Generic placeholder image

Current Physical Chemistry

Editor-in-Chief

ISSN (Print): 1877-9468
ISSN (Online): 1877-9476

Research Article

Modelling to Predict Moisture Ratio in Infrared Drying of Machine Plaster by Particle Swarm Optimization

Author(s): Mehmet Kalender*, Mahmut Temel Özdemir and Hasan Güler

Volume 10, Issue 2, 2020

Page: [126 - 135] Pages: 10

DOI: 10.2174/1877946810666200218092820

Abstract

Background: Gypsum plaster is one of the most important building materials. The use of gypsum plasters is very common due to their many advantages. The drying process is an important stage in the production of gypsum materials and applications. Modelling of drying phenomenon can benefit drying technology. Recently, Particle Swarm Optimization (PSO) technique has been used to obtain optimum model equations for drying processes.

Objective: The aim of this study was to determine a new modeling approach to infrared drying of machine plaster by using PSO.

Methods: Experimental studies supplied by previous work in the literature have been performed by a laboratory scale infrared dryer in the temperature range of 50-70°C and at atmospheric conditions. Experimental moisture ratio values were compared with various mathematical model equations developed for the drying process by using Particle Swarm Optimization (PSO) technique.

Results: Fitting tests indicate that the results obtained from the PSO technique are better than those of the previous study because of lower χ2, RMSE, and RSS values. The best model equation was the model equation based on the Newton drying equation existing in the previous study. However, the model equation derived by Modified Page has been determined as the most compatible model with the experimental data.

Conclusion: It can be said that PSO is successively and reliably used to predict or optimize the experimental data of drying phenomena.

Keywords: PSO, infrared drying, construction, building, machine plaster, modeling.

Graphical Abstract

[1]
Merritt, F.S.; Ricketts, J.T. Building design and construction handbook, 6th ed; McGraw-Hill: New York, 2001.
[2]
Pillai, M.G. Thin layer drying kinetics, characteristics and modeling of plaster of Paris. Chem. Eng. Res. Des., 2013, 91, 1018-1027.
[http://dx.doi.org/10.1016/j.cherd.2013.01.005]
[3]
Pundir, A.; Garg, M.; Singh, R. Evaluation of properties of gypsum plaster-superplasticizer blends of improved performance. J. Build. Eng., 2015, 4, 223-230.
[http://dx.doi.org/10.1016/j.jobe.2015.09.012]
[4]
Margarido, F.; Gonçalves, M.C. Materials for construction and civil engineering; Springer: London, 2015.
[5]
Van Belleghem, M.; Steeman, M.; Janssens, A.; De Paepe, M. Drying behaviour of calcium silicate. Constr. Build. Mater., 2014, 65, 507-517.
[http://dx.doi.org/10.1016/j.conbuildmat.2014.04.129]
[6]
McCabe, W.L.; Smith, J.C.; Harriott, P. Unit operations of chemical engineering McGraw-Hill: New York, 1993; 5, 154, .
[7]
Toğrul, H. Suitable drying model for infrared drying of carrot. J. Food Eng., 2006, 77, 610-619.
[http://dx.doi.org/10.1016/j.jfoodeng.2005.07.020]
[8]
Franzen, C.; Zötzl, M.; Trommler, U.; Hoyer, C.; Holzer, F.; Höhlig, B. Microwave and radio wave supported drying as new options in flood mitigation of imbued decorated historic masonry. J. Cult. Herit., 2016, 21, 751-758.
[http://dx.doi.org/10.1016/j.culher.2016.04.007]
[9]
Ganesap ı̇llaı̇, M.; Regupathi, I.; Murugesan, T. An empirical model for the estimation of moisture ratio during microwave drying of plaster of Paris. Dry. Technol., 2008, 26, 963-978.
[http://dx.doi.org/10.1080/07373930802142978]
[10]
Celma, A.R.; Cuadros, F.; López-Rodríguez, F. Characterization of industrial tomato by-products from infrared drying process. Food Bioprod. Process., 2009, 87, 282-291.
[http://dx.doi.org/10.1016/j.fbp.2008.12.003]
[11]
Fenton, G.; Kennedy, M. Rapid dry weight determination of kiwifruit pomace and apple pomace using an infrared drying technique. NZ. J. Crop Hortic. Sci., 1998, 26, 35-38.
[http://dx.doi.org/10.1080/01140671.1998.9514037]
[12]
Dhib, R. Infrared drying: From process modeling to advanced process control. Dry. Technol., 2007, 25, 97-105.
[http://dx.doi.org/10.1080/07373930601160908]
[13]
Riadh, M.H.; Ahmad, S.A.B.; Marhaban, M.H.; Soh, A.C. Infrared heating in food drying: An overview. Dry. Technol., 2015, 33, 322-335.
[http://dx.doi.org/10.1080/07373937.2014.951124]
[14]
Arsoy, S. Temperature-controlled infrared drying characteristics of soils. Dry. Technol., 2008, 26, 1477-1483.
[http://dx.doi.org/10.1080/07373930802412165]
[15]
Saïd, M.N.A. Measurement methods of moisture in building envelopes–A literature review. Int. J. Archit. Herit., 2007, 1, 293-310.
[http://dx.doi.org/10.1080/15583050701476754]
[16]
Bennamoun, L.; Kahlerras, L.; Mi chel, F.; Courard, L.; Salmon, T.; Fraı̇ kı̇n, L. Determination of moisture diffusivity during drying of mortar cement: Experimental and modeling study. Int. J. Energy Eng., 2013, 3, 1-6.
[17]
Seck, M.D.; Keita, E.; Faure, P.; Cavalié, P.; Van Landeghem, M.; Rodts, S.; Coussot, P. Subflorescence and plaster drying dynamics. Chem. Eng. Sci., 2016, 148, 203-211.
[http://dx.doi.org/10.1016/j.ces.2016.04.012]
[18]
Toğrul, H. Simple modeling of infrared drying of fresh apple slices. J. Food Eng., 2005, 71, 311-323.
[http://dx.doi.org/10.1016/j.jfoodeng.2005.03.031]
[19]
Doymaz, I.; Pala, M. Hot-air drying characteristics of red pepper. J. Food Eng., 2002, 55, 331-335.
[http://dx.doi.org/10.1016/S0260-8774(02)00110-3]
[20]
Lewis, W.K. The rate of drying of solid materials. Ind. Eng. Chem., 1921, 13, 427-432.
[http://dx.doi.org/10.1021/ie50137a021]
[21]
Verma, L.R.; Bucklin, R.A.; Endan, J.B.; Wratten, F.T. Effects of drying air parameters on rice drying models. Trans. ASAE, 1985, 28, 296-0301.
[http://dx.doi.org/10.13031/2013.32245]
[22]
Yaldiz, O.; Ertekin, C.; Uzun, H. Mathematical modeling of thin layer solar drying of sultana grapes. Energy, 2001, 26, 457-465.
[http://dx.doi.org/10.1016/S0360-5442(01)00018-4]
[23]
Mı̇ dı̇ llı̇ , A.; Kucuk, H.; Yapar, Z. A new model for single-layer drying. Dry. Technol., 2002, 20, 1503-1513.
[http://dx.doi.org/10.1081/DRT-120005864]
[24]
Ganesapi llaı̇ , M.; Regupathi, I.; Murugesan, T. Characterization and process optimization of microwave drying of plaster of Paris. Dry. Technol., 2008, 26, 1484-1496.
[http://dx.doi.org/10.1080/07373930802412199]
[25]
Zı̇ elı̇nska, M.; Markowski, M. Air drying characteristics and moisture diffusivity of carrots. Chem. Eng. Process.: Process Intensif., 2010, 49, 212-218.
[http://dx.doi.org/10.1016/j.cep.2009.12.005]
[26]
Vası̇ć, M.; Grbavčı̇ć, Ž.; Radojevı̇ć, Z. Determination of the moisture diffusivity coefficient and mathematical modeling of drying. Chem. Eng. Process.: Process Intensif., 2014, 76, 33-44.
[http://dx.doi.org/10.1016/j.cep.2013.12.003]
[27]
Rojas, S.; Lopez-Rodriguez, F. Mathematical modelling of thin-layer infrared drying of wet olive husk. Chem. Eng. Process.: Process Intensif., 2008, 47, 1810-1818.
[http://dx.doi.org/10.1016/j.cep.2007.10.003]
[28]
Sander, A. Thin-layer drying of porous materials: Selection of the appropriate mathematical model and relationships between thin-layer models parameters. Chem. Eng. Process.: Process Intensif., 2007, 46, 1324-1331.
[http://dx.doi.org/10.1016/j.cep.2006.11.001]
[29]
Aghbashlo, M.; Hosseinpour, S. Mujumdar, A.S. Application of Artificial Neural Networks (ANNs) in drying technology: A comprehensive review. Dry. Technol., 2015, 33, 1397-1462.
[http://dx.doi.org/10.1080/07373937.2015.1036288]
[30]
Sablani, S.; Mujumdar, A. An artificial neural network model for prediction of drying rates. Dry. Technol., 2003, 21, 1867-1884.
[http://dx.doi.org/10.1081/DRT-120025512]
[31]
Youssefi, S.; Emam-Djomeh, Z.; Mousavi, S. Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice. Dry. Technol., 2009, 27, 910-917.
[http://dx.doi.org/10.1080/07373930902988247]
[32]
Sarimeseli, A.; Coskun, M.A.; Yuceer, M. Modeling microwave drying kinetics of thyme (Thymus Vulgaris L.) leaves using ANN methodology and dried product quality. J. Food Process. Preserv., 2014, 38, 558-564.
[http://dx.doi.org/10.1111/jfpp.12003]
[33]
Suzaki, P.Y.R.; Munaro, M.T.; Triques, C.C.; Kleinübing, S.J.; Klen, M.R.F.; de Matos Jorge, L.M.; Bergamasco, R. Biosorption of binary heavy metal systems: Phenomenological mathematical modeling. Chem. Eng. J., 2017, 313, 364-373.
[http://dx.doi.org/10.1016/j.cej.2016.12.082]
[34]
Fiorentin, L.D.; Trigueros, D.E.; Módenes, A.N.; Espinoza-Quiñones, F.R.; Pereira, N.C.; Barros, S.T.; Santos, O.A. Biosorption of reactive blue 5G dye onto drying orange bagasse in batch system: Kinetic and equilibrium modeling. Chem. Eng. J., 2010, 163, 68-77.
[http://dx.doi.org/10.1016/j.cej.2010.07.043]
[35]
Módenes, A.N.; Kroumov, A.D.; Espinoza-Quiñones, F.R. Modeling of biodegradation process of BTEX compounds: Kinetic parameters estimation by using particle swarm global optimizer. Process Biochem., 2010, 45, 1355-1361.
[http://dx.doi.org/10.1016/j.procbio.2010.05.007]
[36]
Khajeh, M.; Sarafraz-Yazdi, A.; Moghadam, A.F. Modeling of solid-phase tea waste extraction for the removal of manganese and cobalt from water samples by using PSO-artificial neural network and response surface methodology. Arab. J. Chem., 2017, 10, pp S1663-S1673.
[37]
Romdhana, H.; Bonazzi, C.; Esteban-Decloux, M. Computer-aided process engineering for environmental efficiency: Industrial drying of biomass. Dry. Technol., 2016, 34, 1253-1269.
[http://dx.doi.org/10.1080/07373937.2015.1104348]
[38]
Vitor, J.F.D.A.; Gomes, M.M.R.D.C. Estimation of coefficients of fluidized bed drying through the PSO and GA metaheuristic approaches. Dry. Technol., 2011, 29, 848-862.
[http://dx.doi.org/10.1080/07373937.2010.542263]
[39]
Shokouhmand, H.; Abdollahi, V.; Hosseini, S.; Vahidkhah, K. Performance optimization of a brick dryer using porous simulation approach. Dry. Technol., 2011, 29, 360-370.
[http://dx.doi.org/10.1080/07373937.2010.497954]
[40]
Kalender, M. Thin layer infrared drying kinetics, characteristics, and modelling of machine plaster. Fırat Univ. J. Eng. (Stevenage), 2017, 29, 287-293.
[41]
Christie, J. Transport processes and separation process principles; Prentice Hall Professional Technical Reference: Indiana, 2003.
[42]
Eberhart, R.; Kennedy, J. A new optimizer using particle swarm theory. Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.
[http://dx.doi.org/10.1109/MHS.1995.494215]

© 2024 Bentham Science Publishers | Privacy Policy