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Record W3011211420 · doi:10.2172/1603765

Biomass Electrochemical Reactor for Upgrading Biorefinery Waste to Industrial Chemicals and Hydrogen

2019· report· en· W3011211420 on OpenAlexfundno aff
John A. Staser

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersOffice of Energy EfficiencyOffice of Energy Efficiency and Renewable EnergyLakehead UniversityU.S. Department of Energy
KeywordsBiorefineryLigninDepolymerizationBiomass (ecology)Environmental scienceProcess engineeringWaste managementChemistryBiofuelEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The goal of this project was to develop an electrochemical process for depolymerization of biorefinery lignin to industrial chemicals, most notably as replacements in resins. The project team consisted of Ohio University (lead organization), Hexion, Inc. and Lakehead University. There were six distinct project tasks, consisting of Initial Validation, Development of TiO2 Nanowire-supported Ni-Co Electrocatalysts, Electrochemical Characterization of TiO2 Nanowire-supported Ni-Co Electrocatalysts, Development of Electrochemical Flow Reactor for Production of Industrial Chemicals and Hydrogen, Product Formulations and Integrated Biorefinery Analysis. The project was divided into two budget periods. After intermediate validation near the end of Budget Period 1, the project was discontinued. This final report is intended to highlight major project objectives and results. Technical metrics were developed between the project team and the DOE validation team during the initial validation period. Critical technical metrics included conversion of lignin to products and product selectivity. Desired products were low molecular weight aromatic compounds in the 150-300 MW range. We targeted this class of compounds because of their potential to replace petroleum derivatives in resin formulations. Electrochemical oxidation of biorefinery lignin can lead to significant lignin depolymerization and conversion, with >40% of the lignin reacted. However, product stream analysis is extremely difficult. We were not able to adequately quantify the concentration of the primary oxidation products, or to determine with any degree of certainty what the product selectivity was. The project team initially proposed analytical techniques such as GC-MS to identify product distributions. However, due to 1) difficulty in extracting products into an organic phase suitable for GC analysis and 2) the wide range of product compounds with similar structures, GC-MS analysis was difficult to perform. The project team did have some success with NMR spectroscopic analysis, but not enough so to adequately identify product selectivity. By UV-vis spectroscopy and statistical analysis, we were able to confirm achieving the technical milestone for lignin conversion, and achieved at least 40% conversion by the electrochemical oxidation technique. However, we were not able to confirm product selectivity using GC-MS analysis, as originally postulated. Despite these difficulties, we were able to incorporate the product stream into a resin, and observed better resin synthesis behavior using the oxidized lignin versus the unoxidized lignin. However, no resin synthesized with oxidized biorefinery lignin met the commercial standards for resin quality. One potential problem with electrochemical oxidation of biomass is the competing oxygen evolution reaction (OER) at the reactor anode. We observed that, especially at higher anode potentials, the OER can consume up to 50% of the energy input to the system, meaning that much of the energy goes toward generation of an essentially worthless product (O2). However, we also observed that the OER can be avoided almost entirely at lower potentials. These uncertainties made assigning a value to the electrochemical reactor product stream difficult. Any product generated electrochemically would have to be valuable enough to offset the additional cost of electricity incurred, including electricity to run the electrochemical reactor and the additional electricity that must be purchased because some of the lignin (which is currently burned in biorefineries to recover energy) is diverted to the electrochemical reactor. It is not clear at this time whether the product stream has sufficient economic value to offset these additional electricity costs. The two most promising areas for this project in terms of demonstrating economic feasibility are product stream analysis and resin synthesis. In particular, product stream analysis has proven to be difficult. With additional time, we would refine analytical techniques to better understand key product stream characteristics, such as aromatic compound content. Evaluating the product streams in resin synthesis would likely provide more information on the economic value of the product stream. If we could determine that value, then we would be able to better evaluate whether the electrochemical conversion approach is feasible. The details of this work are presented in the following final report. The report is organized into individual tasks for clarity, although all tasks were integrated and many occurred simultaneously.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.252
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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