Analysis Of Economic, Environmental And Societal Benefits By Using Biomass As Manufacturing Feedstocks
Bibliographic record
Abstract
Abstract One of the key importance in utilizing biomass as manufacturing feedstocks is their feasibility to produce products that include energy, chemicals, materials, foods, and feeds from inexpensive and easily available resources. This fact has distinguished biomass’s advantages as compared to other renewable resources such as wind, solar, geothermal, tidal, and so on, in which their main uses are to produce energy. With all of the potentials and possibilities to diverse product generations, concerns are directed to select the optimal one out of all the feasible generating options because it will guide a better decision making within the boundary of the problem. In this study, the objective was to develop an optimization model in order to quantify economic, societal, and environmental benefits of using biomass feedstocks to produce range of products such as torrefied pellet, steam, power, syngas, formaldehyde, methanol, ammonia, hydrogen, bio-char, bio-diesel and bio-oil. The optimization model, classified as linear programming, has been formulated by taking miscanthus, switchgrass and forage sorghum as biomass feedstocks. A superstructure of alternatives was first constructed to show possible product generations in a series of biomass processing stages that included pre-processing, main processing, further processing 1, and further processing 2. In each of the processing stage, flow of materials were shown by arrows that have interconnected between feedstocks, technologies and produced products. By maximizing the total expected benefits in $ per year and writing the product’s yield as one of the decision variables, multiplications of the variable with product’s selling price, employee’s salary, and emission level would have determined values for economic, societal and environmental benefits, respectively. These values were subjected to the given parameters and typically require a perturbation or sensitivity analysis. It is expected that the optimal biomass utilization and product generation option will give the maximized potential benefits to the economic, society and the environment in the current study. A more thorough conclusion will be drawn once the optimal results are obtained by using the General Algebraic Modeling System. Key Words: Biomass Utilizations and Benefits, Superstructure Optimization, Miscanthus, Switchgrass, Forage Sorghum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".