Factors on Palm Oil Fruit Bunches Production Volume for Biomass Fuel and Biofuel During Cogeneration Processes

Abstract

Studies on palm oil cogeneration systems, and design and analysis to further improve the energy efficiency have been done based on process integration technology. The products during cogeneration are crude palm oil (CPO) and solid wastes which come from empty fruit bunches, fibers and nutshells. However, factors affecting the production of biomass and biofuel from solid wastes and crude palm oil from oil palm fruit bunches for the boilerbased and combustion-based cogeneration can be further explored. This study hence aims to determine these factors, and then expound further in forecasting the production volume of biomass fuel and biofuel produced during cogeneration. For this purpose, the multiple regression (MR) technique is employed, and the results based on the mathematical modelling concept are thus compared. Mathematical models on the production of oil palm fruit bunches are developed via the model-building processes. Data variables are transformed using the ladderpower transformation method from a data set of 31 observations. Two models are developed, namely, Model I is for the production volume on biomass fuel from fresh oil palm fruit bunches, while Model II is the production volume on liquid biofuel from crude palm oil (CPO). There are five independent variables in Model I, and four independent variables in Model II. The four-phase in multiple regression model-building are carried out to change the nonnormal data to normality. The best model obtained by the model transformation method in Model I is M72.2.5 where the main factor is the total workers employed during last pay period, and interaction factors up to the second order are: harvested area interact with yield per hectare, harvested area interact with local delivery average price, harvested area interact with total workers employed during last pay period, yield per hectare interact with local delivery average price, harvested area interact with local delivery average price interact with total workers employed during last pay period and yield per hectare interact with local delivery average price interact with total workers employed during last pay period. The significant factors on the biomass production are the yield per hectare and the harvested area of the oil palm fruit bunches. The mean absolute prediction error (MAPE) value for the best model on model transformation Model II is 2.62 %. Thus, the best model using the model transformation method is said to be excellent and acceptable to forecast for the production volume of biomass fuel and biofuel during cogeneration.

Ridge Regression as Efficient Model Selection and Forecasting of Fish Drying Using V-Groove Hybrid Solar Drier

“…Eight selection criteria (8SC) are always used for model selection purpose. For instance, in the study of Abdullah et al (2015), they found that the application of multiple regression with 8SC was able to model and forecast biomass and biofuel production. Besides, Abdullah et al (2011) used the polynomial regression technique with 8SC to find out the best model to estimate the volumetric stem biomass.…”