Toward Smart Energy Generation Using Economic Model Predictive Control
Bibliographic record
Abstract
Model predictive control (MPC) is the most widely used advanced process control technique. Traditionally, MPC is often used in a hierarchical process control architecture and is mainly designed for tracking set-points determined by the real-time optimization (RTO) layer. The huge success of MPC in industrial applications owes much to its ability to optimally handle process constraints and interactions. However, with the increasing demand for profitability and flexibility in process operations, the hierarchical process control architecture is not sufficient in many applications. In the past decade, a new form of MPC called economic MPC (EMPC) has been developed and is considered as a promising next-generation advanced control method. Unlike MPC that optimizes a quadratic cost function penalizing the deviation of the system state and input from the target steady state, EMPC optimizes a general cost function that is often directly linked to the economic metrics (profit, efficiency, sustainability) of the process. The direct incorporation of an economic cost into EMPC makes it a very flexible decision-making tool. In this chapter, a brief introduction to EMPC is given. Then, the applications of EMPC to a few representative energy-related systems including a post-combustion carbon capture plant, a coal-fired boiler-turbine generating system, a wind energy conversion system and oil sand separation process will be presented. The benefits of EMPC and the challenges in its implementation compared with the traditional tracking MPC will be discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".