A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge. (1st October 2021)

• Record Type: Journal Article
• Authors: Djandja, Oraléou Sangué; Duan, Pei-Gao; Yin, Lin-Xin; Wang, Zhi-Cong; Duo, Jia
• Is Part Of: Energy. Volume 232(2021)
• Publication Date: 2021-10-01
• Subjects: Hydrothermal carbonization -- Sewage sludge -- Hydrochar -- Nitrogen content -- Machine learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121010
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this work, 138 datapoints, including elemental composition and ultimate analysis of various types of sewage sludge, and the hydrothermal carbonization reaction conditions, are used to develop a prediction model for the nitrogen content of the hydrochar. The results suggested that a two-layer feedforward neural network with five (05) neurons in the hidden layer can accurately predict the nitrogen content of the hydrochar based on the reaction temperature and the contents of nitrogen, carbon, volatiles and fixed carbon in the feedstock. Over 100 runs, the R 2 and RMSE are in [87.547–99.097%] and [0.243–1.431] wt.% (db), respectively. Moreover, a statistical and regression analysis revealed that the sewage sludge-N is the main contributor to the hydrochar-N. Mostly, 40–70% of sewage sludge-N goes to hydrochar-N. The results are consistent with previous experimental reports, and this model can be used to predict the sewage sludge-derived hydrochar-N. Graphical abstract: Image 1 Highlights: The dataset regarding hydrochar was extracted from published papers. Statistical analysis was conducted. Neural networks model showed good results in predicting hydrochar-N. Appropriate inputs were critical for the hydrochar-N prediction. The most crucial factor for adjusting hydrochar-N was the nitrogen.