Advanced Control of a fed-batch reaction system to increase the yield in the polyhydroxyalkanoates production process
Bibliographic record
Abstract
ABSTRACT: Plastics are one of the main products of the petrochemical industry, due to its wide variety of physical and chemical properties. However, the intensive use of plastics has created an important environmental problem, and therefore, many alternatives are currently explored for reducing it. The Polyhydroxyalkanoates (PHAs) are polymers from biological origins, which are an environmentally friendly option for replacing the use of petroleum based plastic materials in a wide number of applications [1], [2]. Currently, big companies in China, United States, Brazil and Canada are carrying out the production of PHAs at industrial level. However, in order to replace or at least to compete against the petroleum –based plastic materials, it is still needed to assure the technical and economic feasibility of the process. For this reason, it is important to develop strategies towards increasing the yield of the process. For this, advanced model-based control strategies must be applied instead of the classical control strategies, which have shown to be inefficient in many bioprocess applications [3], [4], [5] and [6]. Since 2012, the biotransformation research group at Universidad de Antioquia has conducted research projects towards finding a technical and economical feasible alternative for producing PHAs using Colombian agricultural wastes. Preliminary results obtained at bioreactor scale have shown to be promising assuring the technical feasibility of the production process. However, in order to increase the productivity of the process for assuring its economic feasibility, optimization and control tools are proposed to be used in this master thesis. In this work, the optimizing control of the PHAs fed-batch process is carried out by formulating and solving a dynamic optimization problem for maximizing the process productivity. The optimization problem is subject to constraints on the feed flow rates, the final volume and the maximum concentrations reached on the substrate and nitrogen source, in order to avoid inhibition. Furthermore, the number average molecular weight distribution (Mn) is predicted using state estimation strategy and is used as constraint in order to fulfill desired end-product specifications. By solving the problem stated, it was possible to find the optimal values for the substrate and the nitrogen source feed concentrations, as well as their optimal feeding profiles that maximize the process productivity. Solution of the dynamic optimization problem was carried out by the control vector parameterization approach. Different kinds of parameterization for the control vector were tested in order to compare their advantages and disadvantages. Results of the dynamic optimization problem have shown that sinusoidal type control profiles lead to higher productivity values (i.e. in comparison to step-type or constant feeding policies), while fulfilling the constraints, being remarkable that the number average molecular weight was kept around 4.05x105 g/mol and the productivity of the final amount of polymer over 138.44 g or 0.62 g/Lh for a 32 h fermentation and a final volume of 7 L. Finally, it was shown that the optimizing control strategy coupled to the prediction of Mn is an interesting and applicable alternative that could help to improve the PHAs productivity at industrial scale. Further work will be directed towards applying the mentioned strategy experimentally at pilot plant scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".