Framework for scaling-up extraction processes in nutraceutical beverages: A simulation, techno-economic, and environmental analysis approach
Bibliographic record
Abstract
The nutraceutical beverages market has increased in recent years, motivated by the increasing trend of consumers choosing food and beverages beneficial to health, mostly after the COVID-19 pandemic. Several researchers have proposed different formulations, where the combination of plants has been tested at the laboratory and pilot scales to maximize the desirable features of the beverages, including antioxidant capacity, anticarcinogens, and anti-inflammatory properties. Developing these products requires scaling-up from these scales to the industry one and, hence, identifying the criteria and/or parameters affecting process yield due to the transport phenomena associated with the scale increment. This work proposes a framework for scaling up solid-liquid extraction in a nutraceutical beverage process using available pilot plant data, combining brute-force and empirical scaling approaches. This framework provides an alternative for industries that have acquired equipment without considering the principles of similarity between the larger scale and the laboratory stage. Operating conditions are tuned to reach the product quality at the pilot level and the maximum beverage's antioxidant capacity. A techno-economic analysis of the production process and an environmental evaluation were performed, providing the basis for an effective scaling-up to the industry level. The scaling-up proved to be feasible, as the net present value of the process is $2018,000 with a payback time of 4.83 years; the major source of solid waste is the raw materials with a carbon footprint less than 0.205 MT eCO 2 due this process operates with temperatures lower than 100 °C. The circular economy indicators in this project were circular material usage rate and Waste Stream Recycling Rate. The Circular Material Usage Rate ranged from 16.7 % to 66.7 % depending on the composition of the cocoa husk in the raw material, and the Waste Stream Recycling Rate (%) ranged from 4.4 % to 5 % destined for composting development. The framework is designed to be applicable to other food production processes that encounter equipment constraints. It facilitates the evaluation of process yield and enables the simulation and analysis of economic profitability and environmental impact using circular economy indicators at an industrial/commercial scale. • Feasible scaling-up of solid-liquid extraction for a nutraceutical beverage process. • Operating conditions are tuned to reach the product quality. • The major source of waste is raw materials.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".