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Drying Technology
An International Journal
Volume 33, 2015 - Issue 12
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Review Article

Application of Artificial Neural Networks (ANNs) in Drying Technology: A Comprehensive Review

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REFERENCES

  • Movagharnejad, K.; Nikzad, M. Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture 2007, 59(1), 78–85.
  • Ghasemi-Varnamkhasti, M.; Aghbashlo, M. Electronic nose and electronic mucosa as innovative instruments for real-time monitoring of food dryers. Trends in Food Science & Technology 2014, 38(2), 158–166.
  • Aghbashlo, M.; Mobli, H.; Rafiee, S.; Madadlou, A. The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study. Computers and Electronics in Agriculture 2012, 88, 32–43.
  • Bishop, C.M. Neural Networks for Pattern Recognition; Oxford University Press: Oxford, UK, 1995.
  • Ripley, B.D. Pattern Recognition and Neural Networks; Cambridge University Press: Cambridge, UK, 1996.
  • Priddy, K.L.; Keller, P.E. Artificial Neural Networks: An Introduction; SPIE Press: Bellingham, WA, 2005.
  • Yegnanarayana, B. Artificial Neural Networks; Prentice-Hall: New Delhi, 2009.
  • Hassoun, M.H. Fundamentals of Artificial Neural Networks; MIT Press: Cambridge, MA, 2010.
  • Aghbashlo, M.; Kianmehr, M.H.; Samimi-Akhijahani, H. Evaluation of thin-layer drying models for describing drying kinetics of Barberries (Barberries Vulgaris). Journal of Food Process Engineering 2009, 32(2), 278–293.
  • Kucuk, H.; Midilli, A.; Kilic, A.; Dincer, I. A review on thin-layer drying-curve equations. Drying Technology 2014, 32(7), 757–773.
  • Jinescu, G.; Lavric, V. The artificial neural networks and the drying process modeling. Drying Technology 1995, 13(5–7), 1579–1586.
  • Kamińnski, W.; Strumitto, P.; Tomczak, E. Genetic algorithms and artificial neural networks for description of thermal deterioration processes. Drying Technology 1996, 14(9), 2117–2133.
  • Trelea, I.C.; Courtois, F.; Trystram, G. Dynamic models for drying and wet-milling quality degradation of corn using neural networks. Drying Technology 1997, 15(3–4), 1095–1102.
  • Hussain, M.A.; Rahman, M.S.; Ng, C.W. Prediction of pores formation (porosity) in foods during drying: Generic models by the use of hybrid neural network. Journal of Food Engineering 2002, 51(3), 239–248.
  • Sander, A.; Skansi, D.; Bolf, N. Heat and mass transfer models in convection drying of clay slabs. Ceramics International 2003, 29(6), 641–653.
  • Islam, M.R.; Sablani, S.S.; Mujumdar, A.S. An artificial neural network model for prediction of drying rates. Drying Technology 2003, 21(9), 1867–1884.
  • Hernandez-Perez, J.A.; Garcıa-Alvarado, M.A.; Trystram, G.; Heyd, B. Neural networks for the heat and mass transfer prediction during drying of cassava and mango. Innovative Food Science & Emerging Technologies 2004, 5(1), 57–64.
  • Kaminski, W.; Tomczak, E.; Skorupska, E. Estimation of the effect of shape and temperature on drying kinetics using MLP. Drying Technology 2004, 22(1–2), 191–200.
  • Erenturk, K.; Erenturk, S.; Tabil, L.G. A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: Regression analysis and neural network. Computers and Electronics in Agriculture 2004, 45(1), 71–90.
  • Wu, H.; Avramidis, S. Prediction of timber kiln drying rates by neural networks. Drying Technology 2006, 24(12), 1541–1545.
  • Martynenko, A.I.; Yang, S.X. Biologically inspired neural computation for ginseng drying rate. Biosystems Engineering 2006, 95(3), 385–396.
  • Martynenko, A.; Yang, S.X.; Pan, L. Intelligent computation of moisture content in shrinkable biomaterials. Drying Technology 2007, 25(10), 1667–1676.
  • Erenturk, S.; Erenturk, K. Comparison of genetic algorithm and neural network approaches for the drying process of carrot. Journal of Food Engineering 2007, 78(3), 905–912.
  • Avramidis, S.; Wu, H. Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. Holz als Roh-und Werkstoff 2007, 65(2), 89–93.
  • Lertworasirikul, S. Drying kinetics of semi-finished cassava crackers: A comparative study. LWT-Food Science and Technology 2008, 41(8), 1360–1371.
  • Lertworasirikul, S.; Tipsuwan, Y. Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network. Journal of food Engineering 2008, 84(1), 65–74.
  • Ceylan, I.; Aktaş, M. Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks. Applied Energy 2008, 85(9), 841–854.
  • Ceylan, I. Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology 2008, 26(12), 1469–1476.
  • Khazaei, J.; Chegini, G.R.; Bakhshiani, M. A novel alternative method for modeling the effects of air temperature and slice thickness on quality and drying kinetics of tomato slices: Superposition technique. Drying Technology 2008, 26(6), 759–775.
  • Hernández, J.A. Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse. Food Control 2009, 20(4), 435–438.
  • Omid, M.; Baharlooei, A.; Ahmadi, H. Modeling drying kinetics of pistachio nuts with multilayer feed-forward neural network. Drying Technology 2009, 27(10), 1069–1077.
  • Mohebbi, M.; Akbarzadeh-T, M.R.; Shahidi, F.; Moussavi, M.; Ghoddusi, H.B. Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp. Computers and Electronics in Agriculture 2009, 69(2), 128–134.
  • Khoshhal, A.; Dakhel, A.A.; Etemadi, A.; Zereshki, S. Artificial neural network modeling of apple drying process. Journal of Food Process Engineering 2010, 33(s1), 298–313.
  • Aghbashlo, M.; Kianmehr, M.H.; Nazghelichi, T.; Rafiee, S. Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm. Drying Technology 2011, 29(7), 770–779.
  • Boeri, C.; Neto da Silva, F.; Ferreira, J.; Saraiva, J.; Salvador, Â. Predicting the drying kinetics of salted codfish (Gadus Morhua): Semi‐empirical, diffusive and neural network models. International Journal of Food Science & Technology 2011, 46(3), 509–515.
  • Singh, N.J. Neural network approaches for prediction of drying kinetics during drying of sweet potato. Agricultural Engineering International: CIGR Journal 2011, 13(1), 1–7.
  • Balbay, A.; Şahin, Ö.; Karabatak, M. An investigation of drying process of shelled pistachios in a newly designed fixed bed dryer system by using artificial neural network. Drying Technology 2011, 29(14), 1685–1696.
  • Gorjian, S.; Tavakkoli Hashjin, T.; Khoshtaghaza, M.H. Designing and optimizing a back propagation neural network to model a thin-layer drying process. International Agrophysics 2011, 25, 13–19.
  • Karimi, F.; Rafiee, S. Optimization of air drying process for lavender leaves. International Agrophysics 2011, 25, 229–239.
  • Karimi, F.; Rafiee, S.; Taheri-Garavand, A.; Karimi, M. Optimization of an air drying process for Artemisia absinthium leaves using response surface and artificial neural network models. Journal of the Taiwan Institute of Chemical Engineers 2012, 43(1), 29–39.
  • Tavakolipour, H.; Mokhtarian, M. Neural network approaches for prediction of pistachio drying kinetics. International Journal of Food Engineering 2012, 8(3), 1–17.
  • Balbay, A.; Avci, E.; Şahin, Ö.; Coteli, R. Modeling of drying process of bittim nuts (Pistacia Terebinthus) in a fixed bed dryer system by using extreme learning machine. International Journal of Food Engineering 2012, 8(4), 1–18.
  • Saraceno, A.; Aversa, M.; Curcio, S. Advanced modeling of food convective drying: A comparison between artificial neural networks and hybrid approaches. Food and Bioprocess Technology 2012, 5(5), 1694–1705.
  • Aghajani, N.; Kashaninejad, M.; Dehghani, A.A.; Daraei Garmakhany, A. Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Quality Assurance and Safety of Crops & Foods 2012, 4(2), 93–101.
  • Samadi, S.H.; Ghobadian, B.; Najafi, G.; Motevali, A.; Faal, S. Drying of apple slices in combined heat and power (CHP) dryer: Comparison of mathematical models and neural networks. Chemical Product and Process Modeling 2013, 8(1), 41–52.
  • Rodríguez, J.; Clemente, G.; Sanjuán, N.; Bon, J. Modelling drying kinetics of thyme (Thymus vulgari s L.): Theoretical and empirical models, and neural networks. Food Science and Technology International 2013, 20(1), 13–22.
  • Perazzini, H.; Freire, F.B.; Freire, J.T. Drying kinetics prediction of solid waste using semi‐empirical and artificial neural network models. Chemical Engineering & Technology 2013, 36(7), 1193–1201.
  • Murthy, T.P.K.; Manohar, B. Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network. Journal of Food Science and Technology 2014, 51(12), 3712–3721.
  • Ninchuewong, T.; Tirawanichakul, S.; Tirawanichakul, Y. Empirical model and artificial neural network model approach for air dried sheets (ADS) rubber. Advanced Materials Research 2013, 622, 69–74.
  • Taheri-Garavand, A.; Rafiee, S.; Keyhani, A.; Javadikia, P. Modeling of basil leaves drying by GA–ANN. International Journal of Food Engineering 2013, 9(4), 393–401.
  • Rodríguez, J.; Ortuno, C.; Benedito, J.; Bon, J. Optimization of the antioxidant capacity of thyme (Thymus vulgaris L.) extracts: Management of the drying process. Industrial Crops and Products 2013, 46, 258–263.
  • Nadian, M.H.; Rafiee, S.; Aghbashlo, M.; Hosseinpour, S.; Mohtasebi, S.S. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food and Bioproducts Processing 2015, 94, 263–274.
  • Guiné, R.P.; Cruz, A.C.; Mendes, M. Convective drying of apples: Kinetic study, evaluation of mass transfer properties and data analysis using artificial neural networks. International Journal of Food Engineering 2014, 10(2), 281–299.
  • Tavakolipour, H.; Mokhtarian, M.; Kalbasi‐Ashtari, A. Intelligent monitoring of zucchini drying process based on fuzzy expert engine and ANN. Journal of Food Process Engineering 2014, 37(5), 474–481.
  • Mokhtarian, S.; Koushki, F.; Bakhshabadi, H.; Askari, B.; Daraei Garmakhany, A.; Rashidzadeh, S.H. Feasibility investigation of using artificial neural network in process monitoring of pumpkin air drying. Quality Assurance and Safety of Crops & Foods 2014, 6(2), 191–199.
  • Akyol, U.; Erhan Akan, A.; Durak, A. Simulation and thermodynamic analysis of a hot-air textile drying process. The Journal of the Textile Institute 2015, 106(3), 260–274.
  • Hosseinpour, S.; Rafiee, S.; Aghbashlo, M.; Mohtasebi, S.S. Computer vision system (CVS) for in-line monitoring of visual texture kinetics during shrimp (Penaeus spp.) drying. Drying Technology 2015, 33(2), 238–254.
  • Ozsahin, S.; Aydin, I. Prediction of the optimum veneer drying temperature for good bonding in plywood manufacturing by means of artificial neural network. Wood Science and Technology 2014, 48(1), 59–70.
  • Martínez-Martínez, V.; Gomez-Gil, J.; Stombaugh, T.S.; Montross, M.D.; Aguiar, J.M. Moisture content prediction in the switchgrass (Panicum virgatum) drying process using artificial neural networks. Drying Technology. In press. DOI:10.1080/07373937.2015.1005228.
  • Martynenko, A.; Kudra, T. Non-isothermal drying of medicinal plants. Drying Technology. In press. DOI:10.1080/07373937.2015.1010209.
  • Hodgkin, A.L.; Huxley, A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 1952, 117(4), 500–544.
  • Aghbashlo, M.; Sotudeh-Gharebagh, R.; Zarghami, R.; Mujumdar, A.S.; Mostoufi, N. Measurement techniques to monitor and control fluidization quality in fluidized bed dryers: A review. Drying Technology 2014, 32(9), 1005–1051.
  • Balasubramanian, A.; Panda, R.C.; Ramachandra Rao, V.S. Modelling of a fluidized bed drier using artificial neural network. Drying Technology 1996, 14(7–8), 1881–1889.
  • Cubillos, F.A.; Alvarez, P.I.; Pinto, J.C.; Lima, E.L. Hybrid-neural modeling for particulate solid drying processes. Powder Technology 1996, 87(2), 153–160.
  • Ramesh, M.N.; Kumar, M.A.; Rao, P.S. Application of artificial neural networks to investigate the drying of cooked rice. Journal of Food Process Engineering 1996, 19(3), 321–329.
  • Zbiciński, I.; Strumiłło, P.; Kamiński, W. Hybrid neural model of thermal drying in a fluidized bed. Computers & Chemical Engineering 1996, 20, S695–S700.
  • Zbiciński, I.; Kamiński, W.; Ciesielski, K.; Strumiłło, P. Dynamic and hybrid neural model of thermal drying in a fluidized bed. Drying Technology 1997, 15(6–8), 1743–1752.
  • Watano, S.; Sato, Y.; Miyanami, K. Application of a neural network to granulation scale-up. Powder Technology 1997, 90(2), 153–159.
  • Kamiński, W.; Stawczyk, J.; Tomczak, E. Presentation of drying kinetics in a fluidized bed by means of radial basis functions. Drying Technology 1997, 15(6–8), 1753–1762.
  • Kamiński, W.; Tomczak, E.; Strumill, P. Neurocomputing approaches to modelling of drying process dynamics. Drying Technology 1998, 16(6), 967–992.
  • Kaminski, W.; Tomczak, E. An integrated neural model for drying and thermal degradation of selected products. Drying Technology 1999, 17(7–8), 1291–1301.
  • Zbiciński, I.; Ciesielski, K. Extension of the neural networks operating range by the application of dimensionless numbers in prediction of heat transfer coefficients. Drying Technology 2000, 18(3), 649–660.
  • Kaminski, W.; Tomczak, E. Degradation of ascorbic acid in drying process: A comparison of description methods. Drying Technology 2000, 18(3), 777–790.
  • Woinaroschy, A.; Jinescu, G.; Tebrencu, C. Application of neural nets for optimization of vibro‐fluidization drying. Chemical Engineering & Technology 2000, 23(2), 130–132.
  • Palancar, M.C.; Aragón, J.M.; Castellanos, J.A. Neural network model for fluidised bed dryers. Drying Technology 2001, 19(6), 1023–1044.
  • Panda, R.C.; Zank, J.; Martin, H. Modeling the droplet deposition behavior on a single particle in fluidized bed spray granulation process. Powder Technology 2001, 115(1), 51–57.
  • Rantanen, J.; Räsänen, E.; Antikainen, O.; Mannermaa, J.P.; Yliruusi, J. In-line moisture measurement during granulation with a four-wavelength near-infrared sensor: An evaluation of process-related variables and a development of non-linear calibration model. Chemometrics and Intelligent Laboratory Systems 2001, 56(1), 51–58.
  • Ciesielski, K.; Zbiciński, I. Hybrid neural modelling of fluidised bed drying process. Drying Technology 2001, 19(8), 1725–1738.
  • Castellanos, J.A.; Palancar, M.C.; Aragón, J.M. Designing and optimizing a neural network for the modeling of a fluidized-bed drying process. Industrial & Engineering Chemistry Research 2002, 41(9), 2262–2269.
  • Cubillos, F.; Reyes, A. Drying of carrots in a fluidized bed.II. Design of a model based on a modular neural network approach. Drying Technology 2003, 21(7), 1185–1196.
  • Behzadi, S.S.; Klocker, J.; Hüttlin, H.; Wolschann, P.; Viernstein, H. Validation of fluid bed granulation utilizing artificial neural network. International Journal of Pharmaceutics 2005, 291(1), 139–148.
  • Torrecilla, J.S.; Aragón, J.M.; Palancar, M.C. Modeling the drying of a high-moisture solid with an artificial neural network. Industrial & Engineering Chemistry Research 2005, 44(21), 8057–8066.
  • Satish, S.; Pydi Setty, Y. Modeling of a continuous fluidized bed dryer using artificial neural networks. International Communications in Heat and Mass Transfer 2005, 32(3), 539–547.
  • Alvarez, P.I.; Blasco, R.; Gomez, J.; Cubillos, F.A. A first principles–neural networks approach to model a vibrated fluidized bed dryer: Simulations and experimental results. Drying Technology 2005, 23(1–2), 187–203.
  • Yüzgeç, U.; Becerikli, Y.; Türker, M. Dynamic neural-network-based model-predictive control of an industrial baker's yeast drying process. IEEE Transactions on Neural Networks, 2008, 19(7), 1231–1242.
  • Köni, M.; Türker, M.; Yüzgeç, U.; Dinçer, H.; Kapucu, H. Adaptive modeling of the drying of baker's yeast in a batch fluidized bed. Control Engineering Practice 2009, 17(4), 503–517.
  • Köni, M.; Yüzgeç, U.; Türker, M.; Dinçer, H. Optimal quality control of baker's yeast drying in large scale batch fluidized bed. Chemical Engineering and Processing: Process Intensification 2009, 48(8), 1361–1370.
  • Köni, M.; Yüzgeç, U.; Türker, M.; Dincer, H. Adaptive neuro-fuzzy-based control of drying of baker's yeast in batch fluidized bed. Drying Technology 2010, 28(2), 205–213.
  • Topuz, A. Predicting moisture content of agricultural products using artificial neural networks. Advances in Engineering Software 2010, 41(3), 464–470.
  • Nazghelichi, T.; Aghbashlo, M.; Kianmehr, M.H. Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture 2011, 75(1), 84–91.
  • Nazghelichi, T.; Kianmehr, M.H.; Aghbashlo, M. Prediction of carrot cubes drying kinetics during fluidized bed drying by artificial neural network. Journal of Food Science and Technology 2011, 48(5), 542–550.
  • Nazghelichi, T.; Aghbashlo, M.; Kianmehr, M.H.; Omid, M. Prediction of energy and exergy of carrot cubes in a fluidized bed dryer by artificial neural networks. Drying Technology 2011, 29(3), 295–307.
  • Petrović, J.; Chansanroj, K.; Meier, B.; Ibrić, S.; Betz, G. Analysis of fluidized bed granulation process using conventional and novel modeling techniques. European Journal of Pharmaceutical Sciences 2011, 44(3), 227–234.
  • Jena, S.; Sahoo, A. ANN modeling for diffusivity of mushroom and vegetables using a fluidized bed dryer. Particuology 2013, 11(5), 607–613.
  • Motevali, A.; Younji, S.; Chayjan, R.A.; Aghilinategh, N.; Banakar, A. Drying kinetics of dill leaves in a convective dryer. International Agrophysics 2013, 27(1), 39–47.
  • Malekjani, N.; Jafari, S.M.; Rahmati, M.H.; Zadeh, E.E.; Mirzaee, H. Evaluation of thin-layer drying models and artificial neural networks for describing drying kinetics of canola seed in a heat pump assisted fluidized bed dryer. International Journal of Food Engineering 2013, 9(4), 375–384.
  • Kamble, L.V.; Pangavhane, D.R.; Singh, T.P. Experimental investigation of horizontal tube immersed in gas–solid fluidized bed of large particles using artificial neural network. International Journal of Heat and Mass Transfer 2014, 70, 719–724.
  • Pedreño-Molina, J.L.; Monzó-Cabrera, J.; Sánchez-Hernández, D. A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes. Neurocomputing 2005, 64, 521–528.
  • Pedreño-Molina, J.L.; Monzó-Cabrera, J.; Toledo-Moreo, A.; Sánchez-Hernández, D. A novel predictive architecture for microwave-assisted drying processes based on neural networks. International Communications in Heat and Mass Transfer 2005, 32(8), 1026–1033.
  • Poonnoy, P.; Tansakul, A.; Chinnan, M. Estimation of moisture ratio of a mushroom undergoing microwave-vacuum drying using artificial neural network and regression models. Chemical Product and Process Modeling 2007, 2(3), 1–15.
  • Poonnoy, P.; Tansakul, A.; Chinnan, M. Artificial neural network modeling for temperature and moisture content prediction in tomato slices undergoing microwave‐vacuum drying. Journal of Food Science 2007, 72(1), E042–E047.
  • Motevali, A.; Minaei, S.; Khoshtaghaza, M.H.; Kazemi, M.; Mohamad Nikbakht, A. Drying of pomegranate arils: Comparison of predictions from mathematical models and neural networks. International Journal of Food Engineering 2010, 6(3), 1–19.
  • Momenzadeh, L.; Zomorodian, A.; Mowla, D. Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using artificial neural network. Food and Bioproducts Processing 2011, 89(1), 15–21.
  • Murthy, T.P.K.; Manohar, B. Microwave drying of mango ginger (Curcuma amada Roxb): Prediction of drying kinetics by mathematical modelling and artificial neural network. International Journal of Food Science & Technology 2012, 47(6), 1229–1236.
  • Balbay, A.; Kaya, Y.; Sahin, O. Drying of black cumin (Nigella sativa) in a microwave assisted drying system and modeling using extreme learning machine. Energy 2012, 44(1), 352–357.
  • Mirsephai, A.; Mohammadzaheri, M.; Chen, L.; O'Neill, B. An artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer. International Communications in Heat and Mass Transfer 2012, 39(1), 40–45.
  • Zare, D.; Naderi, H.; Jafari, A.A. Experimental and theoretical investigation of rough rice drying in infrared-assisted hot air dryer using artificial neural network. World Academy of Science, Engineering and Technology 2012, 69, 1–5.
  • Motavali, A.; Najafi, G.H.; Abbasi, S.; Minaei, S.; Ghaderi, A. Microwave–vacuum drying of sour cherry: Comparison of mathematical models and artificial neural networks. Journal of Food Science and Technology 2013, 50(4), 714–722.
  • Yaghoubi, M.; Askari, B.; Mokhtarian, M.; Tavakolipour, H.; Elhami Rad, A.; Jafarpour, A.; Heidarian, S. Possibility of using neural networks for moisture ratio prediction in dried potatoes by means of different drying methods and evaluating physicochemical properties. Agricultural Engineering International: CIGR Journal 2013, 15(4), 258–269.
  • Mirsepahi, A.; Chen, L.; O'Neill, B. A comparative approach of inverse modelling applied to an irradiative batch dryer employing several artificial neural networks. International Communications in Heat and Mass Transfer 2014, 53, 164–173.
  • Sarimeseli, A.; Coskun, M.A.; Yuceer, M. Modeling microwave drying kinetics of thyme (thymus vulgaris l.) leaves using ANN methodology and dried product quality. Journal of Food Processing and Preservation 2014, 38(1), 558–564.
  • Yousefi, G.; Emam-Djomeh, Z.; Omid, M.; Askari, G.R. Prediction of physicochemical properties of raspberry dried by microwave-assisted fluidized bed dryer using artificial neural network. Drying Technology 2014, 32(1), 4–12.
  • Amiri Chayjan, R.; Kaveh, M.; Khayati, S. Modeling some drying characteristics of sour cherry (Prunus cerasu s L.) under infrared radiation using mathematical models and artificial neural networks. Agricultural Engineering International: CIGR Journal 2014, 16(1), 265–279.
  • Chen, C.R.; Ramaswamy, H.S.; Alli, I. Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Drying Technology 2001, 19(3–4), 507–523.
  • Baruch, I.; Genina-Soto, P.; Nenkova, B.; Barrera-Cortés, J. Neural model of osmotic dehydration kinetics of fruits cubes. In Artificial Intelligence: Methodology, Systems, and Applications; Springer: Berlin, 2004; 312–320.
  • Zenoozian, M.S.; Devahastin, S.; Razavi, M.A.; Shahidi, F.; Poreza, H.R. Use of artificial neural network and image analysis to predict physical properties of osmotically dehydrated pumpkin. Drying Technology 2007, 26(1), 132–144.
  • Ochoa-Martínez, C.I.; Ayala-Aponte, A.A. Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. LWT-Food Science and Technology 2007, 40(4), 638–645.
  • Ochoa-Martínez, C.I.; Ramaswamy, H.S.; Ayala-Aponte, A.A. Artificial neural network modeling of osmotic dehydration mass transfer kinetics of fruits. Drying Technology 2007, 25(1), 85–95.
  • Ochoa-Martínez, C.I.; Ramaswamy, H.S.; Ayala-Aponte, A.A. ANN-based models for moisture diffusivity coefficient and moisture loss at equilibrium in osmotic dehydration process. Drying Technology 2007, 25(5), 775–783.
  • Tortoe, C.; Orchard, J.; Beezer, A.; Tetteh, J. Artificial neural networks in modeling osmotic dehydration of foods. Journal of Food Processing and Preservation 2008, 32(2), 270–285.
  • Zenoozian, M.S.; Devahastin, S. Application of wavelet transform coupled with artificial neural network for predicting physicochemical properties of osmotically dehydrated pumpkin. Journal of Food Engineering 2009, 90(2), 219–227.
  • Lertworasirikul, S.; Saetan, S. Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. Journal of Food Engineering 2010, 98(2), 214–223.
  • Fathi, M.; Mohebbi, M.; Razavi, S.M.A. Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food and Bioprocess Technology 2011, 4(8), 1357–1366.
  • Mohebbi, M.; Shahidi, F.; Fathi, M.; Ehtiati, A.; Noshad, M. Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network. Food and Bioproducts Processing 2011, 89(4), 362–366.
  • Mohebbi, M.; Akbarzadeh-T, M.R.; Shahidi, F.; Zabihi, S.M. Modeling and optimization of mass transfer during osmosis dehydration of carrot slices by neural networks and genetic algorithms. International Journal of Food Engineering 2011, 7(2), 1–21.
  • Fathi, M.; Mohebbi, M.; Razavi, S.M.A. Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food and Bioprocess Technology 2011, 4(8), 1357–1366.
  • Fathi, M.; Mohebbi, M.; Razavi, S.M.A. Effect of osmotic dehydration and air drying on physicochemical properties of dried kiwifruit and modeling of dehydration process using neural network and genetic algorithm. Food and Bioprocess Technology 2011, 4(8), 1519–1526.
  • Ćurčić, B.L.; Pezo, L.L.; Filipović, V.S.; Nićetin, M.R.; Knežević, V. Osmotic treatment of fish in two different solutions: Artificial neural network model. Journal of Food Processing and Preservation. In press. DOI:10.1111/jfpp.12275.
  • Mokhtarian, M.; Heydari Majd, M.; Koushki, F.; Bakhshabadi, H.; Daraei Garmakhany, A.; Rashidzadeh, S. Optimisation of pumpkin mass transfer kinetic during osmotic dehydration using artificial neural network and response surface methodology modelling. Quality Assurance and Safety of Crops & Foods 2014, 6(2), 201–214.
  • Bahmani, A.; Jafari, S.M.; Shahidi, S.A.; Dehnad, D. Mass Transfer kinetics of eggplant during osmotic dehydration by neural networks. Journal of Food Processing and Preservation. In press. DOI:10.1111/jfpp.12435.
  • Kwapińska, M.; Zbiciński, I. Prediction of final product properties after cocurrent spray drying. Drying Technology 2005, 23(8), 1653–1665.
  • Chegini, G.R.; Khazaei, J.; Ghobadian, B.; Goudarzi, A.M. Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks. Journal of Food Engineering 2008, 84(4), 534–543.
  • Youssefi, S.; Emam-Djomeh, Z.; Mousavi, S.M. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice. Drying Technology 2009, 27(7–8), 910–917.
  • Azadeh, A.; Neshat, N.; Saberi, M. An intelligent approach for improved predictive control of spray drying process. In Intelligent Engineering Systems (INES), 2010 14th International Conference, IEEE: Piscataway, NJ, 2010; 127–136.
  • Mihajlovic, T.; Ibric, S.; Mladenovic, A. Application of design of experiments and multilayer perceptron neural network in optimization of the spray-drying process. Drying Technology 2011, 29(14), 1638–1647.
  • Neshat, N.; Mahlooji, H.; Kazemi, A. An enhanced neural network model for predictive control of granule quality characteristics. Scientia Iranica 2011, 18(3), 722–730.
  • Azadeh, A.; Neshat, N.; Kazemi, A.; Saberi, M. Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach. The International Journal of Advanced Manufacturing Technology 2012, 58(5–8), 585–596.
  • Keshani, S.; Daud, W.R.W.; Woo, M.W.; Talib, M.Z.M.; Chuah, A.L.; Russly, A.R. Artificial neural network modeling of the deposition rate of lactose powder in spray dryers. Drying Technology 2012, 30(4), 386–397.
  • Fazaeli, M.; Emam-Djomeh, Z.; Omid, M.; Kalbasi-Ashtari, A. Prediction of the physicochemical properties of spray-dried black mulberry (Morus nigra) juice using artificial neural networks. Food and Bioprocess Technology 2013, 6(2), 585–590.
  • Aghbashlo, M.; Mobli, H.; Rafiee, S.; Madadlou, A. An artificial neural network for predicting the physiochemical properties of fish oil microcapsules obtained by spray drying. Food Science and Biotechnology 2013, 22(3), 677–685.
  • Patel, A.D.; Agrawal, A.; Dave, R.H. Development of polyvinylpyrrolidone‐based spray‐dried solid dispersions using response surface model and ensemble artificial neural network. Journal of Pharmaceutical Sciences 2013, 102(6), 1847–1858.
  • Patel, A.D.; Agrawal, A.; Dave, R.H. Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models. European Journal of Pharmaceutics and Biopharmaceutics 2014, 86(3), 404–417.
  • Miletić, T.; Ibrić, S.; Đurić, Z. Combined application of experimental design and artificial neural networks in modeling and characterization of spray drying drug: Cyclodextrin complexes. Drying Technology 2014, 32(2), 167–179.
  • Todorov, Y.V.; Tsvetkov, T.D. Volterra model predictive control of a lyophilization plant. In Intelligent Systems, 2008. IS'08, 4th International IEEE Conference, vol. 3; IEEE: Piscataway, NJ, 2008; 20–13–20–18.
  • Menlik, T.; Kırmacı, V.; Usta, H. Modeling of freeze drying behaviors of strawberries by using artificial neural network. Journal of Thermal Science and Technology 2009, 29(2), 11–21.
  • Menlik, T.; Özdemir, M.B.; Kirmaci, V. Determination of freeze-drying behaviors of apples by artificial neural network. Expert Systems with Applications 2010, 37(12), 7669–7677.
  • Raharitsifa, N.; Ratti, C. Foam‐mat freeze‐drying of apple juice, Part 1: Experimental data and ANN simulations. Journal of Food Process Engineering 2010, 33(s1), 268–283.
  • Polat, K.; Kirmaci, V. A novel data preprocessing method for the modeling and prediction of freeze-drying behavior of apples: Multiple output–dependent data scaling (MODDS). Drying Technology 2012, 30(2), 185–196.
  • Drăgoi, E.N.; Curteanu, S.; Fissore, D. On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. Drying Technology 2013, 31(1), 72–81.
  • Todorov, Y.; Ahmed, S.; Petrov, M.; Chitanov, V. Implementations of a Hammerstein fuzzy-neural model for predictive control of a lyophilization plant. In Intelligent Systems (IS), 2012 6th IEEE International Conference; IEEE: Piscataway, NJ, 2012; 316–321.
  • Drăgoi, E.N.; Curteanu, S.; Fissore, D. On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. Drying Technology 2013, 31(1), 72–81.
  • Guiné, R.P.; Barroca, M.J.; Gonçalves, F.J.; Alves, M.; Oliveira, S.; Mendes, M. Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 2014, 168, 454–459.
  • Pallai, E.; Szentmarjay, T.; Mujumdar, A.S. Spouted bed drying. In Handbook of Industrial Drying; A.S. Mujumdar, Ed.; CRC Press: Boca Raton, FL, 2006; 363–384.
  • Cubillos, F.A.; Vyhmeister, E.; Acuña, G.; Alvarez, P.I. Rotary dryer control using a grey-box neural model scheme. Drying Technology 2011, 29(15), 1820–1827.
  • Duchesne, C.; Thibault, J.; Bazin, C. Dynamics and assessment of some control strategies of a simulated industrial rotary dryer. Drying Technology 1997, 15(2), 477–510.
  • Mateo, J.M.; Cubillos, F.A.; Alvarez, P.I. Hybrid neural approaches for modelling drying processes for particulate solids. Drying Technology 1999, 17(4–5), 809–823.
  • Thibault, J.; Alvarez, P.I.; Blasco, R.; Vega, R. Modeling the mean residence time in a rotary dryer for various types of solids. Drying Technology 2010, 28(10), 1136–1141.
  • Cubillos, F.A.; Vyhmeister, E.; Acuña, G.; Alvarez, P.I. Rotary dryer control using a grey-box neural model scheme. Drying Technology 2011, 29(15), 1820–1827.
  • Casanova-Peláez, P.J.; Palomar-Carnicero, J.M.; Manzano-Agugliaro, F.; Cruz-Peragón, F. Olive cake improvement for bioenergy: The drying kinetics. International Journal of Green Energy 2015, 12(6), 559–569.
  • Perazzini, H.; Freire, F.B.; Freire, J.T. Prediction of residence time distribution of solid wastes in a rotary dryer. Drying Technology 2014, 32(4), 428–436.
  • Strumiłło, C.; Jones, P.L.; Żyłła, R. Energy aspects in drying. In Handbook of Industrial Drying; A.S. Mujumdar, Ed.; CRC Press: Boca Raton, FL, 2006; 1075–1099.
  • Bala, B.K.; Ashraf, M.A.; Uddin, M.A.; Janjai, S. Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather. Journal of Food Process Engineering 2005, 28(6), 552–566.
  • Seginer, I.; Bux, M. Modeling solar drying rate of wastewater sludge. Drying Technology 2006, 24(11), 1353–1363.
  • Khazaei, J.; Daneshmandi, S. Modeling of thin-layer drying kinetics of sesame seeds: Mathematical and neural networks modeling. International Agrophysics 2007, 21(4), 335–348.
  • Tripathy, P.P.; Kumar, S. Neural network approach for food temperature prediction during solar drying. International Journal of Thermal Sciences 2009, 48(7), 1452–1459.
  • Çakmak, G.; Yıldız, C. The prediction of seedy grape drying rate using a neural network method. Computers and Electronics in Agriculture 2011, 75(1), 132–138.
  • Prakash, O.; Kumar, A. Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer. International Journal of Ambient Energy 2014, 35(4), 186–192.
  • Prakash, O.; Kumar, A. ANFIS modelling of a natural convection greenhouse drying system for jaggery: An experimental validation. International Journal of Sustainable Energy 2014, 33(2), 316–335.
  • Farkas, I.; Reményi, P.; Biró, A. A neural network topology for modelling grain drying. Computers and Electronics in Agriculture 2000, 26(2), 147–158.
  • Farkas, I.; Reményi, P.; Biró, A. Modelling aspects of grain drying with a neural network. Computers and Electronics in Agriculture 2000, 29(1), 99–113.
  • Zhang, Q.; Yang, S.X.; Mittal, G.S.; Yi, S. Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosystems Engineering 2002, 83(3), 281–290.
  • Farkas, I. Use of artificial intelligence for the modelling of drying processes. Drying Technology 2013, 31(7), 848–855.
  • Hazarika, M.K.; Datta, A.K. Estimation of drying rate constant from static bed moisture profile by neural network inversion. Agricultural Engineering International: CIGR Journal 2014, 16(1), 253–264.
  • Pallai, E.; Szentmarjay, T.; Mujumdar, A.S. Spouted bed drying. In Handbook of Industrial Drying; A.S. Mujumdar, Ed.; CRC Press: Boca Raton, FL, 2006; 363–384.
  • Jittanit, W.; Srzednicki, G.; Driscoll, R.H. Comparison between fluidized bed and spouted bed drying for seeds. Drying Technology 2013, 31(1), 52–56.
  • Jumah, R.; Mujumdar, A.S. Modeling intermittent drying using an adaptive neuro-fuzzy inference system. Drying Technology 2005, 23(5), 1075–1092.
  • Freire, J.T.; Freire, F.B.; Ferreira, M.C.; Nascimento, B.S. A hybrid lumped parameter/neural network model for spouted bed drying of pastes with inert particles. Drying Technology 2012, 30(11–12), 1342–1353.
  • Nascimento, B.S.; Freire, F.B.; Freire, J.T. Moisture prediction during paste drying in a spouted bed. Drying Technology 2013, 31(15), 1808–1816.
  • Huang, B.; Mujumdar, A.S. Use of neural network to predict industrial dryer performance. Drying Technology 1993, 11(3), 525–541.
  • Hugget, A.; Sebastian, P.; Nadeau, J.P. Global optimization of a dryer by using neural networks and genetic algorithms. AIChE Journal 1999, 45(6), 1227–1238.
  • Cristea, M.V.; Roman, R.; Agachi, Ş.P. Neural networks based model predictive control of the drying process. Computer Aided Chemical Engineering 2003, 14, 389–394.
  • Feng, D.; Dong, L.; Fei, M.; Chen, T. Genetic algorithm based neuro-fuzzy network adaptive PID control and its applications. In Computational and Information Science; Springer: Berlin, 2005; 330–335.
  • Liu, X.; Chen, X.; Wu, W.; Zhang, Y. Process control based on principal component analysis for maize drying. Food Control 2006, 17(11), 894–899.
  • Zhang, D.Y.; Sun, L.P.; Cao, J. Modeling of temperature-humidity for wood drying based on time-delay neural network. Journal of Forestry Research 2006, 17(2), 141–144.
  • Zhao, C.; Chi, Q.; Wang, L.; Wen, B. A model predictive control of a grain dryer with four stages based on recurrent fuzzy neural network. In Advances in Neural Networks–ISNN 2007; Springer: Berlin, 2007; 29–37.
  • Liu, X.; Chen, X.; Wu, W.; Peng, G. A neural network for predicting moisture content of grain drying process using genetic algorithm. Food Control 2007, 18(8), 928–933.
  • Simon, L.L.; Hungerbuhler, K. Industrial batch dryer data mining using intelligent pattern classifiers: Neural network, neuro-fuzzy and Takagi–Sugeno fuzzy models. Chemical Engineering Journal 2010, 157(2), 568–578.
  • Chen, C.H.; Li, J.M.; Yin, J.K.; Zhang, F.; Yao, J. Quality prediction based on ANN in tobacco redrying process. Advanced Materials Research 2011, 211, 1046–1050.
  • Martínez-Martínez, V.; Baladrón, C.; Gomez-Gil, J.; Ruiz-Ruiz, G.; Navas-Gracia, L.M.; Aguiar, J.M.; Carro, B. Temperature and relative humidity estimation and prediction in the tobacco drying process using artificial neural networks. Sensors 2012, 12(10), 14004–14021.
  • Thyagarajan, T.; Panda, R.C.; Shanmugan, J.; Rao, V.P.G.; Ponnavaikko, M. Development of ANN model for nonlinear drying process. Drying Technology 1997, 15(10), 2527–2540.
  • Zbiciński, I.; Smucerowicz, I.; Strumiłło, C.; Kasznia, J.; Stawczyk, J. Murlikiewicz, K. Optimization and neural modelling of pulse combustors for drying applications. Drying Technology 1999, 17(3), 610–631.
  • Kim, K.B.; Kang, H.Y.; Yoon, D.J.; Choi, M.Y. Pattern classification of acoustic emission signals during wood drying by principal component analysis and artificial neural network. Key Engineering Materials 2005, 297, 1962–1967.
  • Kerdpiboon, S.; Kerr, W.L.; Devahastin, S. Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content. Food Research International 2006, 39(10), 1110–1118.
  • Banooni, S.; Hosseinalipour, S.M.; Mujumdar, A.S.; Taherkhani, P.; Bahiraei, M. Baking of flat bread in an impingement oven: Modeling and optimization. Drying Technology 2009, 27(1), 103–112.
  • Zemin, X.; Wenfu, W.; Liyan, Y. Prediction impact of vacuum drying parameters on rice taste value with neural network model. In Digital Manufacturing and Automation (ICDMA), 2010 International Conference, vol. 2; IEEE: Piscataway, NJ, 2010; 95–98.
  • Shrivastav, S.; Kumbhar, B.K. Drying kinetics and ANN modeling of paneer at low pressure superheated steam. Journal of Food Science and Technology 2011, 48(5), 577–583.
  • Watanabe, K.; Matsushita, Y.; Kobayashi, I.; Kuroda, N. Artificial neural network modeling for predicting final moisture content of individual Sugi (Cryptomeria japonica) samples during air-drying. Journal of Wood Science 2013, 59(2), 112–118.
  • Watanabe, K.; Kobayashi, I.; Matsushita, Y.; Saito, S.; Kuroda, N.; Noshiro, S. Application of near-infrared spectroscopy for evaluation of drying stress on lumber surface: A comparison of artificial neural networks and partial least squares regression. Drying Technology 2014, 32(5), 590–596.
  • Kurtulmuş, F.; Gürbüz, O.; Değirmencioğlu, N. Discriminating drying method of tarhana using computer vision. Journal of Food Process Engineering 2014, 37(4), 362–374.
  • Mujumdar, A.S. Principles, classification, and selection of dryers. In Handbook of Industrial Drying; A.S. Mujumdar, Ed.; CRC Press: Boca Raton, FL, 2006; 4–31.
  • Izeboudjen, N.; Larbes, C.; Farah, A. A new classification approach for neural networks hardware: From standards chips to embedded systems on chip. Artificial Intelligence Review 2014, 41(4), 491–534.

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