APPLICATION OF FUZZY RULES-BASED MODELS TO PREDICTION OF QUALITY DEGRADATION OF RICE AND MAIZE DURING HOT AIR DRYING

ABSTRACT The concept of modelling using a rules -based approach is introduced and developed for die prediction of the quality degradation of grains during hot air drying. It is men validated on two typical examples of quality degradation during drying: the head kernel yield of paddy rice and the wet-milling quality of maize. The linguistic approach gives a representation of die experts understanding of phenomena and leads to a good kinetic model after identification of die parameters using genetic algorithms. This study also points out die robustness of die model prediction, even if noisy data are used, and its ability to predict transient behaviours under certain conditions.

[1]  Tuan D. Pham,et al.  Fuzzy logic applied to numerical modelling of engineering problems , 1995 .

[2]  Robert Balshaw,et al.  Effects of Drying Temperature and Farina Blending on Spaghetti Quality Using Response Surface Methodology , 1993 .

[3]  Gilles Trystram,et al.  Dynamic models for drying and wet-milling quality degradation of corn using neural networks , 1997 .

[4]  J. Hawkes,et al.  Prediction of folic acid retention during spray dehydration , 1989 .

[5]  J. J. Bimbenet,et al.  MODELLING OF DRYING IN ORDER TO IMPROVE PROCESSING QUALITY OF MAIZE , 1991 .

[6]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[7]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Gilles Trystram,et al.  Food process modeling and simulation , 1997 .

[10]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[11]  Teresita Rocha Mier.  Influence de pretraitements et des conditions de sechage sur la couleur et l'arome de la mentha (mentha spicata huds. ) et du basilic (ocimum basilicum) , 1993 .

[12]  N. W. Rees,et al.  Identification of dynamic fuzzy models , 1995, Fuzzy Sets Syst..

[13]  Kozo Matsushima,et al.  A simple method of fuzzy modeling for a microorganism reaction , 1992 .

[14]  A. Morris,et al.  Fuzzy neural networks for nonlinear systems modelling , 1995 .

[15]  P. S. Burr.  Process modelling and simulation , 1998 .

[16]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[17]  C. Bonazzil,et al.  INFLUENCE OF DRYING CONDITIONS ON THE PROCESSING QUALITY OF ROUGH RICE , 1997 .

[18]  Pekka Linko,et al.  Artificial intelligence for the food industry , 1993 .

[19]  Shi-Shang Jang,et al.  Nonlinear Rule Based Model Predictive Control of Chemical Processes , 1994 .

[20]  Gilles Trystram,et al.  Dynamic Modelling and Simulation of Food Processes , 1994 .

[21]  Qin Zhang,et al.  FUZZY LOGIC CONTROL FOR A CONTINUOUS CROSSFLOW GRAIN DRYER , 1993 .

[22]  Anne-Lucie Raoult-Wack,et al.  Application of neural network modelling for the control of dewatering and impregnation soaking process (osmotic dehydration) , 1997 .

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[24]  Anne-Lucie Raoult-Wack,et al.  Food drying and dewatering , 1996 .

[25]  Francis Courtois,et al.  EXPERIMENTAL STUDY ON THE QUALITY OF ROUGH RICE RELATED TO DRYING CONDITIONS , 1994 .

[26]  A. Cipriano,et al.  A fuzzy model based predictive controller and its application to a mineral flotation plant , 1995 .

[27]  Joseph Bentsman,et al.  Fuzzy prediction of maize breakage , 1992 .