Experimental and techno-economic analysis of solar-assisted heat pump drying of biomass

Drying enhances the attributes of biomass, such as storability and heating value. In regions with low solar radiation, solar drying has demonstrated limited economic viability. Hybrid systems, combining solar drying with a stable heat source such as a heat pump, present an opportunity to improve drying performance and achieve cost savings, especially with time-variable electricity pricing. To quantify this potential, we constructed an experimental drying system in Finland, incorporating an air-source heat pump and solar thermal collectors for drying woody biomass. Experimental work, consisting of 316 h of operation in varied conditions, evaluated the performance of the hybrid drying system. Subsequently, we developed data-driven models to estimate drying rates and power consumption in both hybrid and solar operation modes. Finally, these models were integrated into a techno-economic optimization model to assess the economic feasibility of the concept through hourly simulations. Hybrid drying experiments resulted in a significantly improved average drying rate (33.0 kg/h +/- 5.4 kg/h) compared to solar drying (9.0 kg/h +/- 3.2 kg/h) with a 45% increase in specific electricity consumption. The experimental work also revealed limited operating flexibility in colder temperatures due to an exponentially increasing preheating time (up to 4.2 h at 0 degrees C). With techno-economic modelling, the simulations yielded a simple payback time of 7.5 years for a commercial-scale drying system without investment subsidies. Applying an optimized operating strategy, system operation shifted on average from hybrid mode (96-33%) to solar mode (3-36%) and inactive state (1-31%) with an increased electricity price level, highlighting the need for advanced operation planning. Commercial deployment should prioritize use cases with high value increase through drying or obtaining compensation from avoided biogenic CO2 emissions through drying, as these two parameters were shown to have the greatest impact on the investment payback time.