Optimizing the Recovery of Latent Heat of Condensation from the Flue Gas Stream through the Combustion of Solid Biomass with a High Moisture Content

Jarosław Kabiesz () and Robert Kubica
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Jarosław Kabiesz: Department of Chemical Engineering and Process Design, Silesian University of Technology, M. Strzody 7, 44-100 Gliwice, Poland
Robert Kubica: Department of Chemical Engineering and Process Design, Silesian University of Technology, M. Strzody 7, 44-100 Gliwice, Poland

Energies, 2024, vol. 17, issue 7, 1-19

Abstract: This study focuses on a specific method of heat recovery in combustion systems especially dedicated to wet biomass. Solid biofuels such as woodchips or bark are sources of renewable energy, a substitute of fossil fuels, of dynamically growing importance due to the energy transformation towards a zero-emission economy. Various solutions are generally known, in particular those based on absorption and compressor heat pumps. The solution presented here eliminates the need for such expensive equipment while maintaining very high efficiency. It involves a system of several suitably configured scrubbers. Chemcad 8 simulation shows how the fluid flow rates affect the efficiency of the proposed solution. Optimal configuration of the system and adjustment of the process parameters (flow rates of water in scrubbers circuits) result in a thermal efficiency as high as 108.2%, close to the maximum theoretically achievable efficiency (111%). The system was compared with other existing solutions for efficiency. The performance of the system was examined under different operating conditions to determine the optimum. The effect of an increased fuel moisture content on efficiency was determined. It was shown that the key to achieving significant cost benefits for such a solution is to optimise the flow rates of the circulating fluids.

Keywords: process integration; process simulation; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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