Accurate prediction of chemical exergy of technical lignins for exergy-based assessment on sustainable utilization processes

Youwang Huang, Haiyong Wang, Xinghua Zhang, Qi Zhang, Chenguang Wang and Longlong Ma

Energy, 2022, vol. 243, issue C

Abstract: The exergy-based assessment on the sustainable utilization processes of technical lignin is important for potential identify and process optimization. In this study, chemical exergy of technical lignin was evaluated for the first time based on the Gibbs free energy relation. The chemical exergy of technical lignin was from 17653.89 to 33337.92 kJ kg−1.The effects of O/C and H/C ratios on the chemical exergy and standard entropy were investigated by using contour plot analysis. The chemical exergy of technical lignin is more significantly influenced by the O/C ratio, compared with the H/C ratio. Three types of prediction models including artificial neural network model with the input of elemental composition, HHV-based correlation, and element-based correlation were developed. The artificial neural network model has an excellent performance of predicting the chemical exergy of technical lignin, with the prediction relative error of less than ±0.15% under the confidential level of 97%. The prediction relative errors of the HHV-based correlation and the element-based correlation are within ±1.0% and ±2.5%, respectively. This work will provide the basic data for exergy-based assessment on the valorization processes of technical lignin, which is an important aspect of improving the economic level of biorefinery industry.

Keywords: Technical lignin; Chemical exergy; Standard entropy; Prediction model; Artificial intelligence technique (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:243:y:2022:i:c:s0360544221032904

DOI: 10.1016/j.energy.2021.123041

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