Fuzzy modeling of a hybrid solar dryer: experimental validation

Yıl 2019, Cilt: 3 Sayı: 1, 1 - 13, 31.03.2019

Ahmed Zoukit , Hicham El Ferouali İssam Salhi Said Doubabi Naji Abdenouri

https://doi.org/10.30521/jes.457645

Cited By: 10

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• 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.