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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
Abstract
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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Authors (6)
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Min Zheng
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.psep.2022.04.058
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
no. 162,
pages 1015 - 1024,
ISSN: 0957-5820 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.1016/j.psep.2022.04.058
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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