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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
Abstrakt
Nitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions (NH4-N, NO2-N, NO3-N, MLSS, MLVSS) along with the recorded online measurements (DO, pH, temperature) were used as input data for the ML models. The data from the experiments at 20 °C and 12 °C, respectively, were used for training and testing of three ML algorithms, including artificial neural network (ANN), gradient boosting machine (GBM), and support vector machine (SVM). The best predictive model was the ANN algorithm and that model was further subjected to the 95% confidence interval analysis for calculation of the true data probability and estimating an error range of the data population. Moreover, Feature Selection (FS) techniques, such as Pearson correlation and Random Forest, were used to identify the most relevant parameters influencing liquid N2O predictions. The results of FS analysis showed that NH4-N, followed by NO2-N had the highest correlation with the liquid N2O production. With the proposed ap- proach, a prompt method was obtained for enhancing prediction of the liquid N2O concentrations for short- term studies with the limited availability of measured data.
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Autorzy (6)
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Mohamad-Javad Mehrani
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Min Zheng
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.psep.2022.04.058
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
nr 162,
strony 1015 - 1024,
ISSN: 0957-5820 - Język:
- angielski
- Rok wydania:
- 2022
- Opis bibliograficzny:
- Mehrani M., Bagherzadeh F., Zheng M., Kowal P., Sobotka D., Mąkinia J.: Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor// PROCESS SAFETY AND ENVIRONMENTAL PROTECTION -Vol. 162, (2022), s.1015-1024
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.psep.2022.04.058
- Źródła finansowania:
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 163 razy