MATEC Web Conf.
Volume 192, 2018The 4th International Conference on Engineering, Applied Sciences and Technology (ICEAST 2018) “Exploring Innovative Solutions for Smart Society”
|Number of page(s)||4|
|Section||Track 3: Food, Chemical and Agricultural Engineering|
|Published online||14 August 2018|
Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy
Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand.
2 Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University 40002, Thailand.
3 Department of Agricultural Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang. 1 Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand.
Corresponding author : firstname.lastname@example.org
The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm-1. The scanning was done with a resolution of 8 cm-1 and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg-1, which was low. Hence, the model was acceptable and cloud be used for screening.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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