A Systematic Computer-Aided Product Design and Development Procedure: Case of Disinfectant Formulations
Bibliographic record
Abstract
Product formulation design involves selecting a few ingredients from a large set through screening based on several criteria, and using the optimal proportion in the formulation. The limitation in the traditional strategy for chemical product formulation design is to carry out a large number of trials, which in most practical cases, is either economically infeasible or a very slow process. Furthermore, the presence of constraints, sometimes contradictory to some extent, further complicates the formulation design process. Such traditional trial-and-error and one-factor-at-a-time methodologies can be very cumbersome. They can also lead to a slow and high-cost process. The outcome of following such techniques does not usually lead to optimal designs. In this work, a methodology that deals with the complexity of product formulation design problem with contradictory constraints is presented and illustrated in a real case study. This methodology starts with defining needs for a new product and generating ideas. It then screens the candidate ingredients, using design of experiment techniques, and develops a model for each response. It then inverts the models using a nonlinear optimization technique, and obtains an optimal design for the product based on the desired properties. This methodology is proven to be more effective, faster, and less expensive in the development of new products or improvements on the existing ones. Furthermore, the final product is an optimal formulation, with respect to a preset performance measure and desired properties. The procedure is illustrated and tested on the case of disinfectant formulations. The optimized formulation was prepared, tested, and compared to an existing formulation. The optimized formulation faired significantly better than the existing product, in terms of technical and economic preference.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".