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Prediction of combustion activation energy of NaOH/KOH catalyzed straw pyrolytic carbon based on machine learning

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  • Jiang, Wen
  • Xing, Xianjun
  • Zhang, Xianwen
  • Mi, Mengxing

Abstract

Wheat straw, corn straw and sorghum straw were used as raw materials. KOH and NaOH were used as catalysts to prepare straw pyrolytic carbon (SPC) and the characteristics of combustion activation energy (AE) were analyzed by thermogravimetric analysis. The distributed modified Coats-Redfern integration method was used to compute the distributed AE. The predictive models of combustion AE based on Linear Regression (LR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were proposed and compared. The results showed the AE variation trend of three kinds of SPCNaOH, SPCKOH and SPCNa-KOH were basically the same and obviously decreased. In the LR model, degree value was 2 and R2 reached 0.8531. In the SVR model, the kernel function was Polynomial, C = 3000, degree = 4, coef0 = 0.3 and R2 reached 0.9048. In the RFR model, the n_estimators value was 400 and R2 reached 0.9834. Compared with the LR and SVR model, the RFR model was more suitable for the AE prediction of alkali-catalyzed SPC.

Suggested Citation

  • Jiang, Wen & Xing, Xianjun & Zhang, Xianwen & Mi, Mengxing, 2019. "Prediction of combustion activation energy of NaOH/KOH catalyzed straw pyrolytic carbon based on machine learning," Renewable Energy, Elsevier, vol. 130(C), pages 1216-1225.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:1216-1225
    DOI: 10.1016/j.renene.2018.08.089
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
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    1. Teimouri, Zahra & Abatzoglou, Nicolas & Dalai, Ajay K., 2023. "Design of a renewable catalyst support derived from biomass with optimized textural features for fischer tropsch synthesis," Renewable Energy, Elsevier, vol. 202(C), pages 1096-1109.
    2. Rahimi, Mohammad & Abbaspour-Fard, Mohammad Hossein & Rohani, Abbas, 2021. "A multi-data-driven procedure towards a comprehensive understanding of the activated carbon electrodes performance (using for supercapacitor) employing ANN technique," Renewable Energy, Elsevier, vol. 180(C), pages 980-992.

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