Enhanced Biomass Characteristics Index in palm biomass calorific value estimation
Oil palm industry contributes a huge amount of valuable crude palm oil, and simultaneously producing a large quantity of plantation waste or biomass, which will be utilized as fuel. In order to give a clear insight of the energy output estimation from the biomass, a comprehensive study on the physic...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier Ltd
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/72299/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976480940&doi=10.1016%2fj.applthermaleng.2016.05.090&partnerID=40&md5=3251b846b8e4bb34d861332728714caa |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | Oil palm industry contributes a huge amount of valuable crude palm oil, and simultaneously producing a large quantity of plantation waste or biomass, which will be utilized as fuel. In order to give a clear insight of the energy output estimation from the biomass, a comprehensive study on the physical properties of the biomass: bulk density and moisture content is crucial. In a conventional approach, these properties are obtained through empirical methods on individual sample basis. However, the conventional empirical methods have several drawbacks: (i) require a huge amount of experimental results to construct biomass properties’ curve (ii) data variation affects the accuracy of analysis result. These create a limitation in properties estimation and further affecting the optimum biomass utilization. To tackle this issue, there is a need to search for a direct representation of the properties. A Biomass Characteristics Index (BCI) is proposed to represent the relationship between bulk density and moisture content. A numerical framework is developed to determine the BCI. This index is used to estimate the biomass bulk density and moisture content before the calorific value calculation. A regression graph is plotted to illustrate the relationship among those values with respect to different appearance shapes of biomass. The result shows that different size and shape of biomass has its own specific BCI. The classification of biomass according to its specific BCI can forecast the related bulk density and moisture content. Therefore, it reduces the hassle, especially in terms of time constraint to get those values through conventional empirical method. This will increase the overall biomass operational management efficiency. |
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