Grey prediction fuzzy control for pH processes in the food industry

Abstract Proper regulation of pH value is an important issue in the food industry for quality production. A food pH process usually has non-linear dynamics with system uncertainty. This study treats the pH regulation process of a reactor tank as a grey box with partially known system information. The behaviour of the process is predicted one-step ahead with a first-order grey model. A fuzzy controller takes the prediction together with the current system response to regulate the discharge of base (NaOH) or acid (HCl) solution into the reactor tank to arrive at a desired pH value. The integrated grey prediction fuzzy control (GPFC) strategy is simple in control-law derivation and system implementation and is efficient in computation. The developed GPFC was validated with perform base/acid titration and continuous acidification/deacidification control. The controlled system response error was trivial in the titration and was less than 1% in the continuous control under proper agitation of the reactants. The system was used to control Acetobacter xylinum fermentation for cellulose production. The GPFC scheme exerted smooth control action, achieved a trivial steady-state error in pH control, and yielded more cellulose and acetic acid products but consumed much less material than PID or manual control.

[1]  Joao P. Hespanha,et al.  Multi-model adaptive control of a simulated pH neutralization process , 2007 .

[2]  Dale E. Seborg,et al.  Adaptive nonlinear control of a pH neutralization process , 1994, IEEE Trans. Control. Syst. Technol..

[3]  Xin-gang Li,et al.  Ammonium lactate production by Lactobacillus lactis BME5-18M in pH-controlled fed-batch fermentations , 2004 .

[4]  Odejobi A. Odetunji,et al.  Computer simulation of fuzzy control system for gari fermentation plant , 2005 .

[5]  J. Horiuchi,et al.  Selective production of organic acids in anaerobic acid reactor by pH control. , 2002, Bioresource technology.

[6]  Yuguo Zheng,et al.  pH control strategy in astaxanthin fermentation bioprocess by Xanthophyllomyces dendrorhous , 2006 .

[7]  G. Pajunen.  Comparison of linear and nonlinear adaptive control of a pH-process , 1987 .

[8]  Yo-Ping Huang,et al.  The integration and application of fuzzy and grey modeling methods , 1996, Fuzzy Sets Syst..

[9]  Ayla Altinten,et al.  Generalized predictive control applied to a pH neutralization process , 2007, Comput. Chem. Eng..

[10]  Mohamed Azlan Hussain,et al.  Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..

[11]  C. Hill,et al.  Surviving the Acid Test: Responses of Gram-Positive Bacteria to Low pH , 2003, Microbiology and Molecular Biology Reviews.

[12]  R. A. Wright,et al.  On-line identification and nonlinear control of an industrial pH process , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[13]  Mahmoud Reza Pishvaie,et al.  Control of pH processes using fuzzy modeling of titration curve , 2006, Fuzzy Sets Syst..

[14]  Ho-Hsien Chen,et al.  Optimization of the Monascus purpureus Fermentation Process Based on Multiple Performance Characteristics , 2008 .

[15]  Ruey-Jing Lian,et al.  A grey prediction fuzzy controller for constant cutting force in turning , 2005 .

[16]  Ho-Hsien Chen,et al.  A PID ratio control for removal of HCI/SOx in flue gas from refuse municipal incinerators , 2008 .

[17]  Josse De Baerdemaeker,et al.  Effects of process conditions on the pH development during yogurt fermentation , 1999 .

[18]  C. Riverol,et al.  Integration of fuzzy logic based control procedures in brewing , 2002 .

[19]  Tzou-Chi Huang,et al.  GREY RELATIONAL ANALYSIS OF DRIED ROSELLE (HIBISCUS SABDARIFFA L.) , 2005 .

[20]  C. Riverol,et al.  Estimation of the ester formation during beer fermentation using neural networks , 2007 .

[21]  M. Chidambaram,et al.  Nonlinear controller for a pH process , 1990 .

[22]  Roland Benz,et al.  A self adaptive computer-based pH measurement and fuzzy-control system , 1996 .

[23]  K. Vanbroekhoven,et al.  Dark fermentative H2 production from xylose and lactose—Effects of on-line pH control , 2008 .

[24]  Babatunde A. Ogunnaike,et al.  A contemporary industrial perspective on process control theory and practice , 1996 .

[25]  H Honda,et al.  Fuzzy control of bioprocess. , 2000, Journal of bioscience and bioengineering.

[26]  Junghui Chen,et al.  Applying neural networks to on-line updated PID controllers for nonlinear process control , 2004 .

[27]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[28]  Katarina Kavsˇek-Biasizzo,et al.  Fuzzy predictive control of highly nonlinear pH process , 1997 .

[29]  Eva Balsa-Canto,et al.  Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation , 2007 .

[30]  Gülay Özkan,et al.  Application of self-tuning PID control to a reactor of limestone slurry titrated with sulfuric acid , 2006 .

[31]  Canan Özgen,et al.  Online Identification and Control of pH in a Neutralization System , 2008 .

[32]  Robert Babuska,et al.  Fuzzy self-tuning PI control of pH in fermentation , 2002 .

[33]  Y K Yang,et al.  Effects of pH and dissolved oxygen on cellulose production by Acetobacter xylinum BRC5 in agitated culture. , 1999, Journal of bioscience and bioengineering.

[34]  Mahdi Mahfouf,et al.  Supervisory Generalised Predictive Control for electro-fluid systems , 1994 .

[35]  Fernando Tadeo,et al.  Fuzzy control of a neutralization process , 2006, Eng. Appl. Artif. Intell..

[36]  J. Choi,et al.  Adaptive control for pH systems , 1998 .

[37]  Murad Samhouri,et al.  Formulation and fuzzy modeling of emulsion stability and viscosity of a gum–protein emulsifier in a model mayonnaise system , 2008 .

[38]  Gary J. Lye,et al.  pH control in microwell fermentations of S. erythraea CA340: influence on biomass growth kinetics and erythromycin biosynthesis , 2003 .