Control Of Nitrate Flow In Pre-denitrification Systems Using Long-range Identification For Predictive Control

This paper is concerned with long-range adaptive predictive control of the biological nitrogen removal process in a pre-denitrification activated sludge plant. A control-oriented identification procedure is used for modelling a multi-step ahead (long-range) predictor using a reduced model of the nitrate concentration at the plant anoxic zone, in the range required by the predictive controller. The optimal structure of the reduced model and its parameters are identified by an advanced regression orthogonal estimator, based on the Modified Gram-Schmidt algorithm. The proposed controller controls the internal nitrate recirculation flow in order to maintain a desired nitrate concentration set-point at the anoxic zone. Simulation results, using a benchmark model, compare the performance of the proposed adaptive predictive controller against the performance of a well tuned PI controller, showing that the proposed controller presents much better external process disturbances attenuation and equivalent set-point tracking ability. © 2005 IEEE.200530433048Ingildsen, P., (2002) Realising Full-Scale Control in Wastewater Treatment Systems Using In Situ Nutrient Sensors, , PhD Thesis, Lund University, SwedenOlsson, G., Newell, B., (1999) Wastewater Treatment Systems: Modelling, Diagnosis and Control, , IWA Publishing, London;J. M. V. Lara, B. E. A. Milani and O. A. Z. Sotomayor, Adaptive Predictive Control of Ammonium Level Based on Dissolved Oxygen Variable Set-Point Control in an Activated Sludge Process, In Mercosur Congress on Process Systems Engineering, ENPROMER 2005, Rio de Janeiro, Brazil, 2005Clarke, D.W., Mohtadi, C., Tuffs, P.S., Generalized Predictive Control - Part I. The Basic Algorithm (1987) Automatica, 23 (2), pp. 137-148Wahlberg, B., Ljung, L., Design variables for bias distribution in tranfer function estimation (1986) IEEE Transactions on Automatic Control, 31 (2), pp. 134-144Shook, D.S., Mohtadi, C., Shah, S.L., Identification for long-range predictive control (1991) IEE Proceedings-D, 138 (1), pp. 75-84Sotomayor, O.Z., Park, S.W., Garcia, C., A simulation benchmark to evaluate the perfomance of advanced control techniques in biological wastewater treatment plant (2001) Brazilian Journal of Chemical Engineering, 18 (1), pp. 81-101Braun, M.W., Rivera, D.E., Stenman, A., A 'Model-on-Demand' identification methodology for nonlinear process systems (2001) International Journal of Control, 74 (18), pp. 1708-1717Henze, M., Grady Jr., C.P., Gujer, W., Marais, G.V.R., Matsuo, T., A general model for single-sludge wastewater treatment system (abbreviated report) (1987) Water Resources, 21 (5), pp. 505-515Chen, S., Billings, S.A., Luo, W., Orthogonal least square methods and their application to nonlinear system identification (1989) International Journal of Control, 50 (5), pp. 1873-1896Barker, H.A., Zhuang, M., Design of pseudo-random perturbation signals for frequency-domain identification of nonlinear systems (1997) Proc. of the 11th IFAC Symposium on System Identification SYSID, pp. 1635-1640. , Kitakyushu, Japan, ppJ. M. V. Lara and B. E. A. Milani, Identification of Neutralization Process Using Multi-level Pseudo-Random Signals, Proc. of the 2003 American Control Conference, ACC2003, Denver, USA, 2003, pp. 3822-3827Aguirre, L.A., A nonlinear correlation function for selecting the time delay in dynamical reconstructions (1995) Physics Letter A, 203, pp. 88-94R. Shridhar and D. J. Cooper, A Tuning Strategy for Unconstrained SISO Model Predictive Control, Ind. Eng. Chem. Res., 36, 1997, pp. 729-746Clarke, D.W., Adaptive Predictive Control (1996) A. Rev. Control, 20, pp. 83-94Yuan, Z., Keller, J., Lant, P., Optimization and control of nitrogen removal, activated sludge process: A review of recent developments (1999) Proc. of the 9th European Conference on Biotechnology, , Brussels, BelgiumOlsson, G., Lessons learnt at ica2001 (2002) Water Science and Technology, 45 (4-5), pp. 1-8Rehnström, A., (2000) Automatic control of an activated sludge process in a wastewater treatment plant - a benchmark study, , Master Thesis, Uppsala University, Sweden