3D Printed Prototype for Water-soluble Polymer Preparation: PLC Operation
Bibliographic record
Abstract
Water-soluble polymers (WSPs) are commonly used in different oil and gas applications, such as enhanced oil recovery (EOR), drilling fluids, disposal water treatment, and partially in cementing.Water-soluble polymers are prepared manually in small amounts at laboratories.However, such a preparation method could have multiple disadvantages, such as: human error, poor consistency, high process lead time, adjustment of powder batches, and waste materials.To overcome such limitations, we propose a novel methodology to automate the polymer preparation processes through implementing multiple advanced stages.These processes consist of initial design, software programming, as well as advanced 3D printing and manufacturing.First, the solid work design software is utilized to make the original model.Second, the generated model is constructed using 3D printing technology.Finally, the programmable logic controller (PLC) is connected to operate the automation process including polymer doses and mixing time.At the designing stage, internal components might hinder the preparation and mixing processes.Therefore, the placement of such internal components is considered to ensure flow continuity and solution homogeneity.The system components consist of funnel, hollow pipe, screw extruder, vertical powder banks, electrical valves, rotating motor, and nozzle.The optimum design is then sent to 3D printing for manufacturing at high resolution.The PLC is operated to function the whole system.The applied PLC components and setup are: three input gates, three timers, and five output gates.In short, the novel automated preparation system in this study will eventually produce more homogenous and consistent polymeric solutions.In addition, it will minimize the common encountered uncertainties in the conventional methods.It will produce a more representative solution and improve the mixing process.The new system can be also upgraded to improve chemical mixing procedures at large volumes in oil and gas operations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".