Modelling of biomass briquette storage processing system for electrical grid supply

Slow improvement of industrial waste usage in the energy mix portfolio for electricity makes independent power producers to have less confidence to produce renewable energy for their long-term profit and continuous stable income. Among the issues in the biomass-energy supply chain management is the unreliable biomass waste supply as energy conversion feedstock. This study provides a new theoretical contribution in the area of bioenergy supply chain optimization. The problem faced in electricity production based on oil palm fibre waste is unsustainability of raw material supply to ensure sustainable electricity production. Oil palm fibre waste production is significantly related to the volume of fresh fruit bunch (FFB) processed by a mill. The quantity of FFB processed by the mill is related to several factors such as the market price of oil palm, weather, pollution, unstable FFB supply from farmers to the mills, unsubstantiated co-operation between the factory and supplier, dependence on a single supplier, and variation in harvesting time. The need to identify oil palm fibre waste storage level is to optimize electricity production and profit earned by a palm oil mill. As the fully desired fuel is bio-methane gas, the anaerobic digestion technique is suggested. This study applied the simulation mathematical modelling approach to optimize profit based on feedstock supply. From the developed model, it was found that the cost of biomass briquette import, the levelised cost of electricity (LCOE) value, the rate of money exchange, and the number of plant-operating hours have high significant effects on the profit earned compared to other independent variables. This study provides a solution that researchers and practitioners can use in order to understand the resources required, and to realize the potential values of their waste. Future research is clearly needed to reveal better insights into plant operation, material specification and financial analysis.