Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

Ghugare, Suhas B.; Tiwary, Shishir; Elangovan, Vinayak; Tambe, Sanjeev S.

doi:10.1007/s12155-013-9393-5

Cited by 89 publications

(41 citation statements)

References 26 publications

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“…This is also true for the models created on data from the open literature, as the resulting database is aggregated from these "homogenous" samples. The proportion of the variability of gross calorific value explained by the present model is lower compared to models by Ghugare et al (2014) or Akkaya (2016); however, this was partially caused by the nature of the empirical material used during the research, which was mixed wood chips commonly used in the industrial energy generation. Our goal was to create a model that represented real conditions in the energy industry.…”

Section: Resultscontrasting

confidence: 58%

“…However, using multiple variables does not automatically mean a greater determination of the variability; Phichai et al (2013) used volatile matter content and free carbon content in their model and were only able to explain about 41% of the variability, Jiménez and Gonzáles (1991) were able to determine about 53% of the variability of gross calorific value through volatile matter and free carbon content. On the other hand, Ghugare et al (2014) developed a model with more than 96% determination using genetic programing on proximate analysis data, and Akkaya (2016) achieved 88% determination of gross calorific value through proximate analysis data with an adaptive neuro-fuzzy inference system model. Most models are built around material collected from one locality, harvested by one technology, from one tree species, etc.…”

Section: Resultsmentioning

confidence: 99%

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Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

et al. 2017

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Biomass utilization is vital for developing sustainability in the bioenergy sector. In this work the effects of high ash content on the heating properties of wood chips were evaluated. In an analysis of 450 wood chips samples, the ash content, moisture content, and gross calorific value were determined, and a generalized linear model was created to identify the relationship between the gross calorific value and the ash content of the wood chips. The mean ash content of the analyzed wood chips samples was 2.64%, the mean moisture content was 38.8%, and the mean gross calorific value was 19.43 MJ kg -1. Statistical analyses showed that 49% of the gross calorific value variability was due to the ash content variability. A one percent increase in ash content resulted in a 0.11 MJ kg -1 decrease of gross calorific value. The estimated costs of ash disposal at various ash contents were calculated. Burning wood chips with 5% ash content would lead to depositing an extra 5.6 megatons in the US or 21.2 megatons in the EU, compared to burning wood chips with 2.5% ash content.

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Section: Resultscontrasting

confidence: 58%

Section: Resultsmentioning

confidence: 99%

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

et al. 2017

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“…It is defined as the energy released per unit mass or per unit volume of fuel after complete combustion, including the energy contained in the water vapor in the exhaust gases [45]. The HHV indicates the best use for biomass fuel, as it describes the energy content [36].…”

Section: Higher Heating Valuementioning

confidence: 99%

Thermal Properties of Biochars Derived from Waste Biomass Generated by Agricultural and Forestry Sectors

et al. 2017

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Waste residues produced by agricultural and forestry industries can generate energy and are regarded as a promising source of sustainable fuels. Pyrolysis, where waste biomass is heated under low-oxygen conditions, has recently attracted attention as a means to add value to these residues. The material is carbonized and yields a solid product known as biochar. In this study, eight types of biomass were evaluated for their suitability as raw material to produce biochar. Material was pyrolyzed at either 350 • C or 500 • C and changes in ash content, volatile solids, fixed carbon, higher heating value (HHV) and yield were assessed. For pyrolysis at 350 • C, significant correlations (p < 0.01) between the biochars' ash and fixed carbon content and their HHVs were observed. Masson pine wood and Chinese fir wood biochars pyrolyzed at 350 • C and the bamboo sawdust biochar pyrolyzed at 500 • C were suitable for direct use in fuel applications, as reflected by their higher HHVs, higher energy density, greater fixed carbon and lower ash contents. Rice straw was a poor substrate as the resultant biochar contained less than 60% fixed carbon and a relatively low HHV. Of the suitable residues, carbonization via pyrolysis is a promising technology to add value to pecan shells and Miscanthus.

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“…Among these, the tree-structured GP forms the most commonly employed GP-implementation. Earlier, in a related study on biomass fuels, GP was employed for the development of nonlinear correlations possessing high prediction accuracies and generalization abilities for the prediction of higher heating values (HHVs) of biomass fuels from the constituents of proximate and ultimate analyses (Ghugare et al 2014). Other recent applications of GP in chemical engineering range from soft-sensor development for biochemical systems to prediction of the synthesis of heat-integrated complex distillation systems (Wang et al 2008).…”

Section: Genetic Programming (Gp)mentioning

confidence: 99%

Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis

Ghugare

Tiwary

Tambe

2014

Int J Syst Assur Eng Manag

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Biomass is a renewable and sustainable source of ''green'' energy. The elemental composition comprising carbon (C), hydrogen (H) and oxygen (O) as major components, is an important measure of the biomass fuel's energy content. Its knowledge is also valuable in: (a) computing material balance in a biomass-based process, (b) designing and operating biomass utilizing efficient and clean combustors, gasifiers and boilers, (c) fixing the quantity of oxidants required for biomass combustion/ gasification, and (d) determining the volume and composition of the combustion/gasification gases. Obtaining the elemental composition of a biomass fuel via ultimate analysis is an expensive and time-consuming task. In comparison, proximate analysis that determines fixed carbon, ash, volatile matter and moisture content is a cruder characterization of the fuel and easier to perform. Thus, there exists a need for models possessing high accuracies for predicting the elemental composition of a solid biomass fuel from its proximate analysis constituents. Accordingly, this study utilizes three computational intelligence (CI) formalisms, namely, genetic programming, artificial neural networks and support vector regression, for developing nonlinear models for the prediction of C, H and O fractions of solid biomass fuels. A large database of 830 biomasses has been used in the stated model development. A comparison of the prediction accuracy and generalization performance of the nine CI-based models (three each for C, H and O) with that of the currently available linear models indicates that the CI-based models have consistently and significantly outperformed their linear counterparts. The models developed in this study have proved to be the best models for the prediction of elemental composition of solid biomass fuels from their proximate analyses.

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Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms

Cited by 89 publications

References 26 publications

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

Thermal Properties of Biochars Derived from Waste Biomass Generated by Agricultural and Forestry Sectors

Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis

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