Chemical Product and Process Modeling
Bibliographic record
Abstract
2020 Best Paper Award The editors of Chemical Product and Process Modeling (CPPM) are delighted to announce that the following three papers have been selected as those which gained most interest from readers during 2020. Simulation of Membrane Gas Separation Process Using Aspen Plus® V8.6 Sharifian, S., Harasek, M., Haddadi, B. (2016) Chemical Product and Process Modeling, 11 (1), pp. 67-72. Mathematical Modeling of Natural Gas Separation Using Hollow Fiber Membrane Modules by Application of Finite Element Method through Statistical Analysis Dehkordi, J.A., Hosseini, S.S., Kundu, P.K., Tan, N.R. (2016) Chemical Product and Process Modeling, 11 (1), pp. 11-15. Study of process factor effects and interactions in synthesis gas production via a simulated model for glycerol steam reforming Adeniyi, A.G., Ighalo, J.O. (2019) Chemical Product and Process Modeling, 14 (1) The editors would also like to thank ALL authors and reviewers for the high standard of their contributions to the journal. Objective Chemical Product and Process Modeling (CPPM) is a quarterly journal that publishes theoretical and applied research on product and process design modeling, simulation and optimization. Thanks to its international editorial board, the journal assembles the best papers from around the world on to cover the gap between product and process. The journal brings together chemical and process engineering researchers, practitioners, and software developers in a new forum for the international modeling and simulation community. Editors represent top engineering institutions across the globe, such as the University of Tehran, the Ecole Polytechnique de Montreal, Rutgers, Indian Institute of Technology, the Technical University Hamburg, Jordan University of Science and Technology, Dalhouse University, University Politehnica of Bucharest, University College London, Auburn University, Universidade do Minho, National University of Singapore, University of Paderborn, University of Lapeenranta, University of Pannonia, The City College of New York, Indian Institute of Technology Roorkee, Texas A&M University at Qatar, Texas A&M University at Qatar, Politecnico of Milano, University of Wollongong, Norwegian University of Science and Technology, CANMET Energy Technology Centre, University of Saskatchewan, Universitat Politecnica de Catalunya, Ecole Nationale Supérieure en Génie des Technologies Industrielles, ENSGTI, University of Western Ontario, Aristotle University of Thessaloniki and Centre for Research and Technology, IFP Energies nouvelles, University of Calgary, Abo Akademi University, Technical University Hamburg, University of Auckland, UCLA School of Public Health and the University of Buenos Aires. Topics equation oriented and modular simulation optimization technology for process and materials design, new modeling techniques shortcut modeling and design approaches performance of commercial and in-house simulation and optimization tools challenges faced in industrial product and process simulation and optimization computational fluid dynamics environmental process, food and pharmaceutical modeling topics drawn from the substantial areas of overlap between modeling and mathematics applied to chemical products and processes Article formats Editorial Notes, Research Articles, Reviews > Information on submission process Submit Article
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".