An object oriented framework for developing dynamic models of a paper machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An object oriented component library is being built for the dynamic simulation of systems in a paper mill. Ever increasing demands for quality require better performance in all areas of paper making. In order to improve these complex systems it is necessary to have a testbed on which to learn about the system, and evaluate performance of new ideas (like control strategies). Modelling of a paper machine approach system is used as the basis for developing a structure that will be expanded to include blocks for simulation of all parts of the paper mill. The simulation components are being built in the modelling language OMOLA for use with the dynamic simulation tool, OmSim. This is an object oriented environment which uses true differential equations and noncausal modelling together with a simultaneous system simulation package. The design attempts to take full advantage of the object oriented and modular features of OmSim, in order to create a class library that can easily be expanded to encompass new components of the mill, and to allow for easy selection of varying submodel complexity. This paper introduces some fundamentals of the OMOLA modelling language, discusses the main features, key to the flexibility of the design, and demonstrates the use of the blocks to simulate the pulp flows from the machine chest to the headbox of a high speed newsprint machine.
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.
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 it