Periodic Control of a Wastewater Treatment Process to Improve Productivity
Authors: Muhammad Rizwan Azhar, Emadadeen Ali
Abstract:
In this paper, periodic force operation of a wastewater treatment process has been studied for the improved process performance. A previously developed dynamic model for the process is used to conduct the performance analysis. The static version of the model was utilized first to determine the optimal productivity conditions for the process. Then, feed flow rate in terms of dilution rate i.e. (D) is transformed into sinusoidal function. Nonlinear model predictive control algorithm is utilized to regulate the amplitude and period of the sinusoidal function. The parameters of the feed cyclic functions are determined which resulted in improved productivity than the optimal productivity under steady state conditions. The improvement in productivity is found to be marginal and is satisfactory in substrate conversion compared to that of the optimal condition and to the steady state condition, which corresponds to the average value of the periodic function. Successful results were also obtained in the presence of modeling errors and external disturbances.
Keywords: Dilution rate, nonlinear model predictive control, sinusoidal function, wastewater treatment.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332332
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[1] S, Ahsan, S, Koneco, K, Ohta, T, Mizuno, and K, Kani, Use of some natural and waste materials for wastewater treatment, (2001) 3738-3742.
[2] Alex, J., Beteau, J.F., Copp, J.B., Hellinga, C., Jeppsson, U., Marsililibelli,S, et al.(1999), Benchmark for evaluating control strategies in wastewater treatment plants. In Proceedings ECC-99.
[3] Iqbal J. and Guria C., Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm, Chem. Eng. Res. Des., 87, (2009) 1481-1496.
[4] S. Menzi, and M. Steiner, "Model-Based Control for nitrogeneliminating wastewater treatment plants", IEEE conference on Control Applications, (1995), pp. 842-847.
[5] A. Aoyama, F. Doyle III, V. Subramnian, "Control-affine Neural Approach for Non-linear Minimum Phase Non-linear Process Control", J. Process Control, (1996), pp. 17-26.
[6] E. Ali and E. Zafiriou, Optimization-based tuning of non-linear model predictive control with state estimation, Journal of Process Control, 3, (1993) 97-107.
[7] S. J. Qin, and T. A. Badgwell, An overview of industrial model predictive technology, in Fifth International Conference on Chemical Process Control, AIChE Symposium Series 316, 93, Jeffrey C. Kantor, Carlos E. Garcia, and Brice Carnahan, eds., (1997) 232-256.
[8] M. Morari, and J. Lee, Model predictive control: past, present and future, Computers and Chemical Engineering, 21, (1999), 667-682.
[9] M. Al-haj Ali, E. Ali, Broadening the polyethylene molecular weight Distribution by periodic hydrogen feed rates, Macromolecular Reaction Engineering, 5, (2011) 85-95.
[10] Ajbar. A, Ali. E, Periodic Control of a Reverse Osmosis Desalination Process, Journal of Process Control 22 (2012) 218- 227.
[11] O-Brien.M, Mack.J, Lennox.B and Wall A, Model predictive control of an activated sludge process: A case study, Control Engineering Practice 19 (2011) 54-61.
[12] Zhao Y., and Skogestad S., (1997) Comparison of various control configurations for continuous bioreactor``, Ind. Eng. Chem. Res., 36, 697-705.
[13] Sundstrom D., Keli H., Molvar A., (1973). "The use of dimensionless groups in the design of activated sludge reactors--, Water Research, 7, 1905-1913.