Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors

Mjalli, F.S. and Hussain, Mohd Azlan (2009) Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors. Industrial & Engineering Chemistry Research, 48 (24). pp. 11034-11047. ISSN 0888-5885, DOI

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Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feedforward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the poleplacement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves.

Item Type: Article
Additional Information: Cited By (since 1996): 12 Export Date: 5 March 2013 Source: Scopus CODEN: IECRE Language of Original Document: English Correspondence Address: Mjalli, F. S.; Department of Chemical Engineering, University of Malaya, Kuala Lumpur, Malaysia; email: References: Tapasvi, D., Wiesenborn, D., Gustafson, C., (2004) Process Modeling Approach for Evaluating the Economic Feasibility of Biodiesel Production, pp. MB04-304. , North Central ASAE/CSAE Conference; Freedman, B., Butterfield, R.O., Pryde, E.H., Transesterification kinetics of soybean oil (1986) J. Am. Oil Chem. Soc., 63 (10), pp. 1375-1380; Tumer, T.L., (2005) Modeling and Simulation of Reaction Kinetics for Biodiesel Production, , Ph.D. Thesis, North Carolina State University; Noureddini, H., Zhu, D., Kinetic of transesterification of soybean oil (1997) J. Am. Oil Chem. Soc., 74, pp. 1457-1463; Boocock, D.G.B., Konar, S.K., Mao, V., Lee, C., Buligan, S., Fast formation of high-purity methyl esters from vegetable oils (1998) J. Am. Oil Chem. Soc., 75 (9), pp. 1167-1172; Darnoko, D., Cheryan, M., Kinetics of palm oil transesterification in a batch reactor (2000) J. Am. Oil Chem. Soc., 77, pp. 1263-1267; Baird, L., Hanson C, (1983) Handbook of Solvent Extraction, , Wiley-Interscience: New York; Dumont, G.A., Huzmezan, M., Concepts, methods and techniques in adaptive control (2002) Am. Control Conf., 2, pp. 1137-1150; Suykens, J.A.K., Vandewalle, J.P.L., De Moor, B.L.R., (1995) Artificial Neural Networks for Modelling and control of Non-linear systems, , Kluwer Academic Publishers: Boston; Miller, W.T., Sutton, R.S., Werbos, P.J., (1990) Neural networks for control, , MIT Press: Cambridge, MA; Bayoumy, A., Bordeneuve-Guibé, J., A neural predictive control scheme for nonlinear plants (2002) Structural Dynamics, and Materials Conference, pp. 22-25. , Denver, Colorado; Shah, S., Joshi, D., (1987) Handbook of Single-phase Convective Heat Transfer, , Wiley-R. K. Interscience: New York; Chapter 5; Wilkinson, W.L., Edwards, M.F., Heat transfer in agitator vessels part I-newtonian fluids (1972) Chem. Engr., London, 264, pp. 310-319; Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., (2001) Neural Network for Modelling and Control of Dynamic System, , 2nd ed.; Springer-Verlag: London; Sjöberg, J., Ljung, L., (1992) Overfitting, Regularization and Searching for Minimum in Neural Network, , Preprint IFAC Symposium on Adaptive Systme in Contol and Signal Processing, Grenoble, France; Demuth, H., Beale, M., (2000) Neural Network Toolbox for Use with MATLAB, User Guide, , The MathWorks Inc; Hagan, M.T., Demuth, H.B., Beale, M.H., (1996) Neural Network Design, , PWS Publishing: Boston, MA; �ström, K.J., Wittenmark, B., (1995) Adaptive Control, , 2nd ed.; Addison-Wesley; Clarke, D.W., Self-tuning control of nonminimum-phase systems (1984) Automatica, 20 (5), pp. 501-517; Sørensen, O., (1994) Neural Networks in Control Applications, , Ph.D. Dissertation, Department of Control Engineering, Aalborg University, Denmark; �ström, K.J., Theory and applications of adaptive control-a survey (1983) Automatica, 19, pp. 471-486; Yasuchika, M., Futoshi, A., On a stability of minimum variance control (2007) Electr. Eng. Jpn., 159 (1), pp. 56-62; Soeterboek R, (1992) Predictive Control: A Unified Approach, , Prentice Hall: New York
Uncontrolled Keywords: Approximate model; Approximate model predictive controls; Control algorithms; Control problems; Delayed input; Linearization technique; Minimum variance control; Non-linear model; Non-Linearity; Palm oil; Pole placement; Poor performance; Product concentration; Reactor temperatures; Self tuning controls; Self-tuning adaptive control; Selftuning; Set-point; Set-point tracking; Transesterification reaction; Adaptive algorithms; Adaptive control systems; Biodiesel; Feedforward neural networks; linearization; Predictive control systems; Tuning; Vegetable oils; Model predictive control.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 02:51
Last Modified: 10 Feb 2021 03:49

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