Optimization of Enzyme Hydrolysis of Seafood Waste for Microwave Hydrothermal Carbonization
Bibliographic record
Abstract
Hydrothermal carbonization (HTC) is a promising technique that converts wet biomass into a coal-like material and has a wide application to the fields of energy, material science, and nanotechnology. HTC has been primarily used to treat a limited number of feedstocks, mainly lignocellulosic biomass such as wood. Recently, the HTC process has been utilized to treat high-moisture-containing complex waste streams, a mixture of lignocellulosic and nonlignocellulosic biomass, such as sewage and municipal waste. However, there is limited knowledge on the effectiveness of HTC on purely nonlignocellulosic industrial waste like seafood waste. Processing of seafood generates enormous amounts of waste in the form of solid residues and liquid effluents. Currently there is a demand for attractive seafood waste utilization strategies that minimize environmental pollution while recovering products that are of commercial interest to the industry. In this study, we have devised one such strategy where seafood waste is pretreated by enzymatic hydrolysis for subsequent HTC to produce hydrochar and biocrude liquor. Enzyme hydrolysis conditions including enzyme concentration, incubation time, and enzyme ratios were carefully optimized for maximal hydrolysis of seafood waste. By using an enzyme cocktail of Viscozyme, Lipase, and Protease, it was found that an enzyme ratio of 1:1:1 (w/w/w), and an enzyme concentration of 10–20% with a treatment time of 16 h, resulted in maximal hydrolysis of fish and shrimp waste. Subsequently, hydrochar and biocrude liquor were generated from hydrolyzed fish and shrimp waste by microwave hydrothermal carbonization (MHTC) using a high-pressure Mini WAVE Digestion Module (SCP Science, Canada) with quartz vessels at conditions of 150 °C for a 1 h reaction time. The results of this study show for the first time that MHTC can be successfully employed to produce valuable products from pure nonlignocellulosic waste like seafood waste. This would pave the way for effective utilization of other moisture-rich nonlignocellulosic industrial wastes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".