller for a Non-Linear Process with a Distributed Control System in a Chlorine Scrubber System
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1
Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Perundurai, Erode, Tamil Nadu, 638 057, India.
2
Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641 407, India
Submission date: 2022-05-10
Final revision date: 2022-09-22
Acceptance date: 2022-10-19
Publication date: 2022-12-20
Corresponding author
T. Maris Murugan
Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Perundurai, Erode, Tamil Nadu, 638 057, India.
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(4):231-255
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ABSTRACT
The seawater desalination process is emerged as a substantial source of fresh water by removing salt and minerals from an infinite supply of seawater effectively. The first stage in a desalination plant is to use chlorine gas to sterilize the microorganisms in the water. During the excess Chlorine leakage, an alert will be activated, employees will be relocated away from the site for a certain period, and dampers will be manually opened. This will cause unsafe working conditions and a waste of time. To overcome this problem, this paper proposes a Coefficient Diagram Method based Proportional Integral Derivative (CDM-PID) control strategy for the tune the control parameter with the Distributed Control System (DCS) interfaced Conical Tank. During operation, a 10 % NaOH solution is injected into the top of the scrubber column using an Ethylene-Ter-Polymer (ETA) designed distributor to ensure that the solution is evenly distributed across the packing surface. The three control strategies are compared to tune the control parameter with the DCS interfaced Conical Tank. Instead of the sodium hydroxide tank in the chlorine scrubber system, this work presents the Pilot Plant of DCS interfaced with two conical tank interacting systems with different liquid level heights. Here, the proposed CDM-PID controller is compared with the standard Ziegler-Nichols (ZN)-Ultimate Cycling method, and Internal Model Control (IMC) method. The results demonstrated that the proposed CDM-PID approach is superior to existing approaches in terms of low oscillation, settling period, and high robustness.
ACKNOWLEDGEMENTS
The authors show their sincere gratitude to Peak National General Trading and Contracting Company, w.l.l., Kuwait for providing professional training in DCS and for sharing their rich technical expertise in the field. The authors show their special acknowledgement to the Ministry of Electricity & Water, AlZour South Power station, Kuwait for aiding in identifying the research problem in chlorine gas scrubber systems in chlorination units.
METADATA IN OTHER LANGUAGES:
Polish
Implementacja wielomianowego regulatora opartego na metodzie diagramu współczynników dla procesu nieliniowego z rozproszonym systemem sterowania w systemie płuczki chloru
system oczyszczania chloru, regulator proporcjonalno-całkująco-różniczkujący (PID), rozproszony system sterowania (DCS), metoda diagramu współczynników (CDM), współpracujący zbiornik stożkowy
Proces odsalania wody morskiej staje się znaczącym źródłem świeżej wody dzięki skutecznemu usuwaniu soli i minerałów z nieskończonych zasobów wody morskiej. Pierwszym etapem w zakładzie odsalania jest użycie chloru gazowego do sterylizacji mikroorganizmów w wodzie. Podczas nadmiernego wycieku chloru zostanie aktywowany alarm, pracownicy zostaną na pewien czas przeniesieni z terenu zakładu, a klapy zostaną ręcznie otwarte. Spowoduje to niebezpieczne warunki pracy i stratę czasu. Aby rozwiązać ten problem, w niniejszym artykule zaproponowano strategię sterowania opartą na metodzie wykresu współczynników proporcjonalno-całkująco-różniczkujących (Coefficient Diagram Method-Proportional Integral Derivative – CDM-PID) w celu dostrojenia parametru sterowania za pomocą zbiornika stożkowego połączonego z rozproszonym systemem sterowania (Distributed Control System – DCS). Podczas pracy do góry płuczki wstrzykuje się 10% roztwór NaOH za pomocą dystrybutora zaprojektowanego z etylenu-ter-polimeru (Ethylene-Ter-Polymer – ETA), aby zapewnić równomierne rozprowadzenie roztworu na powierzchni wypełnienia. Trzy strategie sterowania są porównywane w celu dostrojenia parametru kontrolnego za pomocą zbiornika stożkowego połączonego z DCS. Zamiast zbiornika wodorotlenku sodu w systemie płuczki chloru, w niniejszej pracy przedstawiono Instalację Pilotażową DCS połączoną z dwoma współpracującymi ze sobą stożkowymi układami zbiorników o różnych wysokościach poziomu cieczy. Tutaj, proponowany regulator CDM-PID jest porównywany ze standardową metodą Zieglera-Nicholsa (ZN)-Ultimate Cycling oraz metodą kontroli modelu wewnętrznego (Internal Model Control – IMC). Wyniki pokazały, że proponowane podejście CDM-PID przewyższa istniejące podejścia pod względem niskich oscylacji, okresu osiadania i wysokiej odporności.
REFERENCES (19)
1.
Asa, E. and Yamamoto, Y. 2021. Aircraft Flight Stabilizer System by CDM Designed Servo State-Feedback Controller. Aerospace 8(2), p. 45, DOI: 10.3390/aerospace8020045.
2.
Bhaba, P.K. and Somasundaram, S. 2009. Real time implementation of a new CDM-PI control scheme in a conical tank liquid level maintaining system. Modern Applied Science 3(5), pp. 38–45, DOI: 10.5539/mas.v3n5p38.
3.
Coelho et al. 2017 – Coelho, J.P., Pinho, T.M., Boaventura-Cunha, J. and de Oliveira, J.B. 2017. A new brain emotional learning Simulink® toolbox for control systems design. IFAC-PapersOnLine 50(1), pp. 16009–16014.
4.
George et al. 2020 – George, M.A., Kamath, D.V. and Thirunavukkarasu, I., 2020, October. An Optimized Fractional-Order PID (FOPID) Controller for a Non-Linear Conical Tank Level Process. [In:] 2020 IEEE Applied Signal Processing Conference (ASPCON), pp. 134–138.
5.
Godo-Pla et al. 2021 – Godo-Pla, L., Rodríguez, J.J., Suquet, J., Emiliano, P., Valero, F., Poch, M. and Monclús, H. 2021. Control of primary disinfection in a drinking water treatment plant based on a fuzzy inference system. Process Safety and Environmental Protection 145, pp. 63–70, DOI: 10.1016/j.psep.2020.07.037.
6.
Hamamci, S.E. and Tan, N. 2006. Design of PI controllers for achieving time and frequency domain specifications simultaneously. ISA transactions 45(4), pp. 529–543, DOI: 10.1016/S0019-0578(07)60230-4.
7.
Hegab, H.M. and Zou, L. 2015. Graphene oxide-assisted membranes: fabrication and potential applications in desalination and water purification. Journal of Membrane Science 484, pp. 95–106, DOI: 10.1016/j.memsci.2015.03.011.
8.
Heshmati et al. 2020 – Heshmati, M., Noroozian, R., Jalilzadeh, S. and Shayeghi, H. 2020. Optimal design of CDM controller to frequency control of a realistic power system equipped with storage devices using grasshopper optimization algorithm. ISA transactions 97, pp. 202–215, DOI: 10.1016/j.isatra.2019.08.028.
9.
Imal, E. 2009. CDM based controller design for nonlinear heat exchanger process. Turkish Journal of Electrical Engineering & Computer Sciences 17(2), pp. 143–161, DOI: 10.3906/elk-0905-45.
10.
Kanagasabai, N. and Jaya, N. 2014. Design of multiloop controller for three tank process using CDM techniques. International Journal on Soft Computing 5(2), p. 11, DOI: 10.5121/ijsc.2014.5202.
11.
Kumpanya et al. 2000 – Kumpanya, D., Benjanarasuth, T., Ngamwiwit, J. and Komine, N. 2000. PI controller design with feedforward by CDM for level processes. 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No. 00CH37119), IEEE vol. 2, pp. 65–69, DOI: 10.1109/TENCON.2000.888390.
12.
Lavanya et al. 2013 – Lavanya, M., Aravind, P., Valluvan, M. and Caroline, B.E. 2013. Model based control for interacting and non-interacting level process using labview. International journal of advanced research in electrical, electronics and instrumentation engineering 2(7).
13.
Manabe, S. 1998. Coefficient diagram method. IFAC Proceedings Volumes 31(21), pp. 211–222, DOI: 10.1016/S1474-6670(17)41080-9.
14.
Manic et al. 2016 – Manic, K.S., Devakumar, S., Vijayan, V. and Rajinikanth, V. 2016. Design of centralized PI controller for interacting conical tank system. Indian Journal of Science and Technology 9(12), p. 1–4, DOI: 10.17485/IJST/2016/V9I12/89920.
15.
Meena et al. 2022 – Meena, V.P., Anand, A., Verma, R., Khatri, M., Behera, S. and Singh, V.P. 2022. Interval Modeling of Doha Water Treatment Plant. [In:] Intelligent Computing Techniques for Smart Energy Systems, pp. 455–462. Springer, Singapore, DOI: 10.1007/978-981-19-0252-9_41.
16.
Roengruen et al. 2009 – Roengruen, P., Tipsuwanporn, V., Puawade, P. and Numsomran, A. 2009. Smith predictor design by cdm for temperature control system. World Academy of Science, Engineering and Technology 35.
17.
Sonehara et al. 2016 – Sonehara, M., Van Toai, N. and Sato, T. 2016. Fundamental study of non-contact water salinity sensor by using electromagnetic means for seawater desalination plants. IEEE transactions on Magnetics 52(7), pp. 1–4, DOI: 10.1109/TMAG.2016.2537921.
18.
Tlili et al. 2003 – Tlili, M.M., Manzola, A.S. and Amor, M.B. 2003. Optimization of the preliminary treatment in a desalination plant by reverse osmosis. Desalination 156(1–3), pp. 69–78.
19.
Zhou, Y. and Tol, R.S. 2004. Implications of desalination for water resources in China – an economic perspective. Desalination 164(3), pp. 225–240, DOI: 10.1016/S0011-9164(04)00191-2.