SUMMARYThe Gothenburg Biomass Gasification plant (2015) is currently the largest plant in the world producing biomethane (20 MW biomethane ) from woody biomass. We present the experimental data from the first measurement campaign and evaluate the mass and energy balances of the gasification sections at the plant. Measures improving the efficiency including the use of additives (potassium and sulfur), high-temperature pre-heating of the inlet streams, improved insulation of the reactors, drying of the biomass and introduction of electricity as a heat source (power-to-gas) are investigated with simulations. The cold gas efficiency was calculated in 71.7%LHV daf using dried biomass (8% moist). The gasifier reaches high fuel conversion, with char gasification of 54%, and the fraction of the volatiles is converted to methane of 34% mass . Because of the design, the heat losses are significant (5.2%LHV daf ), which affect the efficiency. The combination of potential improvements can increase the cold gas efficiency to 83.5%LHV daf , which is technically feasible in a commercial plant. The experience gained from the Gothenburg Biomass Gasification plant reveals the strong potential biomass gasification at large scale.

Process simulation of a dual fluidized bed (DFB) gasifier is challenging, owing to the high degree of freedom inherent to the operation of the double-reactor system and the complexity of the reactions therein. We propose a method for simulation of the gasifier based on the analysis of experimental data and of the total uncertainty associated with them. The overall aim is to use data from the large amount of pilot and demonstration gasifiers in the analysis and optimization of gasification-based processes. In the method proposed a set of fuel conversion variables and their associated uncertainties are calculated using a stochastic approach that takes into account the effect of unclosed mass balance, incomplete characterization of the raw gas compounds and measurement errors. Subsequently, these fuel conversion variables are used to simulate the gasifier in a flowsheet model developed in Aspen Plus. The results include the evaluation of critical parameters, such as, gasifier efficiency, char gasification, and tar yield and their uncertainties, which depend highly on the measurement system. The method is applied to data sets derived from several measurement setups, and the results are validated with total carbon measurements. The results show that detection of ≥95% of the carbon in the raw gas is necessary to maintain the uncertainty level at <3% and to estimate the char conversion and oxygen transport. The flowsheet model of the gasifier is applied to a database of six operational points; the results show that interpolation and extrapolation of the fuel conversion variables are possible and the gasifier is evaluated in operational conditions different from the experiments. In summary, this method is flexible with respect to different measurement setups and represents a valuable tool for process simulation using flowsheet software.

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We present a comparison of three strategies for the introduction of new biorefineries: standalone and centralized drop‐in, which are placed within a cluster of chemical industries, and distributed drop‐in, which is connected to other plants by a pipeline. The aim was to quantify the efficiencies and the production ranges to support local transition to a circular economy based on biomass usage. The products considered are biomethane (standalone) and hydrogen/biomethane and sustainable town gas (centralized drop‐in and distributed drop‐in). The analysis is based on a flow‐sheet simulation of different process designs at the 100 MWbiomass scale and includes the following aspects: advanced drying systems, the coproduction of ethanol, and power‐to‐gas conversion by direct heating or water electrolysis. For the standalone plant, the chemical efficiency was in the range of 78–82.8 % LHVa.r.50 % (lower heating value of the as‐received biomass with 50 % wet basis moisture), with a maximum production of 72 MWCH4 , and for the centralized drop‐in and distributed drop‐in plants, the chemical efficiency was in the range of 82.8–98.5 % LHVa.r.50 % with maximum production levels of 85.6 MWSTG and 22.5 MWnormalH2 /51 MWCH4 , respectively. It is concluded that standalone plants offer no substantial advantages over distributed drop‐in or centralized drop‐in plants unless methane is the desired product.

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