Control of Input Multiplicity Process (Bioreactor) using Fuzzy logic Techniques
MLA Style: Ballekallu Chinna Eranna , Daniel Tadesse Abebe , Alemayu Chaufamo "Control of Input Multiplicity Process (Bioreactor) using Fuzzy logic Techniques" International Journal of Engineering Trends and Technology 67.5 (2019):98-103.
APA Style: Ballekallu Chinna Eranna , Daniel Tadesse Abebe , Alemayu Chaufamo (2019). Control of Input Multiplicity Process (Bioreactor) using Fuzzy logic Techniques International Journal of Engineering Trends and Technology,67(5),98-103.
In the present work, a Fuzzy logic controller is analyzed to a continuous bioreactor which exhibits input multiplicities in dilution rate on productivity. i.e., two values of dilution rate will give the same value of productivity. The Performance of proposed Fuzzy logic controller and conventional PI controller has been evaluated near optimum productivity. As the Fuzzy controller provides always the two values of Dilution rate for control action and by selecting the value nearer to the operating point, it is found to give stable and faster responses than conventional PI controller. The PI controller results in wash out condition or switch over from initial lower input dilution rate to higher input dilution rate or vice versa. Thus, Fuzzy control is found to overcome the control problems of PI controller due to the input multiplicities near optimal productivity. It is interesting to note that the present fuzzy logic controller is giving superior performance like previously proposed nonlinear controller by authors (Reddy, G.P. and Chidambaram, M (1995) ) to overcome the control problems due to input multiplicities and however fuzzy logic controller is less computationally involved than nonlinear controller.
 Koppel, L.B.(1983) Input multiplicities in process control, Chemical Engineering Education, pp58-63, & 89-92.
 Mamdani,E.H., Assilian, S.(1975) An experiment in linguistic synthesis with a fuzzy logic controller international, Journal of Man-Machine Studies.7pp1-13.
 Srinivas,M and Chidambaram, M(1995) Fuzzy logic control of an unstable bioreactor,Bioprocess Engineering. 12 135-139
 Henson,M.A.and. Seborg,D.E. (1982) Nonlinear control strategies for continuous fementer ”Chemical engineering Science,47pp821-835.
 Reddy, G.P. and Chidambaram, M (1995) Nonlinear control of bioreactors with input multiplicities in dilution rate, Bioprocess Engineering. 12pp 151-155.
 Chidambaram and Reddy, G.P. (1995) Non-linear control of systems with input multiplicities , Computers and Chemical Engineering, 19 pp249-252.
 Abonyi, J ,.Babuska, R. and Ayala Botto, M and Szeifert, F L. Nagy and Nagy (2002) Identification and control of nonlinear systems using fuzzy hammerstein models, Industrial & Engineering chemistry Research.39 pp4302-4314.
 Dash, S.K. and Koppel, L.B.(1989) Sudden destabilization of controlled chemical Processes Chemical Engineering Communications, 84 , pp 129-157.
 Koppel,L.B. (1982) Input multiplicities in nonlinear multivariable control systems AIChE Journal.28 pp935 -945.
 M. A. Henson &.D.E. Seborg , “Nonlinear control strategies for continuous fermenter”, Chemical engineering Science, 47,821-835, 1982.
 Chau, P.C (2002), Chemical Process Control: A First Course with Matlab.
 Chin-Teng Lin & C.S.George Lee, (1996),Neural Fuzzy Systems. Prentice Hall.
 Brent et. al (2006), Optimal model predictive control of constrained nonlinear systems.Computers Chem. Engg Vol. 22, No. 11, pp. 1573- 1579.
 Emad M. Ali and Abu Khalaf, A. M. (2004),Fuzzy Control for the Start-Up of a Non- Isothermal CSTR. Journal of King Saud University, 17, Eng. Sci. (1), Pp.25-4
 Engell, S., and Klatt, K.U. (1993), Gain Scheduling of A Non-Minimum Phase CSTR. Proceedings of the 2nd European Control Conference, Pp 2323 - 2328, IEEE Control Systems Society.
 E. Piron, E. Latrille and E Ren (1996),Application of artificial neural networks for crossflow microfiltration modelling: "black box"and semi-physical
 Baughman and Y.Liu,(1995),Neural Networks In Bio Processing and Chemical Engineering.Academic Press.
 Dumitrache and M. Caramihai (2006), Fuzzy Control of Production Of Fermentable Sugars, Fuzzy Systems, 1, Pp. 69-78.
Fuzzy logic control, Bioreactor, Input Multiplicities, Near optimal productivity.