THERMAL SCIENCE

International Scientific Journal

Ivan Brandić

,

Lato L. Pezo

,

Neven Voća

,

Josip Leto

,

Jona Šurić

,

Anamarija Peter

,

Nikola Bilandžija

ASSESSING THE PROPERTIES OF MISCANTHUS X GIGANTEUS UNDER VARYING LEVELS OF ASH FERTILIZATION TREATMENT AND REGRESSION NEURAL NETWORK INSIGHT INTO CALORIFIC VALUE

ABSTRACT
The aim of the study was to investigate the changes in ultimate, proximate analysis and calorific properties of Miscanthus x Giganteus with three types of planting materials (two rhizomes - R1 and R2 - and one seedling - S) and three ash fertiliser treatments (P0, P2, and P5) were included in the study. The research further examined their effects on crop yield, stem height and various chemical properties. The results showed that the maximum yield was obtained with the R1 x P2 plant type, while the minimum yield was recorded with the R2 x P2 plant type. In addition, the greatest average stem height (3.34 m) was recorded for the R2 x P5 plant type. Significant differences were also found in the chemical components between the plant types and treatments. For example, the highest ash content of 2.25% was found in plant type 'S' x P5, while the highest coke content of 14.48 % was found in plant type R1 x P5. The statistical analysis confirmed that planting material and ash fertilisation had significant influence on the physicochemical properties of Miscanthus x Giganteus. This consequently affects the calorific value, with the average higher and lower heating value being 18.32 and 17.04 MJ/kg, respectively. The neural regression network models showed robust predictive performance for the higher (HHV) and lower heating value LHV, with low chi-square values (Χ2) and high coefficients of determination (R2).
KEYWORDS
Miscanthus x Giganteus, fertilisation, energy properties, Artificial Neural Network, modelling
PAPER SUBMITTED: 2023-11-07
PAPER REVISED: 2024-01-02
PAPER ACCEPTED: 2024-01-15
PUBLISHED ONLINE: 2024-03-10
DOI REFERENCE: https://doi.org/10.2298/TSCI231107060B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 4, PAGES [3281 - 3292]
REFERENCES
  1. Scarlat, N., et al., Brief on Biomass for Energy in the European Union, EU Science Hub. EC Publication, Luxembourg, Luxembourg, 2019, pp. 1-8
  2. Ben, F. N., et al., Miscanthus in the European Bioeconomy: A Network Analysis, Industrial Crops and Products, 148 (2020), 112281
  3. Peni, D., et al., Green Biomass Quality of Perennial Herbaceous Crops Depending on the Species, Type and Level of Fertilization, Industrial Crops and Products, 184 (2022), 115026
  4. Głowacka, K., et al., Genetic Variation in Miscanthus × giganteus and the Importance of Estimating Ge­netic Distance Thresholds for Differentiating Clones, GCB Bioenergy, 7 (2015), 2, pp. 386-404
  5. Nakajima, T., et al., Carbon Sequestration and Yield Performances of Miscanthus × Giganteus and Mis­canthus Sinensis, Carbon Management, 9 (2018), 4, pp. 415-423
  6. Bilandžija, N., et al., Harvest Systems of Miscanthus × giganteus Biomass: A review, Journal of Central European Agriculture, 21 (2020), 1, pp. 159-167
  7. Parrish, A. S., et al., Miscanthus × giganteus Responses to Nitrogen Fertilization and Harvest Timing in Illinois, USA, Bioenergy Research, 14 (2021), Jan., pp. 1126-1135
  8. Littleton, E. W., et al., Uncertain Effectiveness of Miscanthus Bioenergy Expansion for Climate Change Mitigation Explored Using Land Surface, Agronomic and Integrated Assessment Models, GCB Bioener­gy, 15 (2023), 3, pp. 303-318
  9. Morozova, I., et al., Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices, Applied Sciences (Switzerland), 10 (2020), 13
  10. Silva, F. C., et al., Use of Biomass Ash-Based Materials As Soil Fertilisers: Critical Review of the Exist­ing Regulatory Framework, Journal of Cleaner Production, 214 (2019), Mar., pp. 112-124
  11. Wojcik, M., et al., The Use of Wood Biomass Ash in Sewage Sludge Treatment in Terms of Its Agricul­tural Utilization, Waste and Biomass Valorization, 11 (2020), Nov., pp. 753-768
  12. Maschowski, C., Claudine, M., Bottom Ash of Trees from Cameroon as Fertilizer, Applied Geochemistry, 72 (2016), Sept., pp. 88-90
  13. Yu, C. L., et al., Effects of Fly Ash Application on Plant Biomass and Element Accumulations: A Me­ta-Analysis, Environmental Pollution, 250 (2019), July, pp. 137-142
  14. Demeyer, A., et al., Characteristics of Wood Ash and Influence on Soil Properties and Nutrient Uptake: An Overview, Bioresource Technology, 77 (2001), 3, pp. 287-295
  15. Asquer, C., et al., Biomass Ash Reutilisation As an Additive in the Composting Process of Organic Frac­tion of Municipal Solid Waste, Waste Management, 69 (2017), Nov., pp. 127-135
  16. Ma, L., et al., The Impact of Stand Age and Fertilization on the Soil Microbiome of Miscanthus × gigan­teus, Phytobiomes Journal, 5 (2021), 1, pp. 51-59
  17. Smith, R., Slater, F. M., The Effects of Organic and Inorganic Fertilizer Applications to Miscanthus × giganteus, Arundo Donax and Phalaris Arundinacea, when Grown as Energy Crops in Wales, UK, GCB Bioenergy, 2 (2010), 4, pp. 169-179
  18. Adjuik, T., et al., Application of Hydrochar, Digestate, and Synthetic Fertilizer to a Miscanthus × gigan­teus Crop: Implications for Biomass and Greenhouse Gas Emissions, Applied Sciences (Switzerland), 10 (2020), 24, 8953
  19. Oros, A. E. D., et al., Miscanthus giganteus Biomass for Sustainable Energy in Small Scale Heating Sys­tems, Agriculture and Agricultural Science Procedia, 6 (2015), Dec., pp. 538-544
  20. Donti, P. L., Kolter, J. Z., Machine Learning for Sustainable Energy Systems, Annual Review of Environ­ment and Resources, 46 (2021), Aug., pp. 719-747
  21. Elmaz, F., et al., Predictive Modelling of Biomass Gasification with Machine Learning-Based Regression methods, Energy, 191 (2020), 116541
  22. ***, Memmert Model 30-1060, Memmert GmbH + Co. KG, Schwabach, Germany, www.mem­mert.com/
  23. ***, CEN/TS 14774-2:2009, Solid Biofuels - Determination of Moisture Content; CEN: Brussels, Bel­gium, (2009), standards.iteh.ai/
  24. ***, Muffle Furnace, Nabertherm Controller B170, Lilienthal, Germany, nabertherm.com/
  25. ***, EN ISO 18122:2015, Solid Biofuels, Determination of Ash Content. ISO: Geneva, Switzerland, 2015, www.iso.org/, 2015
  26. ***, CEN/TS 15148:2009, Solid Biofuelsm - Determination of the Content of Volatile Matter. CEN, Brussels, Belgium, standards.iteh.ai/, 2009
  27. ***, Vario Macro CHNS Analyzer, Elementar Analysensysteme GmbH, Meerbusch, Germany, www.elementar.com/
  28. ***, EN 15104:2011, Solid Biofuels, Determination of Total Content of Carbon, Hydrogen and Nitrogen - Instrumental Methods, SIS, Stockholm, Sweden, standards.iteh.ai/, 2011
  29. ***, EN 15289:2011, Solid Biofuels, Determination of Total Content of Sulfur and Chlorine, ISO, Gene­va, Switzerland, standards.iteh.ai/, 2011
  30. ***, Adiabatic Bomb Calorimeter, IKA Analysentechnik GmbH, Meerbusch, Germany, www.ika.com/
  31. ***, CEN/TS 14918:2005, Solid Biofuels - Method for the Determination of Calorific Value, CEN, Brus­sels, Belgium, standards.iteh.ai/, 2005
  32. ***, TIBCO Statistica, v. 13.3.0, TIBCO Software Inc, Palo Alto, CA, USA, 2017, www.tibco.com/products/tibco-statistica
  33. Jolliffe, I. T., Cadima, J., Principal Component Analysis: A Review and Recent Developments, Philos Trans. A Math. Phys. Eng. Sci., 13 (2016), 374
  34. Nguyen, Q. H., et al., Influence of Data Splitting on Performance of Machine Learning Models in Predic­tion of Shear Strength Of Soil, Mathematical Problems in Engineering, 6 (2021), Feb., pp. 1-15
  35. Pezo, L. L., et al., Artificial Neural Network Model of Pork Meat Cubes Osmotic Dehydratation, Hemijs­ka Industrija, 67 (2013), 3, pp. 465-475
  36. Brandić, I., et al., Comparison of Different Machine Learning Models for Modelling the HHV of Bio­mass, Mathematics, 11 (2023), 9
  37. Rajković, D., et al., Yield and Quality Prediction of Winter Rapeseedm - Artificial Neural Network and Random Forest Models, Agronomy, 12 (2022), 1
  38. Yoon, Y., et al., A Comparison of Discriminant Analysis vs. Artificial Neural Networks, J. Oper. Res. Soc., 44 (2017), 1, pp. 51-60
  39. Urzhumtsev, A., et al., On the Use of Logarithmic Scales for Analysis of Diffraction Dana, Acta Crystal­lographica Section D: Biological Crystallography, 65 (2009), Dec., pp. 1283-1291
  40. Wierzbowska, J., et al., Environmental Application of Ash from Incinerated Biomass, Agronomy, 10 (2020), 4
  41. Šurić, J., et al., Wastewater Sewage Sludge Management via Production of the Energy Crop Virginia Mallow, Agronomy, 12 (2022), 7
  42. Saletnik, B., et al., Biochar and Biomass Ash as a Soil Ameliorant: The Effect on Selected Soil Properties and Yield of Giant Miscanthus (Miscanthus × giganteus), Energies, 11 (2018), 10
  43. Esteves, B., et al., Influence of Chemical Composition on Heating Value of Biomass: A Review and Bib­liometric Analysis, Energies, 16 (2023), 10, 4226
  44. Voća, N., et al., Energy Properties and Biomass Yield of Miscanthus × giganteus Fertilized by Municipal Sewage Sludge, Molecules, 26 (2021), 14
  45. Choi, J. S., et al., Improving the Analysis of Sulfur Content and Calorific Values of Blended Coals with Data Processing Methods in Laser-Induced Breakdown Spectroscopy, Applied Sciences (Switzerland), 12 (2022), 23
  46. Anshariah, I. A. M., et al., Correlation of Fixed Carbon Content and Calorific Value of South Sulawesi Coal, Indonesia, IOP Conference Series: Earth and Environmental Science, 473 (2020), 1
  47. Nhuchhen, D. R., Afzal, M. T., The HHV Predicting Correlations for Torrefied Biomass Using Proximate and Ultimate Analyses, Bioengineering, 4 (2017), 1
  48. Gismatulina, Y. A., et al., Evaluation of Chemical Composition of Miscanthus × giganteus Raised in Dif­ferent Climate Regions in Russia, Plants, 11 (2022), 20
  49. Osman, A. I., et al., Physicochemical Characterization of Miscanthus and Its Application in Heavy Metals Removal from Wastewaters, Environmental Progress and Sustainable Energy, 11 (2018), 20, pp. 1058-1067
  50. Yorgun, S., Simsek, Y. E., Fixed-Bed Pyrolysis of Miscanthus × giganteus: Product Yields and Biooil Characterization, Energy Sources, 25 (2003), 8, pp. 779-790
  51. Noushabadi, A. S., et al., Estimation of HHV of Biomass Fuels Based on Ultimate Analysis Using Ma­chine Learning Techniques and Improved Equation, Renewable Energy, 179 (2021), Dec., pp. 550-562
PDF VERSION [DOWNLOAD]
Assessing the properties of Miscanthus x Giganteus under varying levels of ash fertilization treatment and regression neural network insight into calorific value

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence