Abstract
The biological wastewater treatment plant, which uses microbial community to remove organic matter and nutrients in wastewater, is known as its nonlinear behavior and uncertainty to operate. In spite of strong needs of automatic monitoring of nutrients, it is thought that tremendous expense may be required to install equipments related with remote control system, especially on-line sensors for monitoring organic and nutrient concentrations in the treatment processes. In this research, as a cost-effective tool for replacing expensive on-line sensor, PNN(Polynomial Neural Network) models were developed to estimate the NOx-N and ammonia concentrations by only using on-line values of ORP, DO and pH at the wastewater treatment plant. Developed PNN model could estimate the NOx-N and ammonia profile well. However, the error was increased at the first anoxic period of the first sub-cycle and NOx-N accumulation was occurred at the sub-cycle. To deal with those errors, the rule-base-compensator was developed based on operational knowledge.
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References
Lee, D.S., Park, J.M.: Neural Network Modeling for On-line Estimation of Nutrient Dynamics in a Sequentially-operated Batch Reactor. Journal of Biotechnology 75, 229–239 (1999)
Choi, D.J., Park, H.: A Hybrid Artificial Neural Network as a Software Sensor for Optimal Control of a Wastewater Treatment Process. Wat. Res. 35(16), 3959–3967 (2001)
Charef, A., Ghauch, A., Baussand, P., Martin-Bouyer, M.: Water Quality Monitoring Using a Smart Sensing System. Measurement 28, 219–224 (2000)
Zyngier, D., Araujo, O.Q.F., Coelho, M.A.Z., Lima, E.L.: Robust Soft Sensors for SBR Monitoring. Wat. Sci. Tech. 43(3), 101–105 (2001)
Luccarini, L., Porra, E., Spagni, A., Ratini, P., Grilli, S., Longhi, S., Bortone, G.: Soft Snsors for Control of Nitrogen and Phosphorus Removal from Wastewaters by Neural Networks. Wat. Sci. Tech. 45(4-5), 101–107 (2002)
Graybill, F.A.: Theory and Application of the Linear Model. Duxbury Press, CA (1976)
Kim, S., Vachtsevanos, G.J.: An Intelligent Approach to Integration and Control of Textile Processes. Information Sciences 123, 181–199 (2000)
Poo, K.M., Jun, B.H., Im, J.H., Ko, J.H., Woo, H.J., Kim, C.W.: ORP/DO Based Control and Remote Monitoring System for Nitrogen Removal in SBR. In: 4th International symposium on advanced environmental monitoring. Jeju Hyatt hotel, Korea (2002)
Bae, H.: Remote Process Management System Based Upon Artificial Intelligence for the Process Control of the SBR Type of the Piggery Wastewater Treatment Plant. In: IWA Specialty Symposium on Strong Nitrogenous and Agro-Wastewater, Seoul, Korea (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, Y. et al. (2006). Soft Sensor Using PNN Model and Rule Base for Wastewater Treatment Plant. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_184
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DOI: https://doi.org/10.1007/11760191_184
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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