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Heat Transfer Research

Publicou 18 edições por ano

ISSN Imprimir: 1064-2285

ISSN On-line: 2162-6561

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.4 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00072 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.43 SJR: 0.318 SNIP: 0.568 CiteScore™:: 3.5 H-Index: 28

Indexed in

PREDICTION OF THE RATE OF MOISTURE EVAPORATION FROM JAGGERY IN GREENHOUSE DRYING USING THE FUZZY LOGIC

Volume 46, Edição 10, 2015, pp. 923-935
DOI: 10.1615/HeatTransRes.2015007463
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RESUMO

In this study an attempt has been made to predict the rate of moisture evaporation by natural and forced convection from jaggery in a controlled environment. For this purpose, we have adopted simulating software called Fuzzy logic in MATLAB software (Version 7.0.1). Initially the input values from the literature, namely the jaggery temperature (Tj), surface temperature (Te), and the relative humidity (RH), were taken corresponding to different jaggery dimensions (0.03 × 0.03 × 0.01 m3, 0.03 × 0.03 × 0.02 m3, and 0.03 × 0.03 × 0.03 m3) with a total quantity of jaggery of a (0.75 kg and 2.0 kg) that are fed in the software tool box and the output values of moisture evaporation (me) were predicted. These values were compared with the experimental values and it was seen that there is close accuracy between the values. The results from this investigation indicate that the Fuzzy tool predicts the moisture evaporation rate with an absolute error varying from no error in the case of a jaggery piece of dimensions 0.03 × 0.03 × 0.01 m3 to a maximum error of 0.27 for a jaggery piece of 2 kg and of dimensions 0.03 × 0.03 × 0.02 m3 in the forced convection mode. The values of root mean square error and coefficient of determination are calculated and they are found to be 0.112 and 0.986, respectively. Thus it is concluded that the Fuzzy logic can be used to accurately predict the results with a minimum error and the present model can be extended to different places corresponding to different weather conditions, namely ambient temperature, solar radiation, and relative humidity.

CITADO POR
  1. Prakash Om, Laguri Vinod, Pandey Anukul, Kumar Anil, Kumar Arbind, Review on various modelling techniques for the solar dryers, Renewable and Sustainable Energy Reviews, 62, 2016. Crossref

  2. Garduño-García; Ángel, López-Cruz Irineo L., Ruiz-García Agustín, Mathematical modeling of greenhouse solar dryers with natural and forced convection for agricultural products: state of the art, Ingeniería Agrícola y Biosistemas, 9, 1, 2017. Crossref

  3. Prakash Om, Kumar Anil, Sharma Atul, Chapter 11: Solar drying, in Geothermal,Wind and Solar Energy Applications in Agriculture and Aquaculture, 2017. Crossref

  4. Kumar Anil, Deep Harsh, Prakash Om, Ekechukwu O. V., Advancement in Greenhouse Drying System, in Solar Drying Technology, 2017. Crossref

  5. Oueslati Hatem, Mabrouk Salah Ben, Mami Abdelkader, Thermal Modeling of Solar Dryer — Numerical Simulation, Analysis and Performance Evaluation, International Journal of Air-Conditioning and Refrigeration, 26, 04, 2018. Crossref

  6. Zoukit Ahmed, EL Ferouali Hicham, Salhi Issam, Doubabi Said, Abdenouri Naji, Fuzzy modeling of a hybrid solar dryer: experimental validation, Journal of Energy Systems, 2019. Crossref

  7. Prakash Om, Fuzzy Prediction Model for Water Temperature in Scheffler Solar Reflector, in Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017), 476, 2019. Crossref

  8. Taghinezhad Ebrahim, Szumny Antoni, Kaveh Mohammad, Rasooli Sharabiani Vali, Kumar Anil, Shimizu Naoto, Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment, Foods, 9, 1, 2020. Crossref

  9. Sridharan M., Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer, Journal of Solar Energy Engineering, 142, 3, 2020. Crossref

  10. Hosseinpour Soleiman, Martynenko Alex, Application of fuzzy logic in drying: A review, Drying Technology, 40, 5, 2022. Crossref

  11. Vanegas-Ayala Sebastian-Camilo, Barón-Velandia Julio, Leal-Lara Daniel-David, Troussas Christos, A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems, Advances in Human-Computer Interaction, 2022, 2022. Crossref

  12. Bouraoui Chaima, Debenest Gérald, Nejma Fayçal Ben, Numerical performance assessment of a solar greenhouse dryer for the drying of Olive Mill Wastewater, Journal of Thermal Analysis and Calorimetry, 147, 15, 2022. Crossref

  13. Ahmad Asim, Prakash Om, Kumar Anil, Chatterjee Rajeshwari, Sharma Shubham, Kumar Vineet, Kulshreshtha Kushagra, Li Changhe, Eldin Elsayed Mohamed Tag, A Comprehensive State-of-the-Art Review on the Recent Developments in Greenhouse Drying, Energies, 15, 24, 2022. Crossref

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