Decision Support Model for Determining Fuel in Boiler Machines

Abstract

This investigation seeks to formulate a Decision Support Model (DSM) aimed at identifying the most suitable fuel for boiler systems utilized in industrial contexts, encompassing three distinct fuel categories: natural gas, industrial diesel oil, and coal. The assessment is predicated on four fundamental criteria: cost, calorific value, safety, and emissions. Employing a synergistic methodology that combines Analytic Hierarchy Process (AHP) and Fuzzy Logic, AHP allocates weights to each criterion (cost: 0.503, calorific value: 0.273, safety: 0.145, emissions: 0.079). The Fuzzy Logic approach is utilized to effectively address uncertainty and process subjective assessments. The findings indicate that cost constitutes the paramount determinant, exhibiting the highest weight, succeeded by calorific value, safety, and emissions. In accordance with these weighted criteria, the fuels are ordered as follows: coal (0.794), natural gas (0.653), and industrial diesel oil (0.456). These results underscore that cost remains the predominant factor in fuel selection for industrial boilers, whilst safety and environmental ramifications concurrently exert significant influence. The originality of this inquiry is manifested in its implementation of an all-encompassing DSM for fuel selection, marking a pioneering effort within this domain, which integrates both AHP and Fuzzy Logic to furnish a versatile and resilient decision-making framework. The implications of this research are substantial, as it offers a transparent and systematic approach for fuel selection in industrial environments, providing valuable insights into the optimization of energy resources while taking into account economic, environmental, and safety considerations. Subsequent investigations could further examine the incorporation of renewable energy sources and the ramifications of advancing environmental policies on fuel selection.