Technoeconomic analysis (TEA) is an approach for conducting process design and simulation, informed by empirical data, to estimate capital costs, operating costs, mass balances, and energy balances for a commercial scale biorefinery. TEA serves as a useful method to screen potential research priorities, identify cost bottlenecks at the earliest stages of research, and provide the mass and energy data needed to conduct life-cycle environmental assessments. Recent studies have produced new tools and methods to enable faster iteration on potential designs, more robust uncertainty analysis, and greater accessibility through the use of open-source platforms. There is also a trend toward more expansive system boundaries to incorporate the impact of policy incentives, use-phase performance differences, and potential impacts on global market supply.

Significance: Cellulosic biofuels have not yet reached cost parity with conventional petroleum fuels. One strategy to address this challenge is to generate valuable coproducts alongside biofuels. Engineering bioenergy crops to generate value-added bioproducts in planta can reduce input requirements relative to microbial chassis and skip costly deconstruction and conversion steps. Although rapid progress has been made in plant metabolic engineering, there has been no systematic analysis devoted to quantifying the impact of such engineered bioenergy crops on biorefinery economics. Here, we provide new insights into how bioproduct accumulation in planta affects biofuel selling prices. We present the range of bioproduct selling prices and accumulation rates needed to compensate for additional extraction steps and reach a target $2.50/gal minimum biofuel selling price.

Near-term decarbonization of aviation requires energy-dense, renewable liquid fuels. Biomass-derived 1,4-dimethylcyclooctane (DMCO), a cyclic alkane with a volumetric net heat of combustion up to 9.2% higher than Jet A, has the potential to serve as a low-carbon, high-performance jet fuel blendstock that may enable paraffinic bio-jet fuels to operate without aromatic compounds. DMCO can be produced from bio-derived isoprenol (3-methyl-3-buten-1-ol) through a multistep upgrading process. This study presents detailed process configurations for DMCO production to estimate the minimum selling price and life-cycle greenhouse gas (GHG) footprint considering three different hydrogenation catalysts and two bioconversion pathways. The platinum-based catalyst offers the lowest production cost and GHG footprint of $9.0/L-Jet-A_eq and 61.4 gCO_2e/MJ, given the current state of technology. However, when the supply chain and process are optimized, hydrogenation with a Raney nickel catalyst is preferable, resulting in a $1.5/L-Jet-A_eq cost and 18.3 gCO_2e/MJ GHG footprint if biomass sorghum is the feedstock. This price point requires dramatic improvements, including 28 metric-ton/ha sorghum yield and 95–98% of the theoretical maximum conversion of biomass-to-sugars, sugars-to-isoprenol, isoprenol-to-isoprene, and isoprene-to-DMCO. Because increased gravimetric energy density of jet fuels translates to reduced aircraft weight, DMCO also has the potential to improve aircraft efficiency, particularly on long-haul flights.

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

Human milk oligosaccharides (HMOs) are a diverse class of carbohydrates which support the health and development of infants. The vast health benefits of HMOs have made them a commercial target for microbial production; however, producing the approximately 200 structurally diverse HMOs at scale has proved difficult. Here we produce a diversity of HMOs by leveraging the robust carbohydrate anabolism of plants. This diversity includes high-value and complex HMOs, such as lacto-N-fucopentaose I. HMOs produced in transgenic plants provided strong bifidogenic properties, indicating their ability to serve as a prebiotic supplement with potential applications in adult and infant health. Technoeconomic analyses demonstrate that producing HMOs in plants provides a path to the large-scale production of specific HMOs at lower prices than microbial production platforms. Our work demonstrates the promise in leveraging plants for the low-cost and sustainable production of HMOs.