Biowaste to Bio-products through Hybrid Thermo-chemical and Biochemical Conversion: A Circular Economy Concept
Bibliographic record
Abstract
The circular economy concept is a recent waste management strategy, which aims to eliminate waste and convert it as resources into various products for reduction of greenhouse gas (GHG) emission and release of contaminants into soil and water. Municipal food waste (MFW) and corn residue (CR) are two undervalued carbonaceous and nutrient rich biomass. High moistured MFW mixes with impurities during handling, which need immediate disposal and CR contains high moisture in the fall. Ontario farmers traditionally keep CR mostly unharvested in the fall but a small portion is harvested in the spring for livestock bedding and feed. On the other hand, demand for research on production of alternative energy is increasing for reduction of environmental pollution from traditional fossil fuel. This study reports the results of bioenergy and nutrient recovery from non-rooted standing plant fall harvested CR and MFW of a city. MFW was hydro-mechanically pretreated and CR was treated mechanically and hydrothermally for enhancement of biomethane production through mesophilic anaerobic digestion (AD). For excellent bioenergy recovery from CR, a recommended hydrothermal (HT) process condition was determined to maximise biocarbon recovery and HT process water (HTPW) suitable for biomethane production in AD process. For further enhancement of bioenergy production, the most favourable alkaline HT process was determined to maximize biocarbon production and its AD favorable alkaline HT process water (AHTPW) for excellent biomethane production. Finally the most favourable composition of “as received” MFW with i) mechanically treated CR, ii) HTPW and iii) AHTPW were separately determined for anaerobic co-digestion for the highest biomethane production. This study focuses on the most advantageous bioenergy and nutrient recovery in the circular economy concept from both “as received” MFW and CR by determining the most favourable HT and alkaline HT processes and their optimum AD co-digestion compositions, which has not been previously researched. The CR enhanced bioenergy production for mechanical, HT and alkaline HT treatment, which were further enhanced by 30%, 21% and 49%, respectively when their liquid parts were separately AD co-digested with MFW. The best composition of AHTPW and MFW in mesophilic AD co-digestion recovered the highest bioenergy and biofertilizer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".