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Record W3145590713 · doi:10.3390/suschem2020014

Food Waste Digestate-Based Biorefinery Approach for Rhamnolipids Production: A Techno-Economic Analysis

2021· article· en· W3145590713 on OpenAlex
Raffel Dharma Patria, Jonathan W.C. Wong, Davidraj Johnravindar, Kristiadi Uisan, Rajat Kumar, Guneet Kaur

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainable Chemistry · 2021
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsYork University
FundersInnovation and Technology Commission - Hong KongInnovation and Technology Commission
KeywordsDigestateBiorefineryFood wasteRaw materialProduction (economics)Profitability indexIndustrial fermentationAnaerobic digestionPayback periodDowntimeWaste managementEnvironmental scienceEngineeringBiofuelBusinessEconomicsFermentation

Abstract

fetched live from OpenAlex

The present work evaluates the techno-economic feasibility of a rhamnolipids production process that utilizes digestate from anaerobic digestion (AD) of food waste. Technical feasibility, profitability and extent of investment risks between fermenter scale and its operating strategy for rhamnolipids production was investigated in the present study. Three scenarios were generated and compared: production using a single large fermenter (Scenario I), using two small fermenters operated alternately (Scenario II) or simultaneously (Scenario III). It was found that all the scenarios were economically feasible, and Scenario III was the most profitable since it allowed the most optimum fermenter operation with utilization of multiple small-scale equipment to reduce the downtime of each equipment and increase the production capacity and overall productivity. It had the highest net present value, internal rate of return and shortest payback time at a discount rate of 7%. Finally, a sensitivity analysis was conducted to indicate how the variation in factors such as feedstock (digestate) cost, rhamnolipids selling price, extractant recyclability and process capacity influenced the process economics. The work provides important insights on techno-economic performance of a food waste digestate valorization process which would be useful to guide its sustainable scale-up.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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