TITLE:
Prediction of Low Heating Value of Sugar Cane Bagasse as a Fuel for Industrial Boilers in the High Relative Humidity Region: Case of Cameroon
AUTHORS:
Pierre Kana-Donfack, Maxell Tientcheu-Nsiewe, Denis Tcheukam-Toko, César Kapseu
KEYWORDS:
Sugarcane Bagasse, Relative Humidity, Ash, Low Heating Value
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.14 No.6,
June
26,
2024
ABSTRACT: Many attempts have been made to estimate calorific value of bagasse using mathematical equations, which were created based on data from proximate, ultimate, physical and chemical analysis. Questions have been raised on the applicability of these equations in different parts of the globe. This study was initiated to tackle these problems and also check the most suited mathematical models for the Law Heating Value of Cameroonian bagasse. Data and bagasse samples were collected at the Cameroonian sugarcane factory. The effects of cane variety, age of harvesting, source, moisture content, and sucrose on the LHV of Cameroon bagasse have been tested. It was shown that humidity does not change within a variety, but changes from the dry season to the rainy season; the sugar in the rainy season is significantly different from that collected in the dry season. Samples of the same variety have identical LHV. LHV in the dry season is significantly different from LHV in the rainy season. According to the fact that this study was done for cane with different ages of harvesting, the maturity of Cameroonian sugarcane does not affect LHV of bagasse. Tree selected models are much superior tool for the prediction of the LHV for bagasse in Cameroon compared to others. The standard deviation of these validated models is around 200 kJ/kg compared to the experimental. Thus, the models determined in foreign countries, are not necessarily applicable in predicting the LHV of bagasse in other countries with the same accuracy as that in their native country. There was linear relationship between humidity, ash and sugar content in the bagasse. It is possible to build models based on data from physical composition of bagasse using regression analysis.