Energy Efficiency Calculations for a Hybrid Electrochemical Reduction (ECR) + Bioproduction CO2 Sequestration System
The electrochemical reduction of CO2 (eCO2RR) using renewable energy and either air-captured or avoided carbon can produce one and two carbon (C1/C2) intermediates that can potentially serve as lower-carbon-intensity feedstocks for the bio-based synthesis of C3+ products. We evaluate the carbon efficiency and the minimum energy inputs for ~1000 cases of the integrated system, considering 9 candidate intermediates and 50+ bioproducts. The most carbon-efficient and energetically-compelling options include aerobic bioconversion of C2 intermediates or methanol to highly oxidized bioproducts, an intriguing subset of which (e.g. Malonate) require less than 35 GJ of energy input per ton of product. Ethanol provides the highest overall bioproduction mass yields out of all the intermediates, with over 25% of the products having a mass yield greater than one . For fuels, the anaerobic bioconversion of methanol and carbon monoxide each appear promising, with energy inputs in the range of 40-60 GJ/ton. We find that the recycling of CO2 generated during bioproduction is the optimal choice when CO2 capture costs for eCO2R exceeds the separation costs of bioreactor gas stream. Overall, this analysis points to privileged {intermediate-product} pairings in such integrated systems. This repository contains the code used for this hybrid system analysis
The project folder includes two ipynb notebooks. The SPI_ECR-Bio_Analysis is the main notebook responsible for performing the analysis, and is linked with the higher level Input/Output folders SPI_Visualization is a notebook dedicated to generating figures out of the analysis data, and is linked with the Visualization Docs folder, which contains its own set of Input/Output folders
Within the higher level Input folder, exists 4 Excel files. AssimilationPathways is a preset file responsible for linking investigated bioassimilation pathways with the full name of their associated base substrate (which are linked to SubstratePathway and Substrate variables kept consistent in their naming throughout). ECR_Parameters contains ECR data for various substrates, at their base case (CATEGORY 1) and their best case (CATEGORY 2) values.
Of note, these files are not generated by any code, and for the case of propanol, I've assumed ECR parameters similar to ethanol (as they have similarly middling faradaic efficiency and conversion values with propanol only having a slightly lowerthermodynamic standard potential, thus should share similar ideal ECR values). This then requires some minor adjustments in final carbon efficiency values (as the propanol which is labelled as ethanol would seemingly only have 2 carbons), but no other changes are required for energetic effiency and mass yield which are handled separately. If you want to add additional pathways and substrates, you would need to append them to the AssimilationPathways file. If the corresponding substrate is already included in the ECR_Parameters (such as if you wished to investigate an alternative ethanol utilization pathway), this step is complete: simply link it to "Ethanol", otherwise you would have to link it to a similarly performing substrate in the ECR system, like what's been done with Propanol or Ethanol, or append additional lines into the ECR_Parameters file to include the new substrate.
The other two files, both marked FBA_OverallSummary, are partially generated from the FBA (Flux Balance Analysis) Model Building folder (which is further explained in a README in that folder). The FBA_OverallSummary file contains important information such as bioproduction mass yields (the second half of the hybrid system) generated from the FBA, as well as parameters such as product charge, molar mass, product chemical formula, and degree of reduction, which were manually input into the file. The current input Excel files consist of the latest set of generated data. These FBA files are used by the ECR-Bio_Analysis notebook to perform the final analysis, with the results being output as Excel files into the Output folder, which currently has a sample output from an earlier data to serve as a reference.
The Visualization Doc folder consists of an Input folder of Excel files which the Visualization notebook reads from to plot the data. The top 4 files (All labelled from 2021), are a fixed set of data to compare against and are unused in the actual notebook (aside from the HybridCosts-GlucoseANDConventionalCosts Excel which consist of values calculated externally). 2024-05-08_Type-Bio_Case-Category_2_Analysis-Categories_FULL is the Excel file which could be replaced by new Output data generated from the ECR-Bio_Analysis notebook, whereas the FBA_OverallSummary Excels are slightly modified versions of the files from the high level Input folder (which are partially generated from the FBA Model Building folder, and should be updated if additional assimilation pathways or intermediates are added). The final two SIYield files are curated versions of the 2024-05-08_Type-Bio_Case-Category_2_Analysis-Categories_FULL file solely used to generate best performing chemicals/intermediate graphs
The current version of the code is fully functional and can be directly run (with some minor file path changes within the notebooks) to get an idea of what each file/notebook is meant to do. However, if you wish to utilize this to check for the efficiency for an alternative biopathway, or try a different biofeedstock, you would generally need to take the following steps:
- Get new mass yield values from the FBA Model Building (see that folder for further details)
- Add the new pathway/substrate into the AssimilationPathway Excel File, and change ECR_Parameters Excel file accordingly (if necessary)
- Append the FBA Model Building results into the FBA_OverallSummary Excel File
- Run the ECR-Bio_Analysis notebook to get the final desired data in the Output folder
- (Optional) Modify and move that final generated Excel file into the Visualization Docs Input folder, then change the file names within the Visuailization notebook to graph your new pathway's data accordingly
The current iteration of the code is run on Python 3.10.13. For any questions, please email au.zheng@mail.utoronto.ca