The paper is focused on the identification, control design, and experimental verification of a two-input two-output hot-air laboratory apparatus representing a small-scale version of appliances widely used in the industry. A decentralized multivariable controller design is proposed, satisfying control-loop decoupling and measurable disturbance rejection. The proposed inverted or equivalent noninverted decoupling controllers serve for the rejection of cross-interactions in controlled loops, whereas open-loop antidisturbance members satisfy the absolute invariance to the disturbances. Explicit controller-structure design formulae are derived, and their equivalence to other decoupling schemes is proven. Three tuning rules are used to set primary controller parameters, which are further discretized. All the control responses are simulated in the Matlab/Simulink environment. In the experimental part, two data-acquisition, communication, and control interfaces are set up. Namely, a programmable logic controller and a computer equipped with the peripheral component interconnect card commonly used in industrial practice are implemented. A simple supervisory control and data acquisition human-machine interface via the Control Web environment is developed. The laboratory experiments prove better temperature control performance measured by integral criteria by 35.3%, less energy consumption by up to 6%, and control effort of mechanical actuator parts by up to 17.1% for our method compared to the coupled or disturbance-ignoring design in practice. It was also observed that the use of a programmable logic controller gives better performance measures for both temperature and air-flow control.

Centre for Security Information and Advanced Technologies Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 760 05 760 01 Zlín Czech Republic

Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 760 05 Zlín Czech Republic

Department of Chemical and Petroleum Engineering University of Calgary Energy Environment and Experiential Learning Building 750 Campus Dr NW Calgary AB T2N 4H9 Canada

Department of Energy and Power Engineering School of Mechanical Engineering Beijing Institute of Technology Engine East Building 125 Beijing 100081 China

School of Mechanical Engineering and Automation Harbin Institute of Technology Schenzhen Xili University Town Guangdong 518055 China

TEAZ s r o tř Tomáše Bati č p 1658 Otrokovice 765 02 Czech Republic

Zobrazit více v PubMed

Bergman T. L.; Lavine A. S.; Incropera F. P.; Dewitt D. P.. Fundamentals of Heat and Mass Transfer, 7th ed.; John Wiley & Sons: Hoboken, NJ, 2011.

Pekař L.Introduction to heat exchangers. In Advanced Analytic and Control Techniques for Thermal Systems with Heat Exchangers, 1st ed.; Pekař L., Ed.; Academic Press, Elsevier: Cambridge, MA, 2020; pp 3–20.

Corwin J. F. A hot-air apparatus dryer for general laboratory use. Science 1947, 106, 353.10.1126/science.106.2754.353. PubMed DOI

Getahun S.; Ambaw A.; Delele M.; Meyer C. J.; Opara U. L. Analysis of airflow and heat transfer inside fruit packed refrigerated shipping container: Part II – Evaluation of apple packaging design and vertical flow resistance. J. Food Eng. 2017, 203, 83–94. 10.1016/j.jfoodeng.2017.02.011. DOI

Rao D. V. S.; Shivashankara K. S. Individual shrink wrapping extends the storage life and maintains the antioxidants of mango (cvs. ‘Alphonso’ and ‘Banganapalli’) stored at 8 °C. J. Food Sci. Technol. 2015, 52, 4351–4359. 10.1007/s13197-014-1468-6. PubMed DOI PMC

Hot Air Tunnel . https://www.epackagingsrl.com/products/hot-air-cutting-tunnel (accessed Dec 15, 2020).

Hot Air Tunnel . http://www.nortan.it/sleeve/hot-air-tunnel/?lang=en (accessed Dec 15, 2020).

Honzíček J. Curing of air-drying paints: A critical review. Ind. Eng. Chem. Res. 2019, 58, 12485–12505. 10.1021/acs.iecr.9b02567. DOI

Suriyachai P.; Thavarungkul N.; Saeoui P. Effects of azodicarbonamide and vulcanization methods on acrylonitrile-butadiene rubber/polyvinyl chloride foam properties. Rubber Chem. Technol. 2013, 86, 86–95. 10.5254/rct.13.88947. DOI

Visakh P. M.; Thomas S.; Chandra A. K.; Mathew A. P.. Advances in Elastomers I: Blends and Interpenetrating Networks; Springer: Berlin, Heidelberg, Germany, 2013.

Jildeh Z. B.; Wagner P. H.; Schöning M. J. Sterilization of objects, products, and packaging surfaces and their characterization in different fields of industry: The status in 2020. Phys. Status Solidi A 2021, 200073210.1002/pssa.202000732. DOI

Morales-Delgado D. Y.; Téllez-Medina D. I.; Rivero-Ramírez N. L.; Arellano-Cárdenas S.; López-Cortez S.; Hérnandez-Sánchez H.; Gutiérrez-López G.; Cornejo-Mazón M. Effect of convective drying on total anthocyanin content, antioxidant activity and cell morphometric parameters of strawberry parenchymal tissue. Rev. Mex. Ing. Quim. 2014, 13, 179–187.

Doymaz I. Convective drying kinetics of strawberry. Chem. Eng. Process. 2008, 47, 914–919. 10.1016/j.cep.2007.02.003. DOI

El-Mesery H. S.; Mwithiga G. Performance of a convective, infrared and combined infrared- convective heated conveyor-belt dryer. J. Food Sci. Technol. 2015, 52, 2721–2730. 10.1007/s13197-014-1347-1. PubMed DOI PMC

Mihindukulasuriya S. D.; Jayasuriya H. P. W. Drying of chilli in a combined infrared and hot air rotary dryer. J. Food Sci. Technol. 2015, 52, 4895–4904. 10.1007/s13197-014-1546-9. PubMed DOI PMC

Giri S. K.; Prasad S. Drying kinetics and rehydration characteristics of microwave-vacuum and convective hot air dried button mushrooms. J. Food Eng. 2007, 78, 512–521. 10.1016/j.jfoodeng.2005.10.021. DOI

Loha C.; Das R.; Choudhury B.; Chatterjee P. K. Evaluation of air drying characteristics of sliced ginger (Zingiber officinale) in a forced convective cabinet dryer and thermal conductivity measurement. J. Food Process. Technol. 2012, 03, 1–5. 10.4172/2157-7110.1000160. DOI

Stoforos G. N.; Rezaei F.; Simunovic J.; Sandeep K. P. Enhancement of continuous flow cooling using hydrophobic surface treatment. J. Food Eng. 2021, 300, 11052410.1016/j.jfoodeng.2021.110524. DOI

Silva Júnior M. A. V.; Rabi J. A.; Ribeiro R.; Dacanal G. C. Modeling of convective drying of cornstarch-alginate gel slabs. J. Food Eng. 2019, 250, 9–17. 10.1016/j.jfoodeng.2019.01.015. DOI

Mujumdar A. S.Drying principles and practice. In Albright’s Chemical Engineering Handbook; Albright L. F., Ed.; CRC: Boca Raton, FL, 2008; pp 1667–1716.

Rashid M. T.; Ma H.; Jatoi M. A.; Hashim M. M.; Wali A.; Safdar B. Influence of ultrasonic pretreatment with hot air drying on nutritional quality and structural related changes in dried sweet potatoes. Int. J. Food Eng. 2019, 15, 2018040910.1515/ijfe-2018-0409. DOI

Hajjaji S. E.Biorefining of waste coffee grounds: Turning an environmental problem into an opportunity. In IOP Conference Series: Earth and Environmental Science,505, 2020 6th International Conference on Environment and Renewable Energy, Hanoi, Vietnam, 2020; No. 012026.

Ghavidel R. A.; Davoodi M. G. Studies on physiochemical properties of tomato powder as affected by different dehydration methods and pretreatments. World. Acad. Sci. Eng. Technol. 2010, 69, 596–605. 10.5281/zenodo.1077153. DOI

Yilmaz M. S.; Şakiyan Ö.; Barutcu Mazi I.; Mazi B. G. Phenolic content and some physical properties of dried broccoli as affected by drying method. Food Sci. Technol. Int. (London, U. K.) 2019, 25, 76–88. 10.1177/1082013218797527. PubMed DOI

Precoppe M.; Chapuis A.; Müller J.; Abass A. Tunnel dryer and pneumatic dryer performance evaluation to improve small-scale cassava processing in Tanzania. J. Food Process Eng. 2017, 40, e1227410.1111/jfpe.12274. DOI

Verma M.; Singh J.; Kaur D.; Mishra V.; Rai G. K. Effect of various dehydration methods and storage on physicochemical properties of guava powder. J. Food Sci. Technol. 2015, 52, 528–534. 10.1007/s13197-013-1020-0. DOI

Matušů R.; Prokop R.; Dlapa M.. Robust control of temperature in hot-air tunnel. In 2008 16th Mediterranean Conference on Control and Automation, Ajaccio, France, 2008; 576–581.

Matušů R.; Prokop R. Control of air-flow speed in laboratory model of hot-air tunnel. Procedia Eng. 2015, 100, 345–349. 10.1016/j.proeng.2015.01.403. DOI

Pivoňka P.; Nepevný P.. Hot-air tunnel control using multi-dimensional predictive controller based on neural network model. In Annals of DAAAM and Proceedings of the International DAAAM Symposium, Vienna, Austria, 2006; 267–268.

Bogdan S.; Birgmajer B.; Kovačić Z. Model predictive and fuzzy control of a road tunnel ventilation system. Transp. Res. Part C Emerg. Technol. 2008, 16, 574–592. 10.1016/j.trc.2007.11.004. DOI

Oka Y.; Atkinson G. T. Control of smoke flow in tunnel fires. Fire Saf. J. 1995, 25, 305–322. 10.1016/0379-7112(96)00007-0. DOI

Feng S.; Li Y.; Hou Y.; Li J.; Huang Y. Study on the critical velocity for smoke control in a subway tunnel cross-passage. Tunn. Undergr. Space Technol. 2020, 97, 10323410.1016/j.tust.2019.103234. DOI

Chaabat F.; Salizzoni P.; Creyssels M.; Mos A.; Wingrave J.; Correia H.; Marro M. Smoke control in tunnel with a transverse ventilation system: An experimental study. Build. Environ. 2020, 167, 10648010.1016/j.buildenv.2019.106480. DOI

Kondratov A. P.; Volinsky A. A.; Chen J. Macro-mechanism of polyvinyl chloride shrink sleeves embossed marking. J. Appl. Polym. Sci. 2016, 133, 4369110.1002/app.43691. DOI

Choab N.; Allouhi A.; El Maakoul A.; Kousksou T.; Saadeddine S.; Jamil A. Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies. Sol. Energy 2019, 191, 109–137. 10.1016/j.solener.2019.08.042. DOI

Chen L.; Du S.; He Y.; Liang M.; Xu D. Robust model predictive control for greenhouse temperature based on particle swarm optimization. Inf. Process. Agric. 2018, 5, 329–338. 10.1016/j.inpa.2018.04.003. DOI

Jung D.-H.; Kim H.-J.; Kim J. Y.; Lee T. S.; Park S. H. Model predictive control via output feedback neural network for improved multi-window greenhouse ventilation control. Sensors 2020, 20, 175610.3390/s20061756. PubMed DOI PMC

Nachidi M.; Rodríguez F.; Tadeo F.; Guzman J. L. Takagi–Sugeno control of nocturnal temperature in greenhouses using air heating. ISA Trans. 2011, 50, 315–320. 10.1016/j.isatra.2010.11.007. PubMed DOI

Villarreal-Guerrero F.; Kacira M.; Fitz-Rodríguez E.; Linker R.; Kubota C.; Giacomelli G. A.; Arbel A. Simulated performance of a greenhouse cooling control strategy with natural ventilation and fog cooling. Biosyst. Eng. 2012, 111, 217–228. 10.1016/j.biosystemseng.2011.11.015. DOI

Del Sagrado J.; Sánchez J. A.; Rodríguez F.; Berenguel M. Bayesian networks for greenhouse temperature control. J. Appl. Logic 2016, 17, 25–35. 10.1016/j.jal.2015.09.006. DOI

Liu L.; Tian S.; Xue D.; Zhang T.; Chen Y. Q.; Zhang S. A review of industrial MIMO decoupling control. Int. J. Control Autom. Syst. 2019, 17, 1246–1254. 10.1007/s12555-018-0367-4. DOI

Bakule L. Decentralized control: An overview. Annu. Rev. Control 2008, 32, 87–98. 10.1016/j.arcontrol.2008.03.004. DOI

Hu W.; Cai W. J.; Xiao G. Decentralized control system design for MIMO processes with integrators/differentiators. Ind. Eng. Chem. Res. 2010, 49, 12521–12528. 10.1021/ie1005838. DOI

Kadhim A. M. H.Selection of Decentralized Control Configuration for Uncertain Systems. Ph.D. Thesis, Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Luleå, Sweden, 2018.

Mahapatro S. R.; Subudhi B.; Ghosh S. Design and experimental realization of a robust decentralized PI controller for a coupled tank system. ISA Trans. 2019, 89, 158–168. 10.1016/j.isatra.2018.12.003. PubMed DOI

Garrido J.; Vázquez F.; Morilla F. Centralized inverted decoupling control. Ind. Eng. Chem. Res. 2013, 52, 7854–7866. 10.1021/ie400367m. DOI

Garrido J.; Vázquez F.; Morilla F. Multivariable PID control by decoupling. Int. J. Syst. Sci. 2016, 47, 1054–1072. 10.1080/00207721.2014.911390. DOI

Noeding M.; Martensen J.; Lemke N.; Tegethoff W.; Koehler J.. Selection of decoupling control methods suited for automated design for uncertain TITO processes. In 2018 IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA, 2018; 498–505.

Wang Q. G.Decoupling Control; Springer: Berlin, Heidelberg, Germany, 2003.

Gagnon E.; Pomerleau A.; Desbiens A. Simplified, ideal or inverted decoupling?. ISA Trans. 1998, 37, 265–276. 10.1016/S0019-0578(98)00023-8. DOI

Jevtović B. T.; Mataušek M. R. PID controller design of TITO system based on ideal decoupler. J. Process Control 2010, 20, 869–876. 10.1016/j.jprocont.2010.05.006. DOI

Hamdy D. M.; Ramadan A.; Abozalam B. Comparative study of different decoupling schemes for TITO binary distillation column via PI controller. IEEE/CAA J. Autom. Sin. 2018, 5, 869–877. 10.1109/JAS.2016.7510040. DOI

Lee J.; Hyun Kim D.; Edgar T. F. Static decouplers for control of multivariable processes. AIChE J. 2005, 51, 2712–2720. 10.1002/aic.10520. DOI

Rajapandiyan C.; Chidambaram M. Controller design for MIMO processes based on simple decoupled equivalent transfer functions and simplified decoupler. Ind. Eng. Chem. Res. 2012, 51, 12398–12410. 10.1021/ie301448c. DOI

Wade H. L. Inverted decoupling: A neglected technique. ISA Trans. 1997, 36, 3–10. 10.1016/S0019-0578(97)00008-6. DOI

Giraldo S. A. C.; Flesch R. C. C.; Normey-Rico J. E.; Sejas M. Z. P. A method for designing decoupled filtered Smith predictors for square MIMO systems with multiple time delays. IEEE Trans. Ind. Appl. 2018, 54, 6439–6449. 10.1109/TIA.2018.2849365. DOI

Li M.; Zhou P. Analytical design based hierarchical control for non-square MIMO wood-chip refining process. ISA Trans. 2019, 90, 52–63. 10.1016/j.isatra.2018.12.045. PubMed DOI

Chuong V. L.; Vu T. N. L.; Truong N. T. N.; Jung J. H. An analytical design of simplified decoupling Smith predictors for multivariable processes. Appl. Sci. 2019, 9, 2487.10.3390/app9122487. DOI

Jain A.; Babu B. V. Sensitivity of relative gain array for processes with uncertain gains and residence times. IFAC-PapersOnLine 2016, 49, 486–491. 10.1016/j.ifacol.2016.03.101. DOI

Bristol E. On a new measure of interaction for multivariable process control. IEEE Trans. Autom. Control 1966, 11, 133–134. 10.1109/TAC.1966.1098266. DOI

McAvoy T. J.; Arkun Y.; Chen R.; Robinson D.; Schnelle P. D. A new approach to defining a dynamic relative gain. Control Eng. Pract. 2003, 11, 907–914. 10.1016/S0967-0661(02)00207-1. DOI

Balestrino A.; Crisostomi E.; Landi A.; Menicagli A.. ARGA loop pairing criteria for multivariable systems. In Proceedings of the 47th IEEE Conference on Decision and Control (CDC ‘08), Cancun, Mexico, 2008; 5668–5673.

He M. J.; Cai W. J.; Ni W.; Xie L. H. RNGA based control system configuration for multivariable processes. J. Process Control 2009, 19, 1036–1042. 10.1016/j.jprocont.2009.01.004. DOI

Liao Q.; Sun D. Interaction measures for control configuration selection based on interval type-2 Takagi–Sugeno fuzzy model. IEEE Trans. Fuzzy Syst. 2018, 26, 2510–2523. 10.1109/TFUZZ.2018.2791929. DOI

Navrátil P.; Pekař L.; Matušů R. Control of a multi-variable system using optimal control pairs: A quadruple-tank process. IEEE Access 2020, 8, 2537–2563. 10.1109/ACCESS.2019.2962302. DOI

Niederlinski A. A heuristic approach to the design of linear multivariable interacting control systems. Automatica 1971, 7, 691–701. 10.1016/0005-1098(71)90007-0. DOI

Gorez R.; Klán P. Nonmodel-based explicit design relations for PID controllers. IFAC Proc. Vol. 2000, 33, 133–140. 10.1016/S1474-6670(17)38233-2. DOI

Vítečková M.; Víteček A.; Sladká K.. Controller tuning by desired model method. In 2017 18th International Carpathian Control Conference (ICCC 2017), Sinaia, Romania, 2017; 171–176.

Kučera V. Diophantine equations in control – A survey. Automatica 1993, 29, 1361–1375. 10.1016/0005-1098(93)90003-C. DOI

Grimble M. J.Robust Industrial Control. Optimal Design Approach for Polynomial Systems; Prentice Hall: London, 1994.

System Identification Toolbox . https://www.mathworks.com/help/ident/index.html (accessed Feb 01, 2021).

Bobál V.; Böhm J.; Fesl J.; Macháček J.. Digital Self-tuning Controllers: Algorithms, Implementation and Applications; Springer: London, 2005.

Chapellat H.; Dahleh M.; Bhattacharyya S. P. Robust stability under structured and unstructured perturbations. IEEE Trans. Autom. Control 1990, 35, 1100–1108. 10.1109/9.58552. DOI

Skogestad S.; Postlethwaite I.. Multivariable Feedback Control: Analysis and Design; Wiley: New York, 2007; Vol. 2.

PCI-1711-100 kS/s, 12-bit, 16-ch Universal Multifunction PCI Card – Advantech. https://www.advantech.com/products/1-2mlkc9/pci-1711/mod_b8ef5337-44f0-4c36-9343-ad87d01792d1 (accessed Feb 02, 2021).

Control Web Software System . https://www.mii.cz/cat?id=146&lang=409 (accessed Feb 02, 2021).

Pekař L.; Prokop R. Algebraic robust control of a closed circuit heating-cooling system with a heat exchanger and internal loop delays. Appl. Therm. Eng. 2017, 113, 1464–1474. 10.1016/j.applthermaleng.2016.11.150. DOI