Process Simulation - From Large Computers and Small Solutions to Small Computers and Large Solutions
Bibliographic record
Abstract
This paper reviews the evolution of process simulation in conjunction with the evolution of computer hardware and software technologies from a chemical engineering perspective. A brief history of this hardware evolution is presented and points to exponential growth of computing power. The current personal computers or full function workstations have at least 1GB of memory, 100GB hard drive and run at 4 GHz. We as chemical engineers can only benefit from this continued hardware evolution, as the personal computer has become our full function slide rule.Concurrent with this hardware evolution there has been a proliferation of operating systems and applications software. Some of the applications software has migrated from mainframes and minicomputers and some has been specifically written to take advantage of the user-friendly features available on today's personal computers. A review of this software is presented with particular emphasis on process simulation software. The currently available fifth generation, non-sequential interactive process simulator does make process modeling and design a truly rewarding experience. This software has been designed to allow the personal computer and the engineer to do what each does best, namely the personal computer performing the systematic number crunching and the engineer the intuitive aspect of process simulation and design. The paper will conclude with a look into the future development of process simulation and its interaction with chemical engineering practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".