The success of long-term sustainable biofuel production on agricultural lands is still questionable. To this end, we investigated the effects of crop prices on the changes of agricultural land use for biofuel canola production in three wheat crop management zones in North Dakota. The effects of canola hydroprocessed esters and fatty acids (HEFA) production on greenhouse gas (GHG) emissions and energy demand were investigated along with different allocation methods. The Environmental Policy Integrated Climate (EPIC) and Alternative Fuel Transportation Optimization Tool (AFTOT) models were used to simulate the life cycle assessment (LCA) inputs for two key stages of the HEFA pathway: cultivation and transportation. From the EPIC model results, the increase in canola price had a significant impact on predicted farmer decisions to displace food crops with energy crops and particularly on resulting changes in soil carbon (C). The LCA results suggested that to increase soil C sequestration, energy canola should be grown in the place of the fallow whenever possible to guarantee the long-term soil C sustainability of canola HEFA. Other possible ways to mitigate the GHG emissions included using anhydrous ammonia as the nitrogen fertilizer for cultivation and H2 integration (use of HEFA coproducts in H2 production) for HEFA conversion.

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAM-LCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAM-LCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

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This tutorial review synthesizes literature on sustainability analyses to introduce quantitative sustainable design (QSD) for technology research, development, and deployment.

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Granulation is necessary before iron ore sintering. The optimum granulation moisture added to the mixture of iron ores, fuel and coke for obtaining suitable size distribution of the granules after tumbled in a drum is of vital importance for the sintering. In order to investigate the optimum moisture content, the moisture capacity of the iron ore, which means the maximum water content held in iron ores of unit mass, is defined and measured in a self-designed apparatus. The relationship between the moisture capacity and some other characteristics of iron ores, which include mineral and chemical composition, size distribution and mineralogy, is discussed. It was found that the mineral composition and the size distribution of the iron ore particles greatly influence the moisture capacity of the iron ores. The moisture capacity of the iron ore was also applied to optimise the granulation by finding the relationship between the moisture capacity of the mixture and the optimum water content. The moisture capacity has positive correlation with the optimum water content. The iron ore, which has high moisture capacity, needs more water added in the granulation process in order to get high permeability. The correlation was given to predict the optimum water content in the granulation based on the moisture capacity measurement.