Analysis of the effects of recycling on process control

1 Roy, P. & Roy, B.K. (2016). Fractional order PI control applied to level control in coupled two tank MIMO system witch experimental validation. Control Eng. Pract. 48, 119. DOI: 10.1016/j.conengprac.2016.01.002. Open DOISearch in Google Scholar

2 Kadhim, R.A., Raheem, A.K.K.A. & Gitaffa, S.A.H. (2017). Implementing of liquid tank level control using arduino-labview interfaceing with ultrasonic sensor. Kufa J. Eng. 8, 29. Search in Google Scholar

3 Kittur, J. (2018). Enhancing the controller design skills in the course linear control systems. J. Eng. Educ. Transform. Special Issue. ISSN 2394-1707. Search in Google Scholar

4 Mendes, J., Osório, L. & Araújo, R. (2017). Self-Tuning PID Controllers in Pursuit of Plug and Play Capacity. Control Eng. Pract. 69, 73. DOI: 10.1016/j.conengprac.2017.09.006. Open DOISearch in Google Scholar

5 Urrego, J.A.R. & Restrepo, N.L.P. (2016). Aplicación de Diseño, simulación, identificación de sistemas e implementación de controladores PID – DIGITROL. Rev. Politécnica. 12, 27. ISSN 2256-5353. Search in Google Scholar

6 Fernandes, J.P.S., Vidal, S.F.S., Marques, M.O., Souza, V.R.S. & Medeiros, L.A. (2019). Identification, control and tuning of non-interactive series tanks. Braz. J. Dev. 5. DOI: 10.34117/bjdv5n10-207. Open DOISearch in Google Scholar

7 Prusty, S.B., Seshagiri, S., Pati, U.C. & Mahapatra, K.K. (2016). Sliding Mode Control of Coupled Tanks using Conditional Integrators. Indian Control Conference, Indian Institute of technology Hyderabad, (2016). Search in Google Scholar

8 Luz, G.R., Conceição, W.A.S., Jorge, Luiz, M. M., paraíso, P.R. & Andrade, C.M.G. (2010). Dynamic modeling and control of soybean meal drying in a direct rotary dryer. Food Bioprod. Process. 88. DOI: 10.1016/j.fbp.2010.01.008. Open DOISearch in Google Scholar

9 Pan, H., Wong, H., Kapila, V. & Queiroz, M.S. (2005). Experimental validation of a non linear backstepping liquid level controller for a state coupled two tank system. Control Eng. Pract. 13, 27. DOI: 10.1016/j.conengprac.2003.12.019. Open DOISearch in Google Scholar

10 Shah, D.H. & Patel, D.M. (2019). Design of sliding mode control for quadruple-tank MIMO process with time delay compensation. J. Process Control 76, 46. DOI: 10.1016/j.jprocont.2019.01.006. Open DOISearch in Google Scholar

11 Ogunnaike, B.A. & Ray, W.H. Process Dynamics, Modeling, and Control. New York: Oxford University Press, 1994. Search in Google Scholar

12 Wang, J., Shin., P.C., Wu, Y. & Carroll, J.M. (2015). Comparative case studies of open source software peer review practices. Inf. Softw. Technol. 67, 1. DOI: 10.1016/j.infsof.2015.06.002. Open DOISearch in Google Scholar

13 Verdejo, H. & Becker, C. (2020). The Erratic Implementation of Measuring, Monitoring and Control System (MMCS) in Chile: The crisis on smart meters. Energy Reports 6, 2140. DOI: 10.1016/j.egyr.2020.08.005. Open DOISearch in Google Scholar

14 Alvaro, R.J., Maria, A.M. & David, F.B. (2018). Level control in system of tanks in interacting mode using Xcos software. Contemp. Eng. Sci. 11, 63. DOI: 10.12988/ces.2018.712206. Open DOISearch in Google Scholar

15 Mankotia, A. & Shukla, A.K. (2021). IOT based manhole detection and monitoring system using Arduino. Mater. Today Process. 30. DOI: 10.1016/j.matpr.2021.12.264. Open DOISearch in Google Scholar

16 Omar, H.M. (2018). Enhancing automatic control learning through Arduinobased projects. Eur. J. Eng. Educ. 43, DOI: 10.1080/03043797.2017.1390548. Open DOISearch in Google Scholar

17 Machado, M.M., Carvalho, A.J., Santos, M.F. & Carvalho, J.R. (2018). Case Study: Level and Temperature Multivariable Control and Design via Arduino through Control Loop Decoupling. 19th International Carpathian Control Conference (ICCC). Search in Google Scholar

18 Veerasamy, V., Wahab, N.I.A., Ramachandran, R., Othman, M.L., Hizam, H., Kumar, J S. & Irudayaraj, A.X.R. Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system. Expert Syst. Appl. 192, (2022). Search in Google Scholar

19 Briones, O., Alarcón, R., Rojas, J.A. & Sbarbaro, D. (2022). Tuning Generalized Predictive PI controllers for process control applications. ISA Trans. 119, 184. DOI: 10.1016/j.isatra.2021.02.040. Open DOISearch in Google Scholar

20 Rodriguez, A., Plett, G.L. & Trimboli, M.S. (2018). Improved transfer functions modeling linearized lithium-ion battery-cell internal electrochemical variables. J. Energy Storage 20, 560. DOI: 10.1016/j.est.2018.06.015. Open DOISearch in Google Scholar

21 Ogata, K. Engenharia de controle moderno. 4ª ed. Prentice-Hall Inc, 2003. Search in Google Scholar

22 Ziegler, J.G. & Nichols, N.B. Optimum Settings For Automatic Controllers. Transactions of the A.S.M.E, 1942. 759–768. Search in Google Scholar

23 Kaistha, N. Liquid level control in a recycle loop. J. Process Control 104 (2021) 11. DOI: 10.1016/j.jprocont.2021.05.014. Open DOISearch in Google Scholar

24 Grimholt, C., Skogestad, S. (2018). Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules. J. Process Control 70, 36. DOI: 10.1016/j.jprocont.2018.06.011. Open DOISearch in Google Scholar

25 Neto, A.F.S., Gomes, F.J. Modeling and Control of a Tito Process System Industrial through the OPC Protocol and FOSS Scilab. Brazilian Soc. Appl. Comput. Mathematics. 4, (2016). Search in Google Scholar

26 Handbook of Chemistry and Physics, CRC press, Ed. 64. Search in Google Scholar

27 Rajamand, S., (2021). Feedback-based control structure for frequency/voltage regulation using the state of electrical vehicle charge station and point estimation method. Sustain. Energy Technol. Assess. 51, 101922. DOI:10.1016/j.seta.2021.101922. Open DOISearch in Google Scholar

28 Meng, X., Yu, H., Zhang, J., Xu, T., Wu, H. & Yan, K. (2021). Disturbance Observer-Based Feedback Linearization Control for a Quadruple-Tank Liquid Level System. ISA Trans 27, DOI: 10.1016/j.isatra.2021.04.021. Open DOISearch in Google Scholar

29 Lawrence, N.P., Forbes, M.G., Loewen, P.D., McClement, D.G., Backström, J.U. & Gopaluni, R.B. (2022). Deep reinforcement learning with shallow controllers: An experimental application to PID tuning. Control Eng. Pract. 121, 105046. DOI: 10.1016/j.conengprac.2021.105046. Open DOISearch in Google Scholar

30 Badu, N.R. & Saikia, L.C. (2022). Optimal location of accurate HVDC and energy storage devices in a deregulated AGC integrated with PWTS considering HPA-ISE as performance index. Eng. Sci. Tech. Inter. J. 33, 101072. DOI: 10.1016/j.jestch.2021.10.004. Open DOISearch in Google Scholar

31 Bui, P.D.H. & You, S.S. (2022). Dynamics modeling and motion control for high-speed underwater vehicles using H-infinity synthesis with anti-windup compensator. J. Ocean Eng. Sci. 7, 84, DOI: 10.1016/j.joes.2021.07.002. Open DOISearch in Google Scholar

32 Neto, A.M.S., Damo, T.P. & Coelho, A.A.R. Laboratory Essay with Online Back-calculation Anti-Windup Scheme for a MTG System. IFAC 45, (2012). Search in Google Scholar

33 Rubio, J.F.M., Cuéllar, B.M., Villa, M.V., Rodríguez, D.C. & Sename, Q. (2012). Control of delayed recycling systems with unstable first order forward loop. J. Process Control 22, 729. DOI: 10.1016/j.jprocont.2012.02.002. Open DOISearch in Google Scholar

34 Samad, T.E. & Annaswamy, A.M. Control in the process industries, The impact of control technology, IEEE Control System Society, 21, (2011). Search in Google Scholar