THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Ana Matin

,

Karlo Špelić

,

Vanja Jurišić

,

Božidar Matin

,

Mateja Grubor

,

Ivana Tomić

,

Tugomir Majdak

,

Ivan Brandić

online first only

The possibility of modeling agricultural biomass ash by neural networks concerning proximate analysis inputs

ABSTRACT
Agricultural biomass is an important renewable energy source with significant environmental and economic benefits. However, high ash content in biomass can lead to problems such as slagging, fouling, and corrosion and can reduce the efficiency of energy systems. This study analyzes the proximate composition of different biomass samples, focusing on ash content, and uses machine learning to model ash content based on other components. Six biomass types, including rapeseed, barley, wheat, corn, soybean and sunflower, were examined to analyze the content of coke, fixed carbon (FC), volatile matter (VM) and ash. The results showed considerable variability, with ash content ranging from 8.25 % for rapeseed to 12.3 % for soybean. Artificial neural networks (ANN) were used to model ash content with a high accuracy of R² = 0.92. The model effectively estimated the ash content based on the input parameters and demonstrated the potential of machine learning to optimize biomass selection for energy production.
KEYWORDS
biomass, artificial neural networks, proximate analysis, ash content, modelling
PAPER SUBMITTED: 2024-06-18
PAPER REVISED: 2024-09-03
PAPER ACCEPTED: 2024-09-13
PUBLISHED ONLINE: 2024-11-09
DOI REFERENCE: https://doi.org/10.2298/TSCI240618238M
REFERENCES
  1. Sasongko, N. A., et al., Review of Types of Biomass as a Fuel-Combustion Feedstock and Their Characteristics, Adv. Food Sci. Sustain. Agric. Agroind. Eng.,6 (2023), pp. 170-184
  2. Johnson, J. M. F., et al., Agricultural Opportunities to Mitigate Greenhouse Gas Emissions, Environ. Pollut. 150 (2007), pp. 107-124
  3. European Parliament and the Council of the European Union. Directive (EU) 2018/2001 of the European Parliament and of the Council on the Promotion of the Use of Energy from Renewable Sources, Off. J. Eur. Union (2018), pp. 82-209
  4. Mehedintu, A., et al., Evolution and Forecasting of the Renewable Energy Consumption in the Frame of Sustainable Development: EU vs. Romania, Sustainability, 13 (2021), 10327
  5. Mohr, A., et al., Food Security Criteria for Voluntary Biomass Sustainability Standards and Certifications, Biomass Bioenergy, 89 (2016), pp. 133-145
  6. Odzijewicz, J. I., et al., Utilization of Ashes from Biomass Combustion, Energies, 15 (2022), 9653
  7. Ma, D., et al., Evaluation of Ash/Slag Heavy Metal Characteristics and Potassium Recovery of Four Biomass Boilers, Biomass Bioenergy, 173 (2023),106770
  8. Zhai, J., et al., Beneficial Management of Biomass Combustion Ashes, Renew. Sustain. Energy Rev., 151 (2021), 111555
  9. Míguez, J. L., et al., Review of the Use of Additives to Mitigate Operational Problems Associated with the Combustion of Biomass with High Content in Ash-Forming Species, Renew. Sustain. Energy Rev., 141 (2021), 110502
  10. Nam, N. H., et al., Physico-Chemical Characterization of Forest and Agricultural Residues for Energy Conversion Processes, VJCH, 58 (2020), pp. 735-741
  11. Liu, X., et al., Review of Artificial Neural Networks in the Constitutive Modeling of Composite Materials, Compos. Part B Eng., 224 (2021), 109152
  12. Jarrett, A., et al., Machine-Learning Based Thermal Conductivity Prediction of Propylene Glycol Solutions Real Time Heat Propagation Approach, Thermal Science, 27 (2023), 4, pp. 2925-2933
  13. Abdulrahim, H., et al.,Machine Learning Models to Prediction Opic Crude Oil Production, Thermal Science, 26 (2022), Special Issue 1, pp. 437-443
  14. Chen, Y., et al., A Review of the Artificial Neural Network Models for Water Quality Prediction, Appl. Sci., 10 (2020), 17, 5776
  15. Khan, M., et al., Artificial Neural Networks for the Prediction of Biochar Yield: A Comparative Study of Metaheuristic Algorithms, Bioresour. Technol., 355 (2022), 127215
  16. Zhao, H., et al., Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges, U.S. Department of Energy, Office of Scientific and Technical Information, Oak Ridge, USA, 2022. Available at: www.osti.gov/biblio/1968870
  17. Ramachandra, R., Mandal, S., Prediction of fly ash concrete type using ANN and SVM models, Innovative Infrastructure Solutions, 8 (2023), 1, pp. 1-12
  18. Abhishek, R., et al., Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques, Journal of Soft Computing in Civil Engineering, 7 (2023), 2, pp. 115-137
  19. Lopez-Arevalo, I., et al., A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning, Entropy, 22 (2020), 12, pp. 1-21
  20. Chen, D., et al., Deep Residual Learning for Nonlinear Regression, Entropy, 22 (2020), 2, 193
  21. Reyad, M., et al., A Modified Adam Algorithm for Deep Neural Network Optimization, Neural Comput. Appl., 35 (2023), pp. 17095-17112
  22. Adeoti, O. A., Osanaiye, P. A., Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. Int. J. Comput. Appl., 69 (2013), pp. 8-12
  23. Figueiredo Filho, D. B., et al., What Is R2 All About?, Leviathan, 3 (2011), 60
  24. Brandić, I., et al., Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass, Mathematics, 11 (2023), 2098
  25. Tahir, M. H., et al., Demonstrating the Suitability of Canola Residue Biomass to Biofuel Conversion via Pyrolysis through Reaction Kinetics, Thermodynamics and Evolved Gas Analyses, Bioresour. Technol., 279 (2019), pp. 67-73
  26. Shen, J., et al., The Prediction of Elemental Composition of Biomass Based on Proximate Analysis, Energy Convers. Manag., 51 (2010), pp. 983-987
  27. Sulaiman, M. A., et al., Experimental Characterization of Maize Cob and Stalk Based Pellets for Energy Use, Eng. J., 23 (2019), pp. 117-128
  28. Motghare, K. A., et al., Comparative Study of Different Waste Biomass for Energy Application, Waste Manag., 47 (2016), pp. 40-45
  29. Kułażyński, M., et al., Technological Aspects of Sunflower Biomass and Brown Coal Co-Firing, J. Energy Inst., 91 (2018), pp. 668-675
  30. Casoni, A. I., et al., Conversion of Sunflower Seed Hulls, Waste from Edible Oil Production, into Valuable Products, J. Environ. Chem. Eng., 7 (2019), 1, 102893
  31. Zhang, Z., et al., Ash Content Prediction of Coarse Coal by Image Analysis and GA-SVM, Powder Technol., 268 (2014), pp. 429-435
  32. Doshi, V., et al., Development of a Modeling Approach to Predict Ash Formation during Co-Firing of Coal and Biomass, Fuel Process. Technol., 90 (2009), pp. 1148-1156
  33. Borello, D., et al., Prediction of Multiphase Combustion and Ash Deposition within a Biomass Furnace, Appl. Energy., 101 (2012), pp. 413-422
  34. Bekat, T., et al., Prediction of the Bottom Ash Formed in a Coal-Fired Power Plant Using Artificial Neural Networks, Energy, 45 (2012), pp. 882-887
  35. Grebovic, M., et al., Overcoming Limitations of Statistical Methods with Artificial Neural Networks, Proceedings of the International Arab Conference on Information Technology (ACIT), Abu Dhabi, United Arab Emirates, (2022), pp. 1-6
  36. Nicodemus, E. R., A Methodology to Assess and Improve the Physics Consistency of an Artificial Neural Network Regression Model for Engineering Applications, Adv. Model. Simul. Eng. Sci., 9 (2022), 11
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The possibility of modeling agricultural biomass ash by neural networks concerning proximate analysis inputs