Citation: Xu He, Wenjie Gao, Jinglei Xu, Zhanjun Cheng, Wenchao Peng, Beibei Yan, Guanyi Chen, Ning Li. Machine learning-assisted construction of C=O and pyridinic N active sites in sludge-based catalysts[J]. Chinese Chemical Letters, ;2025, 36(12): 111019. doi: 10.1016/j.cclet.2025.111019 shu

Machine learning-assisted construction of C=O and pyridinic N active sites in sludge-based catalysts

Xu He ^a ,
Wenjie Gao ^a ,
Jinglei Xu ^a ,
Zhanjun Cheng ^a ,
Wenchao Peng ^b ,
Beibei Yan ^a ,
Guanyi Chen ^{a, c} ,
Ning Li ^{a, *,}

a.
School of Environmental Science and Engineering/ Tianjin Research Center for Safe Disposal of Organic Solid Waste and Energy Utilization Engineering, Tianjin University, Tianjin 300072, China
b.
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300050, China
c.
School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China

liningec@tju.edu.cn

Received Date: 21 October 2024
Revised Date: 13 January 2025
Accepted Date: 27 February 2025
Available Online: 28 February 2025

Figures(6)

The type and quantity of active sites on a catalyst surface determine catalytic activity. In this study, machine learning was employed to assist in the construction of C=O and pyridine N active sites using sludge waste. Reactive descriptors, including C%, N%, O%, Fe%, pyrolysis temperature, heating rate, and pyrolysis time were proposed. Decision tree, extra tree, extreme gradient boosting (XGB), automatic relevance determination, and Bayesian ridge regression models were constructed and optimized. Among these, the XGB model was demonstrated with superior accuracy for prediction of C=O sites on the catalyst surface. Additionally, an ensemble model combining extra trees and XGB was developed to predict pyridine N, with R² value as high as 0.80 and minimum root mean square error (RMSE) of 0.1386. The ensemble model demonstrated a 17% improvement in accuracy compared to individual models. The model enables high-throughput screening of construction conditions for C=O and pyridine N. The study found that a pyrolysis temperature above of 500–800 ℃, a heating rate of 10–20 ℃/min, and a heating time of 120–200 min favor the generation of C=O active sites. For pyridine N sites, a pyrolysis temperature between 400 ℃ and 600 ℃, a heating rate of 5–10 ℃/min, and a pyrolysis time of around 150 min are optimal. Experimental validation demonstrated that both models exhibit excellent predictive performance, with prediction errors below 10% in all cases. This research provides a method to assist in the construction of C=O and pyridine N active sites, which is beneficial for guiding the design of sludge catalysts.
- Machine learning,
- Catalyst,
- Active sites,
- Sludge,
- Model ensemble
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