Chemical-Looping Technology: Application of Nonlinear Model Predictive Control and Reactor Network Modelling Using Biomass as a Fuel
Bibliographic record
Abstract
As climate change becomes a more pressing issue, there is increasing research being performed to investigate greenhouse gas reduction strategies such as carbon capture technology and sustainable fuels. To this end, chemical-looping (CL) technologies such as chemical-looping combustion (CLC) and chemical-looping gasification (CLG) are being explored to improve the sustainability of energy generation. In addition, biomass has recently been studied as a renewable fuel for this process. Various works into CL technologies have been performed, but further investigation is required in the areas of process design and control in order to verify whether this technology can feasibly be implemented for energy generation and to determine the most effective implementation strategies for CL processes. \n \nThe aim of this thesis is to determine reactor design and control strategies which can be implemented to improve the energy generation, gasification efficiency, and carbon capture effectiveness of packed bed CLC and CLG. In this work, optimal control strategies for large-scale packed bed CLC are obtained by implementing nonlinear model predictive control (NMPC). For NMPC, a multiscale model is developed to simulate the plant behaviour and validated against multiple sources of experimental data, while a pseudo-homogeneous model is used as the internal NMPC model to reduce computational costs for implementation of feedback control. By manipulating the inlet air and inert gas fluxes in the oxidation stage, and the inlet fuel flux in the reduction stage, the outlet temperature and CO2 selectivity could be controlled in order to improve the energy generation and carbon capture effectiveness of the process. Then, a reactor network model was proposed to simulate packed bed biomass-fueled CLG and CLC, and validated using experimental data under both CLG and CLC conditions. Using this model, a variety of oxygen carrier (OC) bed lengths and locations were assessed to evaluate the resulting impact on the performance of CLG and CLC. For CLG, the highest gasification efficiency occurred with an OC/biomass ratio of 0.25 combined with a steam/biomass ratio of 1, and the OC placed near the reactor inlet. For CLC, a fully packed bed with steam as the inlet gas resulted in the highest outlet CO2 fraction. \n \nThese design and control strategies obtained through the NMPC scheme and reactor network model can be employed to improve the feasibility of chemical-looping technology. This would make it more CL more practical to implement in existing gasification and combustion processes, improving the sustainability of energy generation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".