Systems modeling and intelligent control of meat drying process

The objective of this research is to develop a system model and an intelligent controller for the meat drying process, which will lead to better control accuracy than the current PID control system used in meat manufacturing. In meat drying rooms, temperature and relative humidity are coupled by nonlinear thermodynamic laws. The coupling results in nonlinear fluctuations in relative humidity. The classical PID control method is not able to effectively control the relative humidity. The models of the systems of temperature and relative humidity for a meat drying room in this paper include the model of the plant, the model of the coupling, the controller, and the complete control system of the meat drying room. The proposed intelligent control uses the methods of a fuzzy PID controller to limit the fluctuations in the meat drying system. Results show that the performance of the proposed fuzzy PID control systems is superior in terms of relative humidity control to the classical PID control method used in the current control systems for the meat drying rooms.

[1]  Martine Baelmans,et al.  Sensitivity of temperature and weight loss in the bulk of chicory roots with respect to process and product parameters , 2004 .

[2]  Robert Sabourin,et al.  Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm , 2005 .

[3]  Tommy S. W. Wong,et al.  Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling , 2010 .

[4]  Jing He,et al.  Research of intelligent humidity control system based on field-bus , 2004, ICARCV.

[5]  S. Prachayawarakorn,et al.  Kinetics of colour change during storage of dried garlic slices as affected by relative humidity and temperature , 2004 .

[6]  Chin-Shung Hsu.  Digital control systems , 1982, Proceedings of the IEEE.

[7]  P. W. Rodgers,et al.  Practical instrumentation transducers , 1972 .

[8]  L. T. Fan,et al.  Simultaneous control of temperature and humidity in a confined space. II - Feedback control synthesis via classical control theory. , 1973 .

[9]  Wei Zhang,et al.  A fuzzy based approach to effective relative humidity tracking in industrial systems , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[10]  Luigi Fortuna,et al.  Soft computing for greenhouse climate control , 2000, IEEE Trans. Fuzzy Syst..

[11]  G. Parolari,et al.  Relationship between cathepsin B activity and compositional parameters in dry-cured hams of normal and defective texture. , 1994, Meat science.

[12]  Konstantinos G. Arvanitis,et al.  A nonlinear feedback technique for greenhouse environmental control , 2003 .

[13]  Simon X. Yang,et al.  Coupling analysis and control of temperature and relative humidity in a drying room , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).

[14]  Naresh K. Sinha,et al.  Modern Control Systems , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Jean-Dominique Daudin,et al.  Development of a new method for fast measurement of water sorption isotherms in the high humidity range Validation on gelatine gel , 2000 .

[16]  S. Lin,et al.  An effective laboratory air humidity generator for drying research , 2005 .

[17]  Somchart Soponronnarit,et al.  Comparative study of different combined superheated-steam drying techniques for chicken meat , 2007 .

[18]  Yasuo Nantou,et al.  Digital Ventilated Psychrometer , 1979, IEEE Transactions on Instrumentation and Measurement.

[19]  Raphael Linker,et al.  Robust climate control of a greenhouse equipped with variable-speed fans and a variable-pressure fogging system , 2011 .

[20]  Plamen Daskalov.  Prediction of Temperature and Humidity in a Naturally Ventilated Pig Building , 1997 .

[21]  Jiangjiang Wang,et al.  Genetic optimization algorithm on PID decoupling controller for variable flow heating system , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[22]  Gabriela Clemente,et al.  Management and optimization of curing chambers , 2005 .

[23]  Morteza M. Ardehali,et al.  Wavelet based artificial neural network applied for energy efficiency enhancement of decoupled HVAC system , 2012 .

[24]  Haruhiko Murase,et al.  Simultaneous Control of Temperature and Humidity in a Confined Space Using Kalman Filter Neural Networks , 1992 .

[25]  Jiangjiang Wang,et al.  Genetic optimization algorithm of PID decoupling control for VAV air-conditioning system , 2009 .

[26]  Cao Jun,et al.  Application of single neuron adaptive PID controller during the process of timber drying , 2003, Journal of Forestry Research.

[27]  Pere Gou,et al.  Fuzzy Control System for a Meat Drying Process , 2004 .

[28]  C. Riverol-Cañizares,et al.  A fuzzy filter for improving the quality of the signal in adaptive-network-based fuzzy inference systems (ANFIS) , 2009, Appl. Soft Comput..

[29]  Xi-Wen Liu,et al.  Design for Fuzzy Decoupling Control System of Temperature and Humidity , 2011, CSIE 2011.

[30]  S. G. Gilbert,et al.  Control of water in foods during storage , 1994 .

[31]  Liu Hong,et al.  Research of Hybrid Fuzzy-PID Control Technology based on the Temperature and Humidity Control , 2008, 2008 International Symposium on Computational Intelligence and Design.

[32]  Josep Comaposada,et al.  Control System for Sausage Drying Based on On-Line NIR aw Determination , 2009 .

[33]  Denis Bruneau,et al.  Drying and smoking of meat: heat and mass transfer modeling and experimental analysis , 2005 .

[34]  M. Hertog,et al.  Humidity and temperature effects on invasive and non-invasive firmness measures , 2004 .

[35]  Vassilis Belessiotis,et al.  Estimation of uncertainties in indirect humidity measurements , 2011 .

[36]  Konstantinos G. Arvanitis,et al.  Non-linear Adaptive Temperature and Humidity Control in Animal Buildings , 2006 .

[37]  Timothy A. G. Langrish,et al.  Assessment of colour development in dried bananas -measurements and implications for modelling , 2009 .

[38]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[39]  M. Velez-Reyes,et al.  Decoupled control of temperature and relative humidity using a variable-air-volume HVAC system and non-interacting control , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[40]  Hojjat Adeli,et al.  Introduction to Computational Intelligence , 2013 .

[41]  J. Comaposada,et al.  Effect of the relative humidity of drying air during the resting period on the composition and appearance of dry-cured ham surface. , 2003, Meat science.

[42]  Richard M. Murray,et al.  Feedback Systems: An Introduction for Scientists and Engineers , 2008 .

[43]  Miguel Velez-Reyes,et al.  Nonlinear control of a heating, ventilating, and air conditioning system with thermal load estimation , 1999, IEEE Trans. Control. Syst. Technol..

[44]  A. L. Jones,et al.  Microprocessors simplify humidity measurements , 1981, IEEE Transactions on Instrumentation and Measurement.

[45]  Mehmet Karaköse,et al.  Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system , 2009, Expert Syst. Appl..

[46]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[47]  Servet Soyguder,et al.  An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach , 2009 .

[48]  R W Powell,et al.  The use of thermocouples for psychrometric purposes , 1936 .

[49]  Qinglei Hu,et al.  Neural network based robust variable structure control of wood drying kiln , 2009, 2009 Chinese Control and Decision Conference.

[50]  Ren Kun,et al.  Design of Intelligent Dry and Wet Bulb Humidity Sensor , 2011 .

[51]  L. Litz,et al.  Fuzzy control for temperature and humidity in refrigeration systems , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[52]  Ming-Li Chiang,et al.  Hybrid system based adaptive control for the nonlinear HVAC system , 2006, 2006 American Control Conference.

[53]  K. Gernaey,et al.  Modelling of coupled heat and mass transfer during a contact baking process , 2011 .

[54]  Terry J. Siebenmorgen,et al.  Low-temperature, low-relative humidity drying of rough rice , 2010 .

[55]  T. Becker,et al.  Impact of air humidity in industrial heating processes on selected quality attributes of bread rolls , 2011 .

[56]  J. Comaposada,et al.  Drying Control of Cured Sausages Through Online Measurement of Product Quality , 2007 .

[57]  Rui Qu,et al.  The strategy of construction equipment energy-saving control , 2012 .

[58]  A. Su,et al.  Numerical Modeling of Proton Exchange Membrane Fuel Cell With Considering Thermal and Relative Humidity Effects on the Cell Performance , 2006 .

[59]  George Srzednicki,et al.  Development of a control system for in-store drying of paddy in Northeast China , 2006 .

[60]  Manel Poch,et al.  Fuzzy Control System in Drying Process of Fermented Sausages , 2005 .

[61]  P. Catalano,et al.  A System for Food Drying Using Humidity Control and Low Temperature , 2008 .

[62]  Raquel Salazar,et al.  A neural network model to predict temperature and relative humidity in a greenhouse , 2008 .

[63]  Jian Wang,et al.  Application of a fuzzy decoupling control algorithm for simultaneous control of both temperature and humidity , 2006, International Symposium on Precision Mechanical Measurements.