Hybrid Thermochemical and Biochemical Conversion of Biomass for Value Added Products
Bibliographic record
Abstract
Greenhouse gas emissions can be minimized by reducing the use of fossil fuels and increasing the use of renewable fuel including biofuel. Biofuel feedstock should be economically feasible and consists of a variety of sources, different from the human food chain. Biofuel from lignocellulosic biomass has been recognized as an appropriate substitute to fossil fuels and first generation biofuel such as hydrolysis fermentation. There are a number of studies in progress worldwide to determine a method for the successfully and economically viable production of solid, liquid and gaseous biofuel from lignocellulosic biomass and municipal waste. Gasification-fermentation is one innovative method in the conversion of lignocellulosic biomass to biofuels. Synthesis gas components (CO, H2, and CO2) are easily converted to bioethanol by microorganisms such as Clostridium ljungdahlii. Bioethanol production from lignocellulosic biomass through conventional fermentation process does not utilize the lignin component, resulting in inefficient utilization of raw biomass. In comparison, hybrid thermochemical (gasification) and biochemical (syngas-fermentation) processes utilizes all components of lignocellulosic biomass, including lignin. Major challenges for the commercialization of biofuel include: low gas-liquid mass transfer, low bioethanol yield, impurities, high cost, and limited studies. Presently, most of the work has focused on fermentation; developing microbes for higher gas to energy conversion when the gas supply (mixture of H2 and CO) is controlled. Additional research is necessary to improve the reactor design such that higher gas-liquid mass transfer can be achieved. Similarly, the kinetic study of treated biomass-coal is an important factor for the future cascade operation of biomass-coal gasifier and syngas fermentation in the production of bioethanol. The blends of torrefied biomass and coal has potential to lower the net greenhouse gas emission by reducing carbon dioxide and NOx/SOx from the gasifier including coal fired electrical or thermal power plants by sacrificing heating value and ash content. This study focuses on the characterization and kinetic analysis of CO2 cogasification of dry torrefied biomass, hydrothermally treated biomass and coal to determine the optimum blend ratio, which has not been previously researched. Bioethanol has then been produced from the simulated syngas through the biochemical (biosynthesis) process in a bioreactor using novel gas-loop-back system. Syngas has been fermented with the microorganism Clostridium ljungdahlii. Different syngas composition and flow rate, media flow, stirrer speed, and reactor design tested to identify the most efficient pathway. This study helps to develop a better understanding of the process, thereby leading to the optimization of the overall system to produce sustainable bioethanol in Ontario. Similarly, mathematical models for thermochemical process (kinetic characterization for the blends) and biochemical process (ethanol extraction models) have been proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".