Code and data in support of: Uncertainty in determining carbon dioxide removal potential of biochar
Data files
Jan 09, 2025 version files 119.27 MB
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Biochar_uncert_code_share_4.zip
59.63 MB
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Biochar_uncert_code_share_5.zip
59.63 MB
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README.md
7.02 KB
Abstract
A quantitative and systematic assessment of uncertainty in life-cycle assessment is critical to informing sustainable development of carbon dioxide removal (CDR) technologies. Biochar is the most commonly sold form of CDR to date, and it can be used in applications ranging from concrete to agricultural soil amendments. Previous analyses of biochar rely on modeled or estimated life-cycle data and suggest a cradle-to-gate range of 0.20–1.3 kg CO2 net removal per kg of biomass feedstock, driven by differences in energy consumption, pyrolysis temperature, and feedstock sourcing. Herein, we quantify the distribution of CDR possible for biochar production with a compositional life-cycle inventory model paired with scenario-aware Monte Carlo simulation in a “best practice” (incorporating lower transportation distances, high pyrolysis temperatures, high energy efficiency, recapture of energy for drying and pyrolysis energy requirements, and co-generation of heat and electricity) and “poor practice” (higher transportation distances, lower pyrolysis temperatures, low energy efficiency, natural gas for energy requirements, and no energy recovery) scenarios. In the best-practice scenario, cradle-to-gate CDR (which is representative of the upper limit of removal across the entire life cycle) is highly certain, with a median removal of 1.4 kg of CO2e / kg biomass and results in net removal across the entire distribution. In contrast, the poor-practice scenario results in median net emissions of 0.090 kg CO2e / kg biomass. Whether this scenario emits (66% likelihood) or removes (34% likelihood) carbon dioxide is highly uncertain. The emission intensity of energy inputs to the pyrolysis process and whether the bio-oil co-product is used as a chemical feedstock or combusted are critical factors impacting the net carbon dioxide emissions of biochar production, together responsible for 98% of the difference between the best- and poor-practice scenarios.
README: Uncertainty in determining carbon dioxide removal potential of biochar
README for Biochar Uncertainty Sceniaro Aware Monte Carlo Simulation code
Accompanies publication: Kane, Seth, et al. "Uncertainty in determining carbon dioxide removal potential of biochar." Environmental Research Letters (2024). doi: 10.1088/1748-9326/ad99e9
Prepared by: Seth Kane and Ahmad Bin Thaneya
Prepared on: 10.4.2024
Last edited on: 08.01.2025
Last edited by: Seth Kane
Last ran in: MATLAB R 2024a (Vers. 24.1.0.2628055), Python Vers. 3.12.8, and Microsoft Excel Vers. 16.91
Contents:
Visual_framework.pdf:
Flow diagram describing the location of input data to each model, interconnection of models contained herein, and description and location of results in the below-described file structure.
Folder: Predictive pyrolysis model uncertainty code
Contains the MATLAB code that determines model error distributions from the residuals of the validation dataset. See README contained in this folder for additional details.
Folder: Scenario-Aware Monte Carlo Simulation code
Contains the Python code that performs Scenario-Aware Monte Carlo simulation. See README contained in this folder for additonal details.
Folder: Scen. 1 Inputs and results
Contains:
Model run results (‘all_stats_Scen1.csv’ for complete results, presented as individual values of the variables listed in the first row that make up the distribution of results and ‘summary_stats_Scen1.csv’ for only final greenhouse gas emission values presented as individual model run results that combined make up the results distributions) including variables in 'all_stats_Scen1.csv', in order of columns:
Col. B: Biochar yield (kg biochar / kg biomass)
Col. C: Biochar ash content (kg ash / kg biochar)
Col. D: Biochar carbon content (kg C / kg biochar)
Col. E: Biochar hydrogen content (kg H / kg biochar)
Col. F: Biochar oxygen content (kg O / kg biochar)
Col. G: Biochar bound carbon (kg CO2e / kg biomass)
Col. H: Syngas final yield (kg syngas / kg biomass)
Col. I: Syngas CO2 content (kg CO2 / kg syngas)
Col. J: Syngas CO content (kg CO / kg syngas)
Col. K: Syngas CH4 content (kg CH4 / kg syngas)
Col. L: Syngas H2 content (kg H2 / kg syngas)
Col. M: Syngas higher molecular weight product (C2p) content (kg C2p / kg syngas)
Col. N: CO2 emissions from combustion of syngas (kg CO2 / kg biomass)
Col. O: Bio-oil yield (kg bio-oil / kg biomass)
Col. P: Bio-oil carbon content (kg C / kg bio-oil)
Col. Q: Bio-oil hydrogen content (kg C / kg bio-oil)
Col. R: Bio-oil oxygen content (kg C / kg bio-oil)
Col. S: Bio-oil bound carbon (kg CO2e / kg biomass)
Col. T: CO2 emissions from combustion of bio-oil (kg CO2 / kg biomass)
Col. U: External (non-waste heat) energy used for biomass drying (MJ / kg biomass)
Col. V: Higher heating value of bio-oil, if combusted (MJ / kg biomass)
Col. W: Lower heating value of bio-oil, if combusted (MJ / kg biomass)
Col. X: Pyrolysis energy requirement (negative is energy input) (MJ / kg biomass)
Col. Y: Syngas higher heating value (MJ / kg biomass)
Col. Z: Syngas lower heating value (MJ / kg biomass)
Col. AA: Total process energy balance (MJ / kg biomass)
Col. AB: CO2 emissions from energy inputs to system (kg CO2e / kg biomass)
Col. AC: Total fossil CO2 emissions of process (kg CO2e / kg biomass)
Col. AD: Total biogenic CO2 emissions of process (kg CO2e / kg biomass)
Col. AE: Total bound carbon of process (kg CO2e / kg biomass)
and of variables in ‘summary_stats_Scen1.csv’:
Col. B: Upstream harvesting emissions allocated to waste biomass (kg CO2e/kg biomass)
Col. C: Upstream transportation emissions (kg CO2e/kg biomass)
Col. D: total fossil CO2 emissions (kg CO2e/kg biomass)
Col. E: total biogenic CO2 emissions (kg CO2e/kg biomass)
Col. F: Total bound carbon of process (kg CO2e / kg biomass)
Result distributions (‘Scen1_dist_stats.csv’) contains descriptive statistics (Minimum of distribution, 10th, 25th, 50th, 75th, and 90th percentile of distribution and maximum value) of the distribution results presented in 'summary_stats_Scen1.csv'. All units are kg CO2e/kg biomass.
Model inputs (‘inputs_Scen1.csv’) for Scenario 1, containing distributions of inputs used to determine results, including:
Col. C-F: Distributions of cellulose, hemicellulose, lignin, and ash in the original biomass in wt. component/dry wt. biomass
Col. G: Moisture content of the biomass feedstock in wt. water/whole wt. biomass
Col. H: Pyrolysis temperature modeled in degree C.
Col. I-L: Pyrolysis thermal efficiency, bio-oil combustion thermal efficiency, and syngas combustion thermal efficiency in MJ/MJ.
Col. M-N: Wood loss efficiency (wt./wt.) and green wood mass requirement (kg) for sawmilling to assign upstream allocation of growth, harvesting, and transportation emissions to biochar product.
See Bose, et al. Life-cycle greenhouse gas footprint of cross-laminated timber from mixed hardwood and softwood from fire-prone California forests, Under review at Environmental Science & Technology for details on determining wood loss efficiency and green wood mass requirement and the main text and Supplemental information of Kane, et al. "Uncertainty in determining carbon dioxide removal potential of biochar." Environmental Research Letters (2024). doi: 10.1088/1748-9326/ad99e9 for details on the determination of all other input parameters.
Folder: Scen. 2 Inputs and results
Contains model run results distributions and model inputs for Scenario 2. See the above description for Scenario 1 inputs for a complete list of variables and files present in this folder.
Folder: Alt. Scen. 1 bio-oil combustion results
Contains model run results distributions for the alternative Scenario 1 with bio-oil combustion, named with the same file convention as folder ‘Sceni. 1 Inputs and results’. Inputs for this model run are the same as contained in “Scen. 1 Inputs and results”
Folder: Deterministic model runs
Contains model input parameters and results for deterministic runs of Scenario 1 (‘Model_Scen1.xlsx’) and Scenario 2 (‘Model_Scen2.xlsx’) of the predictive pyrolysis model. Full description of the model, as well as the original model spreadsheet used to perform the calculations, are reported in:
Seth Kane and Sabbie A. Miller. 2024. “Predicting Biochar Properties and Pyrolysis Life-Cycle Inventories with Compositional Modeling.” Bioresource Technology 399 (May): 130551. https://doi.org/10.1016/j.biortech.2024.130551.