Advances on waste valorization: new horizons for a more sustainable society
Bibliographic record
Abstract
Abstract Increasingly tighter regulations regarding organic waste, and the demand for renewable chemicals and fuels, are pushing the manufacturing industry toward higher sustainability to improve cost‐effectiveness and meet customers’ demand. Food waste valorization is one of the current research areas that has attracted a great deal of attention over the past few years as a potential alternative to the disposal of a wide range of residues in landfill sites. In particular, the development of environmentally sound and innovative strategies to process such waste is an area of increasing importance in our current society. Landfill, incineration and composting are common, mature technologies for waste disposal. However, they are not satisfactory to treating organic waste due to the generation of toxic methane gas and bad odor, high energy consumption and slow reaction kinetics. In fact, research efforts have also been oriented on novel technologies to decompose organic waste. However, no valuable product is generated from the decomposition process. Instead of disposing and decomposing food waste, recent research has focused on its utilization as energy source (e.g., for bioethanol and biodiesel production). Organic waste is also useful to generate useful organic chemicals via biorefinery or white biotechnology (e.g., succinic acid and/or bio‐plastics). This article is aimed to summarize recent development of waste valorization strategies for the sustainable production of chemicals, materials, and fuels through the development of green production strategies. It will also provide key insights into recent legislation on management of waste worldwide as well as two relevant case studies (the transformation of corncob residues into functionalized biomass‐derived carbonaceous solid acids and their utilization in the production of biodiesel‐like biofuels from waste oils in P hilippines, as well as the development of a bakery waste based biorefinery for succinic acid and bioplastic production in H ong K ong) to illustrate the enormous potential of biowaste valorization for a more sustainable society. Future research directions and possible sustainable approaches will also be discussed with their respective proofs of concept.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".