COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

P. Kern, C. Wolf, D. Gaida, M. Bongards, Seán McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.
Original languageEnglish
Title of host publicationAutomation Science and Engineering (CASE), 2014 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages812-817
DOIs
Publication statusPublished - Aug 2014
Event2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014) - Taipei, Taiwan, Province of China
Duration: 18 Aug 201422 Aug 2014

Conference

Conference2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)
CountryTaiwan, Province of China
CityTaipei
Period18/08/201422/08/2014

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    Kern, P., Wolf, C., Gaida, D., Bongards, M., & McLoone, S. (2014). COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques. In Automation Science and Engineering (CASE), 2014 IEEE International Conference on (pp. 812-817). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CoASE.2014.6899419