Predictive HHV Model for Raw and Torrefied Sugarcane Residues

Conag, Angelique T.; Villahermosa, Jaye Earl R.; Cabatingan, Luis K.; Go, Alchris Woo

doi:10.1007/s12649-018-0204-2

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Complete set of new data, used for this estimation, consisted of 237 examples gathered from the literature on experimentally determined values for FC, VM, ASH and HHV. The literature that is used comprised five papers: (Cordero et al 2001), (Alaba et al 2020), (Conag et al 2019), (Lakovic et al 2021) and (Pattanayak et al 2020), denoted with I, II, III, IV and V, respectively, in the Table 8 where the excerpt of the full data set is given. The complete dataset is available from (Jakšić 2021).…”

Section: Resultsmentioning

confidence: 99%

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Jakšić

Jakšić²,

Guha³

et al. 2022

Soft Comput

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The paper presents the comparative qualitative and quantitative analysis of twelve algorithms for training artificial neural networks (ANN) which predict the higher heating value (HHV) of biomass based on the proximate analysis (fixed carbon, volatile matter, and ash percentage). The twelve networks, with the same structure but different training algorithm, were fed with 318 experimental data triplets from literature for different biomass species and trained with 318 corresponding HHV values used for the supervised learning. Our comparative analysis showed that several algorithms resulted in ANNs generating outputs well correlated with the true measured values of the biomass HHV. Of those, Levenberg-Marquardt algorithm gives the best results in terms of mean squared error calculated on the training set data, while Bayesian regularization gives the best results in terms of regression. When applied to new datasets, unknown to the ANNs trained here, the highest accuracy of the HHV prediction was obtained by Conjugate Gradient, Powell/Beale Restarts training function. It ensured prediction based on the unknown datasets better than Levenberg-Marquardt algorithm. The described approach can be used for predicting the calorific values of different biomass species, including newly proposed ones, as well as for optimizing the HHV for both pure biomass and biomass mixtures.

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Section: Resultsmentioning

confidence: 99%

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Jakšić

Jakšić²,

Guha³

et al. 2022

Soft Comput

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“…Realizing the time-consuming and costly laboratory analyses related to HHVs, Conag et al (2018), focused on predicting the HHVs of raw and torrefied sugarcane bagasse. The suggested equation provided an adequate estimate of the HHV having a mean absolute error of 6.1% and a coefficient of determination of 0.91, whereas the authors suggested that moisture should also be included as a model parameter [12]. Collecting more than 500 data sets from the published literature, Vallejo et al (2020) developed a multilinear model and regression tree for the prediction of the HHVs of hydrochars from various biomasses.…”

Section: Introductionmentioning

confidence: 99%

Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge

et al. 2021

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Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters ((higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN1 (based on C, H, O content) exhibited HHV predicting performance with R2 = 0.974, another model, NN2, was also able to predict HHV with R2 = 0.936 using only C and H as input. Moreover, the inverse model of NN3 (based on H, O content, and HHV) could predict C content with an R2 of 0.939.

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“…Many studies have been performed in attempts to reveal the characteristic of biochar-derived fuels from sugarcane bagasse through dry and wet torrefaction [17][18][19]; however, only a few researchers have studied torrefaction on sugarcane leaves. Conag et al [20] found that after dry torrefaction, the fixed carbon, volatile, ash, and higher heating value of torrefied sugarcane leaves at the temperature range 250-300 °C were reached 65.67, 31.95, 14.78%, and 20.80 MJ/kg, respectively. In a study performed by Pattiya [21], the authors showed that the torrefaction condition that gave the highest torrefied sugarcane leaves heating value and energy densification was 280 °C.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of solid biofuels derived from sugarcane leaves with two different thermochemical conversion methods: wet and dry torrefaction

2021

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This work describes the conversion of sugarcane leaves into biocoal with two thermal processes: wet torrefaction (subcritical water, 175-250 °C) and dry torrefaction (nitrogen atmosphere, 225-300 °C). The residence time was 30 min for both processes. The effects on physical and energy characteristics, including mass and energy yield, proximate and ultimate analyses, fiber analysis, higher heating value (HHV), structural parameters determined by Fourier-transform infrared spectroscopy, and O/C and H/C atomic ratios were used for comparisons. The results showed that increasing the reaction temperature lowers the mass yield; however, it also significantly improves the fuel ratio of torrefied samples. The highest HHV of wet and drytorrefied samples were 23.31 and 22.07 MJ/kg, respectively. The best removal of ash and sulfur content was obtained under wet torrefaction. Moreover, wet torrefaction was recommended as a suitable process for hemicellulose depolymerization. At 250 °C, the wet-torrefied sample had the highest fuel ratio (0.48) and was suited for biomass co-firing. The finding that wet-torrefied samples reached the same range of lignite at lower reaction temperatures than dry-torrefied samples was particularly intriguing. Torrefaction at the temperatures below 250 °C did not prove to have a statistically significant effect on the energy properties of the dry-torrefied samples. Therefore, wet torrefaction is a promising process in the thermochemical conversion of sugarcane leaves into solid biofuel.

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Predictive HHV Model for Raw and Torrefied Sugarcane Residues

Cited by 14 publications

References 41 publications

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Comparing artificial neural network algorithms for prediction of higher heating value for different types of biomass

Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge

Comparative study of solid biofuels derived from sugarcane leaves with two different thermochemical conversion methods: wet and dry torrefaction

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