Conversion of Mixed Waste of Wood and Plastic to Clean Fuels UsingPyrolysis in Nigeria – Numerical Study
Bibliographic record
Abstract
Waste management is a major challenge in Nigeria, where around 32 million tons of waste is generated annually including 13 million tonnes of agricultural waste and 2.5 million tons of plastic waste. Currently, the waste management system is very inefficient where almost 70% of the waste ends up in landfills, sewers, beaches and water bodies causing serious environmental and health problems. This work numerically investigates the conversion of mixed wastes (wood and plastics) to produce clean and affordable solid, liquid and gaseous fuels that can be used for cooking, heating and electricity generation. Pyrolysis process which involves heating the waste at different rates in the absence of Oxygen has received significant research interest since it can convert various types of waste to clean fuels thus reducing fossil fuel consumption and CO2 emission. For example, wood waste can be converted to high-quality syngas, oil, and char while plastic waste can be converted to high-quality syngas, oil, light and heavy waxes. Also, it generates lower emissions compared to other waste conversion processes such as combustion, gasification, and plasma treatment. In this study, a numerical model is developed to simulate the pyrolysis process of mixed wood and plastic waste materials to predict the outputs in terms of char, syngas, oil and wax production. The model is based on the kinetics of the reactions associated with wood and plastic when subjected to different heating rates from 320 to 923 Kelvin for 20 minutes. Results showed that for 1kg of mixed waste (30% plastic and 70% wood) and after 20 minutes of heating, the output consists of 50.13% syngas, 8.35% oil, 9.5% char, 10.8% light Wax and 8.33% heavy wax. This modelling allows for controlling the output composition based on varying the input waste constituents which can optimize the waste conversion process.
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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".