Optimization and Characterization of Hydrochar Derived from Shrimp Waste
Bibliographic record
Abstract
Shrimp, a most consumed seafood, when processed, results in an enormous generation of wastes. Current ways of shrimp waste utilization are uneconomical and far from being environmentally friendly. Alternative sustainable technologies to utilize shrimp wastes completely are essential. Hydrothermal carbonization (HTC) that converts moisture-rich biomass into hydrochar is mostly employed for pure lignocellulosic biowaste. However, the suitability of HTC to produce good quality hydrochar from pure non-lignocellulosic wastes such as shrimp waste is unknown. Here, for the first time, a response surface design guided optimization of microwave hydrothermal carbonization (MHTC) process parameters, holding temperature (150–210 °C) and time (60–120 min), showed that a temperature of ∼184 °C and a time of ∼112 min yielded maximal hydrochar (∼42%). The atomic carbon and ash content, and calorific value of hydrochar were ∼39–49%, ∼21–25%, and 18.26–23.22 MJ/kg, respectively, depending on the MHTC operating conditions. Taken together, these results confirm that MHTC produces hydrochar from shrimp waste of quality comparable to one produced from low-grade lignocellulosic, sewage, and municipal wastes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".