Gradient boosting decision tree in the prediction of NOx emission of waste incineration

Xiaosong Ding, Chong Feng, Peiling Yu, Kaiwen Li and Xi Chen

Energy, 2023, vol. 264, issue C

Abstract: This paper investigates the real-time prediction of nitrogen oxides (NOx) emission by using around 17000 samples involved in a collection of three-day real data from a waste incineration power plant. To disclose the relationship between the ammonia (NH3) ejection and NOx emission, we choose the NOx reduction from inlet to outlet rather than the NOx concentration monitored by continuous emission monitoring system (CEMS). A hybrid procedure is developed to select appropriate features from the large and unsynchronized data, with which we establish a model based on the gradient boosting decision tree (GBDT) for the prediction. Computational experiments demonstrate that, with root mean square error (RMSE) values being 1.851 and 3.593 for training and test data, respectively, GBDT outperforms its two popular counterparts, supporting vector regression (SVR) and long short-term memory (LSTM). Shapley additive explanations (SHAP) is also conducted for analysis.

Keywords: Waste incineration; Supporting vector regression; Long short-term memory; Gradient boosting decision tree; Feature selection; Nitrogen oxides emission (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:264:y:2023:i:c:s0360544222030602

DOI: 10.1016/j.energy.2022.126174

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