How does technology pathway choice influence economic viability and environmental impacts of lignocellulosic biorefineries?

Abstract

Background: The need for liquid fuels in the transportation sector is increasing, and it is essential to develop industrially sustainable processes that simultaneously address the tri-fold sustainability metrics of technological feasibility, economic viability, and environmental impacts. Biorefineries based on lignocellulosic feedstocks could yield high-value products such as ethyl acetate, dodecane, ethylene, and hexane. This work focuses on assessing biochemical and biomass to electricity platforms for conversion of Banagrass and Energycane into valuable fuels and chemicals using the tri-fold sustainability metrics.

Results: The production cost of various products produced from Banagrass was $1.19/kg ethanol, $1.00/kg ethyl acetate, $3.01/kg dodecane (jet fuel equivalent), $2.34/kg ethylene and $0.32/kW-h electricity. The production cost of different products using Energycane as a feedstock was $1.31/kg ethanol, $1.11/kg ethyl acetate, $3.35/kg dodecane, and $2.62/kg ethylene. The sensitivity analysis revealed that the price of the main product, feedstock cost and cost of ethanol affected the profitability the overall process. Banagrass yielded 11% higher ethanol compared to Energycane, which could be attributed to the differences in the composition of these lignocellulosic biomass sources. Acidification potential was highest when ethylene was produced at the rate of 2.56 × 10^-2 and 1.71 × 10^-2 kg SO₂ eq. for Banagrass and Energycane, respectively. Ethanol production from Banagrass and Energycane resulted in a global warming potential of - 12.3 and - 40.0 g CO₂ eq./kg ethanol.

Conclusions: Utilizing hexoses and pentoses from Banagrass to produce ethyl acetate was the most economical scenario with a payback period of 11.2 years and an ROI of 8.93%, respectively. Electricity production was the most unprofitable scenario with an ROI of - 29.6% using Banagrass/Energycane as a feedstock that could be attributed to high feedstock moisture content. Producing ethylene or dodecane from either of the feedstocks was not economical. The moisture content and composition of biomasses affected overall economics of the various pathways studied. Producing ethanol and ethyl acetate from Energycane had a global warming potential of - 3.01 kg CO₂ eq./kg ethyl acetate.

Keywords: Advanced biofuels; Biomass pretreatment; Biorefinery; Life cycle assessments; Lignocelluloses; Process simulation; Systems analysis; Techno-economic analysis.

Fig. 1

Factors in sustainability affecting an industrial process

Fig. 2

Schematics of different processes using Banagrass as a feedstock

Fig. 3

Schematics of different processes using Energycane as a feedstock

Fig. 4

Economic indexes for different scenarios using Banagrass as a feedstock. Capital cost, operational cost and revenues (a). Return on investment and production cost (b). Production cost, production revenue and the error bar indicates the historical prices in the last decade (c)

Fig. 5

Different economic indexes for different scenarios using Energycane as a feedstock. Capital cost, operational cost and revenues (a). Return on investment and production cost (b). Production cost, production revenue and the error bar indicates the historical prices in the last decade (c)

Fig. 6

Sensitivity analysis of important parameters for the different scenarios considered in this study

Fig. 7

Elemental carbon balance in kilogram for every ton of biomass (wet basis) produced in BE and EE scenario

Fig. 8

Environmental impacts for different scenarios using TRACI 2.1 as impact assessment method. The functional unit for different scenarios was mentioned based on their mass or energy. Ethanol, dodecane—MJ, ethyl acetate, ethylene—kg, electricity—kWh

Fig. 9

Comparison of cost estimates for ethanol production with literature reported values (a) and GHG emission estimates for different fuels and chemicals with respect to values reported in GREET (b). For GHG emissions, a respective functional unit for each category is mentioned

Fig. 10

Snapshot of the process flowsheet developed in Superpro designer for the case BEEA

Fig. 11

System boundary representation for the LCA study