Optimization of Pyrolysis Operating Parameters for Biochar Production from Palm Kernel Shell Using Response Surface Methodology
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Bibliographic record
Abstract
The growing demand for clean and sustainable energy has catalyzed global efforts toward greener economies and sustainable development.In this study, we investigated palm kernel shell (PKS) biomass obtained from a palm oil mill in Omu-Aran, Nigeria (Latitude 8°08ʹ18.85ʺNand Longitude 5°06ʹ9.36ʺE)as a potential feedstock for biochar production.The biomass underwent pretreatment and sieving into particle size ranges of 0.1-0.2mm, 0.2-0.4mm, 0.4-0.6 mm, 0.6-0.8mm, and 0.8-1.0mm, and was stored in zip-locked polyethylene bags at room temperature for subsequent characterization and pyrolysis experiments.Response surface methodology (RSM) was employed to model and optimize the operating parameters of pyrolysis.The maximum biochar yield (41.1 wt%) was achieved under optimal conditions: temperature of 320℃, reaction time of 6.5 min, heating rate of 12.8℃/min, nitrogen flow rate of 25 cm³ /min, and particle size of 0.9 mm.The model exhibited a p-value of 0.05, a high F-value for biochar (340.5), and an R² of 0.9887, signifying its appropriateness, reliability, responsiveness, and accurate prediction of experimental data.A strong correlation between actual and predicted values for biochar yield was observed.Fourier-transform infrared (FT-IR) spectroscopy revealed the presence of alcohol groups, as evidenced by peaks at 3906.3, 3809.3,3749.7,3649.7,3678.9, and 3600.6 cm⁻¹, as well as alkynes and alkenes, indicated by high-intensity peaks at 2113.4 and 1904.4 cm⁻¹.Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) analyses of the biochar showed white deposits, cleavages, heterogeneous pores, and cloudy formations, indicating inorganic materials and rapid efflorescence during pyrolysis.
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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 | 0.001 | 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 it