Ahmed ZOUKİT, Hicham EL FEROUALİ, İssam SALHİ, Said DOUBABİ, Naji ABDENOURİ

2210

Fuzzy modeling of a hybrid solar dryer: experimental validation

Fuzzy modeling of a hybrid solar dryer: experimental validation

A Takagi Sugeno fuzzy (TSF) modeling of an indirect hybrid solar-electrical dryer operated in forced convection (0.027 kg/s) was developed. The hybrid dryer was considered as a nonlinear and uncertain system where the operating point varies with weather conditions and airflow. The proposed TSF model was used to predict the drying temperature in no load conditions. Different experimental measurements were set up and used for evaluating the reliability of this model. At first and before applying this method to hybrid solar-electrical dryer, the TSF modeling was tested on solar mode and electrical mode where only one energy source was considered in each mode. The proposed model was experimentally validated in two main modes of the dryer operation: solar mode and electrical mode. The predicted behavior was closed to the experimental data with Root Mean Square Error (RMSE) of 2.34 and 2.21 in solar mode and electrical mode, respectively. The obtained predicted behavior confirms the pertinence of the identified model. The TSF model of the dryer leads to predict the drying temperature instantly with a huge reduction in simulation time in comparison with other modeling techniques. Thus, it is useful for synthesizing a control system of the drying parameters.
Keywords:

Solar energy, Hybrid dryer, Drying temperature, Takagi Sugeno fuzzy modeling,

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  • Frakas, I, Seres, I, Meszaros, C. Analytical and experimental study of a modular solar dryer. Renewable energy 1999; 16: 773-778. DOI: https://doi.org/10.1016/S0960-1481(98)00278-X.
  • Belloulid, M, O, Hamdi, H, Mandi, L, Ouazzani, N. Solar drying of wastewater sludge: a case study in Marrakesh, Morocco. Environmental Technology (United Kingdom) 2018; 0(0): 1–7. DOI: 10.1080/09593330.2017.1421713.
  • Chan, Y, Dyah, N, Abdullah, K. Performance of a recirculation type integrated collector drying chamber (ICDC) solar dryer. Energy Procedia 2015; 68: 53-59. DOI: https://doi.org/10.1016/j.egypro.2015.03.232.
  • Lopez-Vidana, E, C, Mendez-Lagunas, LL, Rodriguez-Ramirez, J. Efficiency of a hybrid solar-gas dryer. Solar energy 2013; 93: 23-31. DOI: https://doi.org/10.1016/j.solener.2013.01.027.
  • Kumar, A, Singh, R, Prakash, O. Review of global solar drying status. Agric. Eng. Int 2014; 16.
  • Prakash, O, Kumar, A, Laguri, V. Performance of modified greenhouse dryer with thermal energy storage. Energy reports 2016; 2: 155-162. DOI: https://doi.org/10.1016/j.egyr.2016.06.003.
  • Boughali, S, Benmoussa, H, Bouchekima, B, Mennouche, D, Bouguettaia, H, Bechki, D. Crop drying of an indirect active hybrid Solar-Electrical dryer in eastern Algerian Septentrional Sahara. Solar energy 2009; 83: 2223-2232. DOI: https://doi.org/10.1016/j.solener.2009.09.006.
  • Alejandro, R, Andrea, M, Francisco, C, Pedro, H. Mushroom dehydration in a hybrid solar dryer. Energy conversion and management 2013; 70: 31-39. DOI: https://doi.org/10.1016/j.enconman.2013.01.032.
  • Augustus Leon, M, Kumar, S. Design and performance evaluation of a solar assisted biomass drying system with thermal storage. Drying technology 2008; 26: 936-947. DOI: https://doi.org/10.1080/07373930802142812.
  • Ferreira, A, G, Charbel, A, L, T, Peres, R, L, Silva, J, G, Maia, C, B. Experimental analysis of a hybrid dryer. Thermal engineering 2007; 6: 03-07.
  • Onat, M, Koten, H, Celik, H. Constant Temperature Control with a Fast Transient Response for a Gel Card Incubator, 6th Eur. Conf. Ren. Energy Sys. 25-27 June 2018, Istanbul, Turkey.
  • Prakash, O, Laguri, V, Pandey, A, Kumar, A. Review on various modelling techniques for the solar dryers. Renew. Sustain. Energy Rev 2016; 62: 396-417. DOI: https://doi.org/10.1016/j.rser.2016.04.028.
  • Seo, S, Kim, Y, Choi, H, H. Model predictive controller design for boost DC – DC converter using T – S fuzzy cost function. Int. J. Electron 2017; 104: 838–1853. DOI: https://doi.org/10.1080/00207217.2017.1329945.
  • Tatli, H, ŞEN, Z. Prediction of Daily Maximum Temperatures Via Fuzzy Sets. Turkish Journal of Engineering and Environmental Sciences 2014; 25 (1): 1-9.
  • Ibrahim, S. A, Ahmet Sahiner, A, Ibrahim, A. A. Fuzzy Logic Modeling for Prediction of the Nuclear Tracks; Journal of Multidisciplinary Modeling and Optimization 2018; 1: 33-40.
  • Tütmez, B, Tercan, E. Use of Fuzzy Modeling Approach in Grade Estimation; Scientific Mining Journal 2006; 45: 39-47.
  • Laouafi, A, Mordjaoui, M, Boukelia T, E. An adaptive neuro-fuzzy inference system-based approach for daily load curve prediction. Journal of energy system 2018; 2(3): 115-126.
  • Prakash, O, Kumar, A, ANFIS modelling of natural convection greenhouse drying system for jiggery: an experimental validation. Int. J. Sustain. Energy 2014; 33: 316-335. DOI: https://doi.org/10.1080/14786451.2012.724070.
  • Prakash, O, Kumar, A, Kaviti, AK, Kumar, PV. Prediction of the rate of moisture evaporation from jiggery in greenhouse drying using the fuzzy logic. Heat Transf. Res 2015; 46.
  • Prakash, O, Kumar, A. ANFIS prediction model of a modified active greenhouse dryer in no-load condition in the month of January. Int. J. Adv.Comput. Res 2013; 3: 220-223.
  • Singh, S, Kumar, S. Testing method for thermal performance based rating of various solar dryer designs. Solar energy 2012; 86: 87-98. DOI: https://doi.org/10.1016/j.solener.2011.09.009.
  • Takagi, T, Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. IEEE trans 1985; 15: 116-132. DOI: 10.1109/TSMC.1985.6313399.
  • Zhu, B, He, CZ, Liatsis, P, Li, XY. A GMDH-based fuzzy modeling approach for constructing TS model. Fuzzy Sets Syst 2012 ; 189 : 19-29. DOI: https://doi.org/10.1016/j.fss.2011.08.004.
  • Du, H, Zhang, N. Application of evolving Takagi-Sugeno fuzzy model to linear system identification. App. Soft Comput J 2008; 8: 676-686. DOI: 10.1016/j.asoc.2007.05.006.
  • Johansen, T, Shorten, R, Murray-Smith, R. On the interpretation and identification of dynamic (Takagi-Sugeno) fuzzy models. IEEE transaction 2000; 8: 297-313. DOI: 10.1109/91.855918.
  • El Hamdaoui, A, Salhi, I, Belattar, A, Doubabi, S. Modeling a three-phase micro hydropower plant prototype using Takagi Sugeno fuzzy approach. International journal of hydrogen energy 2017; 42: 575-581. DOI: https://doi.org/10.1016/j.ijhydene.2017.02.167.
Journal of Energy Systems-Cover
  • Başlangıç: 2017
  • Yayıncı: Erol KURT
Arşiv
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Fuzzy modeling of a hybrid solar dryer: experimental validation

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