IoT Gas and Temperature Monitoring Interface of a Low Temperature Pyrolysis Reactor for the Production of Biochar
Bibliographic record
Abstract
The Langara College Biochar Project consists on the use of a small pyrolysis reactor to convert a variety of biomass compounds into biochar. The current reactor lacks a monitoring interface to keep track of the different gas concentrations and the temperature of the kiln. This project aims to create a device that: communicates gas concentrations and temperatures from the pyrolytic reaction to a website; in the case of an emergency event, sends SMS alerts to the operator, and enables an actuator to shut off the reaction; and finally, stores data locally. The device used for data acquisition and manipulation was an Arduino Mega 2560, fitted with a Wi-Fi shield for the communications and data storage. Sensor wise, the Grove Multichannel Gas Sensor, DHT22, and TMP36, were employed for the measurement of gas concentrations, humidity, and temperature, respectively. ThingSpeak and IFTTT were used for the monitoring and alert system. The scope of this project was to provide a starting point to such a device by employing inexpensive components and laying out most of the software. As a consequence, our results were affected by cross-sensitivity between gas sensors. Regardless, the device is capable of displaying trend-lines for the concentration, sending them to a remote server, storing the data locally, and sending alerts when an emergency event occurs. Future iterations should employ a fully featured website and more precise sensors.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".