• Energy Research

AbstractThe decision‐making landscape to maximize the use of sustainable biomass resources, and achieve long‐term environmental and socioeconomic benefits, is complex with a high level of uncertainty in biomass supply and logistics, technical and economic performance of the biorefinery routes, lifecycle performance of the finished products, and other sustainability criteria. Numerous decision‐making support models have been developed but these models usually assess only a few specific aspects of technology, regulations, economic, environment, and society. Decision‐making support models with a limited capability to capture environmental and socioeconomic performance of the biorefining pathways are not able to identify the best available biorefining routes. This study reviews and discusses recent progress on the harmonization, standardization, and integration of the existing decision‐making support models that aim to improve the comparability of the results of these models when different pathways are being assessed and align the decisions made at the strategic, tactical and operational levels. With the growing number of climate‐change policies and greenhouse gas (GHG) emission reduction targets, national and international efforts to harmonize the input databases, the model assumptions and system boundaries, and the integration of the existing models have been increasing. However, the deployment of the integrated frameworks among the bioeconomy stakeholders that are capable of evaluating and identifying the promising biorefining routes with significant economic, social and environmental benefits is still not a common practice. This study proposes an integrated decision‐making support framework to identify cost‐competitive, low‐carbon fuel production pathways that are technically viable and can potentially provide maximum GHG emission reduction.

AbstractThe decision‐making landscape to maximize the use of sustainable biomass resources, and achieve long‐term environmental and socioeconomic benefits, is complex with a high level of uncertainty in biomass supply and logistics, technical and economic performance of the biorefinery routes, lifecycle performance of the finished products, and other sustainability criteria. Numerous decision‐making support models have been developed but these models usually assess only a few specific aspects of technology, regulations, economic, environment, and society. Decision‐making support models with a limited capability to capture environmental and socioeconomic performance of the biorefining pathways are not able to identify the best available biorefining routes. This study reviews and discusses recent progress on the harmonization, standardization, and integration of the existing decision‐making support models that aim to improve the comparability of the results of these models when different pathways are being assessed and align the decisions made at the strategic, tactical and operational levels. With the growing number of climate‐change policies and greenhouse gas (GHG) emission reduction targets, national and international efforts to harmonize the input databases, the model assumptions and system boundaries, and the integration of the existing models have been increasing. However, the deployment of the integrated frameworks among the bioeconomy stakeholders that are capable of evaluating and identifying the promising biorefining routes with significant economic, social and environmental benefits is still not a common practice. This study proposes an integrated decision‐making support framework to identify cost‐competitive, low‐carbon fuel production pathways that are technically viable and can potentially provide maximum GHG emission reduction.

Carbon capture, utilization, and storage (CCUS) is an attractive technology for the decarbonization of global energy systems. However, its early development stage makes impact assessment difficult. Moreover, rising popularity in carbon pricing necessitates the development of a methodology for deriving carbon abatement costs that are harmonized with the price of carbon. We develop, using a combined bottom-up analysis and top-down learning curve approach, a levelized cost of carbon abatement (LCCA) model for assessing the true cost of emissions mitigation in CCUS technology under carbon pricing mechanisms. We demonstrate our methodology by adapting three policy scenarios in Canada to explore how the implementation of CO2-to-diesel technologies could economically decarbonize Canada’s transportation sector. With continued policy development, Canada can avoid 932 MtCO2eq by 2075 at an LCCA of CA$209/tCO2eq. Technological learning, low emission hydroelectricity generation, and cost-effective electricity prices make Quebec and Manitoba uniquely positioned to support CO2-to-diesel technology. The additional policy supports beyond 2030, including an escalating carbon price, CO2-derived fuel blending requirements, or investment in low-cost renewable electricity, which can accelerate market diffusion of CO2-to-diesel technology in Canada. This methodology is applicable to different jurisdictions and disruptive technologies, providing ample foci for future work to leverage this combined technology learning + LCCA approach.

Carbon capture, utilization, and storage (CCUS) is an attractive technology for the decarbonization of global energy systems. However, its early development stage makes impact assessment difficult. Moreover, rising popularity in carbon pricing necessitates the development of a methodology for deriving carbon abatement costs that are harmonized with the price of carbon. We develop, using a combined bottom-up analysis and top-down learning curve approach, a levelized cost of carbon abatement (LCCA) model for assessing the true cost of emissions mitigation in CCUS technology under carbon pricing mechanisms. We demonstrate our methodology by adapting three policy scenarios in Canada to explore how the implementation of CO2-to-diesel technologies could economically decarbonize Canada’s transportation sector. With continued policy development, Canada can avoid 932 MtCO2eq by 2075 at an LCCA of CA$209/tCO2eq. Technological learning, low emission hydroelectricity generation, and cost-effective electricity prices make Quebec and Manitoba uniquely positioned to support CO2-to-diesel technology. The additional policy supports beyond 2030, including an escalating carbon price, CO2-derived fuel blending requirements, or investment in low-cost renewable electricity, which can accelerate market diffusion of CO2-to-diesel technology in Canada. This methodology is applicable to different jurisdictions and disruptive technologies, providing ample foci for future work to leverage this combined technology learning + LCCA approach.

Abstract A multi-objective optimization of simultaneous saccharification and fermentation process for cellulosic ethanol production was carried out to simultaneously maximize the ethanol yield/cellulose conversion and minimize the enzyme consumption by manipulating the initial sugar concentrations, and cellulose and enzyme loadings. The study was based on an experimentally verified kinetic model. Several bi-objective optimization problems with different combinations of objectives and constraints were solved by a controlled elitist genetic algorithm, a variant of the non-dominated sorting genetic algorithm II (NSGA-II). The optimum operating conditions were verified by experiments. There was significant performance improvement in terms of ethanol yield, cellulose conversion and enzyme loading. An overall 40% reduction of enzyme consumption per ethanol produced was attained at the same ethanol yield (32%) of a non-optimized process. However, the optimum conditions are highly sensitive to the selected kinetic model and associated kinetic parameters therefore, selection of the appropriate kinetic model is critical.

Abstract A multi-objective optimization of simultaneous saccharification and fermentation process for cellulosic ethanol production was carried out to simultaneously maximize the ethanol yield/cellulose conversion and minimize the enzyme consumption by manipulating the initial sugar concentrations, and cellulose and enzyme loadings. The study was based on an experimentally verified kinetic model. Several bi-objective optimization problems with different combinations of objectives and constraints were solved by a controlled elitist genetic algorithm, a variant of the non-dominated sorting genetic algorithm II (NSGA-II). The optimum operating conditions were verified by experiments. There was significant performance improvement in terms of ethanol yield, cellulose conversion and enzyme loading. An overall 40% reduction of enzyme consumption per ethanol produced was attained at the same ethanol yield (32%) of a non-optimized process. However, the optimum conditions are highly sensitive to the selected kinetic model and associated kinetic parameters therefore, selection of the appropriate kinetic model is critical.