Optimization of a SNCR/LN NOx Reduction System using Model Predictive Control
Bibliographic record
Abstract
An increase awareness in climate change has pushed governments to tighten regulations surrounding environmental emissions limits for industry. Existing industrial plants are required to meet these new regulations, which requires the implementation of innovative technologies. These retrofits are very costly for older facilities to both implement and maintain. Application of one such system at a Metro Vancouver Waste to Energy facility utilized a Low NOxtm and Selected Non-Catalytic Reduction to reduce the plant’s NOx output. This project, completed in 2013, did not perform well due to the requirement of an operator to manually balance the Low NOxTM and Selected Non-Catalytic Reduction system. The manual balancing resulted in an estimated 40% more Ammonia being used at an estimated cost of $ 48 000.00 per year. This project provided the feasibility, design, and configuration of an advanced control algorithm, Model Predictive Control, to maximize the performance of these two systems and to reduce the overall operational cost of the system. \n \nAdvanced process control has a slow adoption rate in Industry especially in smaller facilities, where portray the benefits of newer technologies is an uphill battle. As a result, this project was structured as a Front-End Engineering and Design (FEED) project. The project involved a performance analysis, cost-benefit analysis, design work, and proof of concept configuration in a Digital Twin of the plant’s Distributed Control System. A detailed evaluation of the original control strategy was performed to determine its limitations and constraints. A cost-benefit study showed the benefits of an optimized system. Design documents were created to provide a base for the modifications that would be required to implement the new control strategy. A Digital Twin of the site’s control system was created and used as a development system. The new MPC controller was configured using standard function block programming and was added to the site’s Human Machine Interface. To create the prediction model for the MPC controller, a training set of data was created by performing tests on the live system, and the created model was verified against a separate set of data. The model was then evaluated and refined before creating a simulation and testing the final configuration. \n \nIt was found that an optimized control strategy would result in higher utilization of Low NOxtm and a overall reduction of Ammonia usage. Additionally, it was found that the Ammonia became more effective at a higher temperature, and further savings are attainable by operationally running the furnace at a temperature above 1050 ºC. The final optimization of the system showed significant saving opportunities. The implementation of MPC in this manner showed that implementing new technology can help aging facilities remain viable as emissions regulations continue to be lowered.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".