Economic nonlinear model predictive control and scheduling of multiple fed-batch fermenters in a lignocellulosic biorefinery
Bibliographic record
Abstract
In this work, an integrated scheduling and Economic Nonlinear Model Predictive Control (ENMPC) framework is designed for the optimal operation of a fermentation process comprising multiple fed-batch fermenters operating in parallel. A lignocellulosic biomass biorefinery that produces bioethanol, a promising but limited alternative to fossil fuels, is used in this work. The integrated scheduling and ENMPC control framework aims to find optimal decisions among staggered reactors operating simultaneously, being able to imitate continuous operation. Overall, the proposed operation strategy is able to economically distribute feed flows and yeast used in each reactor, by considering coupled scheduling and control interactions, and user defined constraints, e.g., avoiding excessively large feed flow changes. The yeast, the non-constant substrate feeding policies, and the optimal fed-batch times that maximize profit and reject feedstock composition disturbances are obtained. The results show that, in contrast to traditional scheduling and constant feeding rate policies, variable feeding rates integrated with scheduling decisions may lead to reductions in operating costs, while yielding a similar ethanol productivity, which could be a step forward to achieving large-scale sustainable bioethanol production in global decarbonization efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".