Startseite Thermo-kinetics, thermodynamics, and ANN modeling of the pyrolytic behaviours of Corn Cob, Husk, Leaf, and Stalk using thermogravimetric analysis
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Thermo-kinetics, thermodynamics, and ANN modeling of the pyrolytic behaviours of Corn Cob, Husk, Leaf, and Stalk using thermogravimetric analysis

  • Mubarak A. Amoloye , Sulyman A. Abdulkareem und Adewale George Adeniyi ORCID logo EMAIL logo
Veröffentlicht/Copyright: 7. Juli 2023
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Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 18 Heft 5

Abstract

In this study, we investigate the thermal stabilities, thermo-kinetic, and thermodynamic behaviours of Corn Cob (CC), Husk (CH), Leaf (CL), and Stalk (CS) during pyrolysis using the Thermogravimetric Analysis (TGA) at a single heating rate of 10 °C/min. Thermo-kinetics and thermodynamic parameters were evaluated for two temperature regions, region I (100–350 °C) and region II (350–500 °C) by employing the Coats–Redfern (CR) integral method to fit the TGA data to sixteen kinetic models. Results showed that diffusion models (D1, D1, D3, and D1) best suited the decomposition of CC, CH, CL, and CS in region I with Ea values of 109.90, 186.01, 129.4, and 78.7 kJ/mol respectively. Similarly, D1, third order model (F3), D3, and nucleation model (P4) with Ea values of 68.50 (CC), 177.10 (CH), 62.10 (CL), and 127.70 (CS) kJ/mol respectively best described residues’ decomposition in region II. Furthermore, kinetic parameters were used to compute the thermodynamic parameters; change in enthalpy (∆H), Gibbs free energy (∆G), and change in entropy (∆S) values for both regions. To study the pyrolytic behaviours of the residues, Artificial Neural Network (ANN) was employed to develop models to predict weight losses in samples by determining the coefficient of determination (R2) and minimum Mean Square Error (MSE). Results showed ANN as a very important tool for predicting the pyrolytic behaviours of corn residues and other biomass samples.

Keywords: artificial neural network; Coats–Redfern; corn; kinetics; thermodynamics
Corresponding author: Adewale George Adeniyi, Department of Chemical Engineering, University of Ilorin, P. M. B. 1515, Ilorin, Nigeria, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: No potential conflict of interest was reported by the authors.

  4. Compliance to ethical standards: This article does not contain any studies involving human or animal subjects.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cppm-2023-0021).

Received: 2023-02-26
Accepted: 2023-06-02
Published Online: 2023-07-07

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. A review of frictional pressure drop characteristics of single phase microchannels having different shapes of cross sections
  4. Research Articles
  5. Taguchi L16 (44) orthogonal array-based study and thermodynamics analysis for electro-Fenton process treatment of textile industrial dye
  6. Green synthesis of silver nanoparticles from Aspergillus flavus and their antibacterial performance
  7. Prediction of effect of wind speed on air pollution level using machine learning technique
  8. Model-based evaluation of heat of combustion using the degree of reduction
  9. Enhanced design of PI controller with lead-lag filter for unstable and integrating plus time delay processes
  10. Effect of operating parameters on the sludge settling characteristics by treatment of the textile dyeing effluent using electrocoagulation
  11. Simultaneous charging and discharging of metal foam composite phase change material in triplex-tube latent heat storage system under various configurations
  12. Optimal design of pressure swing adsorption units for hydrogen recovery under uncertainty
  13. Thermo-kinetics, thermodynamics, and ANN modeling of the pyrolytic behaviours of Corn Cob, Husk, Leaf, and Stalk using thermogravimetric analysis
https://doi.org/10.1515/cppm-2023-0021
Schlagwörter für diesen Artikel
artificial neural network; Coats–Redfern; corn; kinetics; thermodynamics

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. A review of frictional pressure drop characteristics of single phase microchannels having different shapes of cross sections
  4. Research Articles
  5. Taguchi L16 (44) orthogonal array-based study and thermodynamics analysis for electro-Fenton process treatment of textile industrial dye
  6. Green synthesis of silver nanoparticles from Aspergillus flavus and their antibacterial performance
  7. Prediction of effect of wind speed on air pollution level using machine learning technique
  8. Model-based evaluation of heat of combustion using the degree of reduction
  9. Enhanced design of PI controller with lead-lag filter for unstable and integrating plus time delay processes
  10. Effect of operating parameters on the sludge settling characteristics by treatment of the textile dyeing effluent using electrocoagulation
  11. Simultaneous charging and discharging of metal foam composite phase change material in triplex-tube latent heat storage system under various configurations
  12. Optimal design of pressure swing adsorption units for hydrogen recovery under uncertainty
  13. Thermo-kinetics, thermodynamics, and ANN modeling of the pyrolytic behaviours of Corn Cob, Husk, Leaf, and Stalk using thermogravimetric analysis
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