Microwave-Assisted Preparation of Solid Recovered Fuel from Food Waste and its Quality Prediction Using Linear Programming

Abstract

This study presents a novel method for producing solid recovered fuel (SRF) from food waste (FW) using microwave-assisted heating. FW with high moisture content was mixed with plastics to enhance the quality of SRF, achieving an 84% moisture reduction in just 6 min under 1000 W microwave irradiation. This method achieves moisture reduction much faster than conventional methods such as hot air drying, which typically require several hours. Dehydration efficiency was optimized, particularly when the initial moisture content was below 30%. This method offers a faster, energy-efficient alternative to traditional processes such as anaerobic digestion, contributing to waste-to-energy advancements and sustainability by reducing processing time and energy demands. A linear programming model was developed to predict the net calorific values (NCV) of SRFs, achieving an error margin of less than 4.95%, which compares favorably with industry benchmarks. The study also showed that adding polypropylene (PP) plastic increased volatile content and reduced ash content, enhancing SRF quality. These findings highlight a cost-effective and scalable solution for converting FW into renewable energy, paving the way for broader adoption in waste management and sustainable energy sectors. This research provides practical insights for improving waste-to-fuel conversion practices while addressing key challenges in FW processing.

The findings of this study offer valuable insights for industries involved in renewable energy generation, providing a practical approach for assessing the quality of solid recovered fuel (SRF). This method not only enhances the accuracy of SRF quality determination but also contributes to significant time and cost savings, supporting more efficient and sustainable waste-to-energy conversion processes.

This study successfully produced solid recovered fuel (SRF) from food waste (FW), effectively addressing the challenges posed by its high moisture content. The material properties of the SRF were utilized to develop a linear programming model capable of accurately predicting SRF quality.