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Warianty tytułu
Języki publikacji
Abstrakty
The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX) model was developed for comparison purpose. ARX model gave high root mean square error (RMSE) of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS) model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE) and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
27--33
Opis fizyczny
Bibliogr. 13 poz., fig.
Twórcy
autor
- Department of Chemical Engineering, Lautech, PMB 4000, Ogbomoso, Nigeria
autor
- Department of Chemical Engineering, Lautech, PMB 4000, Ogbomoso, Nigeria
autor
- National Space Research and Development Agency, Nigeria
Bibliografia
- 1. Araromi D.O., Olu-Arotiowa O.A., Olajide J.O. and Afolabi T.J. Neuro fuzzy modelling approach for prediction of equilibrium moisture characteristics and shelf-life of corn flour. International Journal of Soft Computing, 3(2), 2008, 159–166.
- 2. Araromi D.O., Afolabi T.J., Aloko D.F. Neural network control of CSTR for reversible reaction using reverence model approach. Leonardo Journal of Sciences, 6(10), 2007, 25–40.
- 3. Boskoski P., Stankovski M. Neuro-fuzzy controllers and application to autonomous robots. Journal of Mechanics, Automatic Control and Robotics, 7(1), 2008, 123–132.
- 4. Chi-Bin C. Process optimization by soft computing and its application to a wire bonding problem. Int. J. Appl. Sci. Eng., 2(1), 2004, 59.
- 5. Denis M., Keyvan N., Claude B., and Erwan G. An application of multivariate simulation in the cement industry. Mathemat. Geol., 37(5), 2005, 493–512.
- 6. George T.A. Shreve chemical processes. (Fifth Edition). McGraw-Hills, New York 1984.
- 7. Jin Y., Jiang J., Zhu J. Neural network based fuzzy identification and its application to modelling and control of complex systems. IEEE Trans. on Systems, Man and Cybernetics, 25(6), 1995, 990–997.
- 8. Kural A., Kizilaslan K., Ertugrul S., and Ozsoy C. A comparative study on modelling of raw material blending process in cement industry using conventional and intelligent technique. Control Application, 1, 2001, 736–741.
- 9. Lin C.J. and Xu Y.J. A hybrid evolutionary learning algorithm for TSK-type fuzzy model design, Mathematical and Computer Modelling, 43, 2006, 563–581.
- 10. Mat Darus I.Z. and Tokhi M.O. Modelling and control of a flexible structure using adaptive neuro-fuzzy inference system. In: Proceedings of the IEEE Conference on Mechatronics, ICM’2004, Istanbul, Turkey, 3-5 June, 2004.
- 11. Mat Darus I.Z. and Tokhi M.O. Adaptive neurofuzzy inference system modelling and control of a flexible plate structure. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, UKCI’2003, Bristol, UK, 1-3 September, 2003.
- 12. Oni G. Personal contact. Purechem Industries Limited, Onigbedu Village, Ogun State, Nigeria 2010.
- 13. Ozsoy C., Kural A. and Baykara C. Modeling of the raw mixing process in cement industry. International Journal of Adaptive Control and Signal Processing, 18(5), 2004, 427–442.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0a92b225-3d73-442b-8a65-38fa893b52db