Toward a Flow Following Ionic Conductivity and Temperature Sensor Package
Bibliographic record
Abstract
We have developed a combined ionic conductivity and temperature sensor package for multiphase chemical reactors, key elements of SmartChip, a flow following, data logging sensor. The initial application of the sensor is to monitor the process inside the hot and caustic chemical environment of a kraft pulp digester (pH ∼ 13, temperatures from 25 to 175 °C, reaching a maximum of 180 °C and pressures up to 2 MPa). The sensor package consists of a 1000 Ω platinum resistance temperature detector (RTD) enclosed in a stainless steel container and a four-electrode conductivity sensor consisting of stainless steel electrodes and installed on a polyetheretherketone (PEEK) package. The sensor package is designed ultimately to move within the kraft pulp digester and record sensor data. The sensors were tested at temperatures of up to 140 °C in NaOH concentrations of 10–100 g/L as Na 2 O with and without the presence of wood chips. Changes in concentration were clearly discernible as changes in conductivity. The presence of chips reduced conductivity as expected, and this reduction depended on the degree of cooking. Although further calibration work is needed to quantitatively determine concentration inside a digester, process variations should be readily detectable. The average power consumption of the sensors is 5 mW, which is sufficiently low to enable autonomous operation of the sensor under battery power. The sensor packages were tested for a full kraft pulping cycle and survived with no signs of corrosion. The demonstration of conductivity measurements represents an important step toward the successful implementation of flow following measurements in the kraft pulping processes to aid process optimization and design.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".