Biomass Gas-Thermal Carbonization Process for Producing Blast Furnace Injection Fuel: Energy Consumption–Economy–Carbon Emission Model and Parameter Influence Research

Abstract

Abstract To quantify the production burden of biochar intended for blast furnace injection, an energy–economy–carbon emission model was developed for biomass gas-thermal carbonization (GTC), coupling steam generation, gas-thermal carbonization, waste-heat predrying, and tail-gas condensation/separation. The model uses 1 t of as-received reed as the reporting basis, explicitly accounts for moisture-sensible heat, vaporization latent heat, and water/steam enthalpy and solves the predehydration amount by simultaneously imposing a dried feed outlet temperature of 120 °C and an upper limit of 175 °C for the tail gas after heat recovery. Under the baseline conditions of 15 wt % feed moisture, 600 °C superheater outlet temperature, approximately 350 °C reactor center temperature, and 3 MPa pressure, the predehydration ratio is 9.33 wt % and steam demand is 1013.84 kg. Electricity consumption, process energy consumption, operating cost, and GTC production-stage carbon emissions are 1185.43 kW·h, 123.77 kgce, 879.75 CNY, and 980.71 kg CO2, respectively. Within 15–25 wt % moisture, each 2 percentage-point increase in feed moisture raises electricity consumption and carbon emissions by an average of 80.47 kW·h and 66.57 kg CO2. At carbonization temperatures of 350–380 °C, the predehydration amount can be adjusted to keep the tail-gas temperature near the 175 °C limit; at 390 °C, the removable external moisture has been fully removed, and further temperature increases cause a marked rise in tail-gas temperature and system burden. Sensitivity analysis identifies dry biomass heat capacity, heat loss fraction, energy prices, and the electricity emission factor as key parameters. The model is intended for operating condition screening within the pilot-calibrated window and provides a basis for subsequent blast furnace application and low-carbon engineering optimization.