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Wood and forestry residues are usually processed as wastes, but they can be recovered to produce electrical and thermal energy through processes of thermochemical conversion of gasification. This study proposes an equilibrium simulation model developed by ASPEN Plus to investigate the performance of 28 woody biomass and forestry residues’ (WB&FR) gasification in a downdraft gasifier linked with a power generation unit. The case study assesses power generation in Iceland from one ton of each feedstock. The results for the WB&FR alternatives show that the net power generated from one ton of input feedstock to the system is in intervals of 0 to 400 kW/ton, that more that 50% of the systems are located in the range of 100 to 200 kW/ton, and that, among them, the gasification system derived by tamarack bark significantly outranks all other systems by producing 363 kW/ton. Moreover, the environmental impact of these systems is assessed based on the impact categories of global warming (GWP), acidification (AP), and eutrophication (EP) potentials and normalizes the environmental impact. The results show that electricity generation from WB&FR gasification is environmentally friendly for 75% of the studied systems (confirmed by a normalized environmental impact [NEI] less than 10) and that the systems fed by tamarack bark and birch bark, with an NEI lower than 5, significantly outrank all other systems owing to the favorable results obtained in the environmental sector.

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In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.