Technical and economic assessment for the production of torrefied ligno-cellulosic biomass pellets in the US

Manufacturing and trade of wood pellets in the United States (US) has seen an exponential growth in the last few years, triggered by its potential utilization in applications typically dominated by fossil fuels, such as heat, power, and combined cycle generation. This combination holds the promise of delivering a high density, high heat value fuel, making it a better substitute for coal and other fossil fuels. This combined process exists only at pilot-plant levels. Scale-up of the technology and feasibility of such projects remain largely unexplored. This research developed a techno-economic model for the production of torrefied wood pellets, considering critical production parameters, and evaluating sensitivity to changes in CAPEX (Capital Expenditure), biomass delivered costs, labor, and energy consumption of a facility, evaluated through a case-study. Results indicated that biomass delivered costs and depreciation are the most significant factors influencing production with CAPEX being the most sensitive variable due to high investments in torrefaction reactors. The selection of different torrefaction technologies, and adequate binders, may represent a major improvement in the feasibility of a project by reducing capital costs drastically. Back-calculated price for torrefied wood pellets is $261/metric ton (100,000 metric tons/year facility), and delivered price may reach $282/metric ton, a similar cost compared to regular pellets. Preliminary analysis of carbon credits as additional income may considerably increase the likeability of the business, and further enhance profitability.

► We elaborated a techno-economic model for production of torrefied wood pellets in the US.
► Production rate, mass and energy balance, CAPEX, labor, etc. are studied in the model.
► Adjusted price reaches $261/ton, being competitive compared to regular wood pellets.
► CAPEX and biomass delivered costs are the most sensitive variables of the model.
► Selecting different technologies and binders may potentially increase profitability.