Prediction of Equations for Higher Heating Values of Biomass Using Proximate and Ultimate Analysis

Krishnan, Renjith; Hauchhum, Lalhmingsanga; Gupta, R.; Pattanayak, Satyajit

doi:10.1109/epetsg.2018.8658984

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Some of the studies that have applied prediction models to estimate biomass properties include; Park et al (2023) estimated higher heating value using proximate or ultimate analysis. Krishnan et al (2019) also used proximate analysis to estimated biomass' HHV. Park et al (2023) developed a model to estimate the amounts of lignin, cellulose, and hemicellulose from ultimate and proximate analyses.…”

Section: Higher Heating Value (Hhv)mentioning

confidence: 99%

Enhancing herbaceous biomass pellets quality by blending with woody biomass and plastic additives, and post-pelletization torrefaction and optimization processes: A review

2024

JETP

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Current research emphasizes the use of assorted biomass resources of varying quality for pelletizing. Biomass pellet fuel is one of the most common and essential ways of harnessing biomass energy. Herbaceous biomasses like corn stovers, switchgrass and miscanthus are abundant in nature and when used to produce pellets, they are of low quality. This study provides an overview of methods for enhancing herbaceous biomass pellets quality through blending with woody biomasses like pine, eucalyptus and spruce saw dusts, use of plastic additives like linear lowdensity polyethylene and low-density polyethylene, post-pelletization torrefaction, and production of pellets at optimum conditions. The review revealed that the use of biomasses from wood and plastics as additives to herbaceous biomass, improved the pellet properties like strength, durability and higher heating values to great extents, while ash contents decreased. Post-pelletization torrefaction studies showed that there was a noticeable improvement in higher heating value. Finally, there was a general improvement of pellet qualities when pellets are produced at optimum conditions as depicted by the review of optimization studies on pelletization. Generally, each of these methods improves the pelletization of herbaceous biomass to different extents. Some studies have focused on combination of two or three of these methods in which the pellet properties are further enhanced. Therefore, there is need to explore the combination of these methods reviewed to produce pellets and evaluate them against internationally set pellet standards for commercialization.

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Section: Higher Heating Value (Hhv)mentioning

confidence: 99%

Enhancing herbaceous biomass pellets quality by blending with woody biomass and plastic additives, and post-pelletization torrefaction and optimization processes: A review

2024

JETP

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“…The final set of data consisted of 318 training input records and corresponding 318 outputs. All input data, used in the process of training, testing and validation of ANNs, are taken from the references (Demirbaş et al 2004), (Krishnan et al 2018) and (Nhuchhen et al 2012) denoted in tables with i, ii and iii, respectively. The whole data set along with the custom programmed standalone MATLAB functions is published at Mendeley Data repository with open access (Jakšić 2021).…”

Section: Input Data For Training Annsmentioning

confidence: 99%

“…For instance, it has been shown that the fraction of oxygen can be a predictor of HHV of gaseous, liquid, and solid fuels (Merckel et al 2017). Due to the fact that HHV is correlated with the composition of the raw material used as fuel, there are numerous mathematical models for the estimation of calorific values of fuels based on ultimate analysis, proximate analysis, physical composition, chemical composition or structural analysis (Krishnan et al 2018), (Cordero et al 2001), (Nhuchhen et al 2012)... In these papers, as well as in the references cited therein, it is shown that HHV can be determined based on the proximate analysis, i.e.…”

Section: Introductionmentioning

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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“…However, designing unique methods for extracting fuels from MSW requires a grasp of the MSW's important properties, notably its High Heating Value (HHV) and elemental composition, which provide professionals' insight into the MSW's value for fuel generation [11][12][13]. The heating value is used to determine the amount of energy recovered from waste during conversion [14,15]. Even if the heating values are measured experimentally using a bomb calorimeter, the present global economic trend demands assessing the heating value of MSW in order to reduce the cost of energy generation.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Higher Heating Value for MSW Using DSVM and BSOA

Jose¹,

Sasipraba²

2023

Intelligent Automation &Amp; Soft Computing

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In recent decades, the generation of Municipal Solid Waste (MSW) is steadily increasing due to urbanization and technological advancement. The collection and disposal of municipal solid waste cause considerable environmental degradation, making MSW management a global priority. Waste-to-energy (WTE) using thermochemical process has been identified as the key solution in this area. After evaluating many automated Higher Heating Value (HHV) prediction approaches, an Optimal Deep Learning-based HHV Prediction (ODL-HHVP) model for MSW management has been developed. The objective of the ODL-HHVP model is to forecast the HHV of municipal solid waste, based on its oxygen, water, hydrogen, carbon, nitrogen, sulphur and ash constituents. In addition, the ODL-HHVP model contains a Deep Support Vector Machine (DSVM) regression component that can accurately predict the HHV. In addition, the Beetle Swarm Optimization (BSO) method is utilised as a hyperparameter optimizer in conjunction with the DSVM model, resulting in the highest HHV prediction accuracy. A comprehensive simulation study is conducted to validate the performance of the ODL-HHVP method. The Multiple Linear Regression (MLR), Genetic Programming (GP), Resilient backpropagation (RP), Levenberg Marquardt (LM) and DSVM approaches have attained an ineffective result with RMSEs of 4.360, 2.870, 3.590, 3.100 and 3.050, respectively. The experimental findings demonstrate that the ODL-HHVP technique outperforms existing state-of-art technologies in a variety of respects.

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Prediction of Equations for Higher Heating Values of Biomass Using Proximate and Ultimate Analysis

Cited by 11 publications

References 9 publications

Enhancing herbaceous biomass pellets quality by blending with woody biomass and plastic additives, and post-pelletization torrefaction and optimization processes: A review

Enhancing herbaceous biomass pellets quality by blending with woody biomass and plastic additives, and post-pelletization torrefaction and optimization processes: A review

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

Estimation of Higher Heating Value for MSW Using DSVM and BSOA

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