Stochastic multi-objective economic model predictive control of two-stage high consistency mechanical pulping processes
Bibliographic record
Abstract
Model predictive control (MPC) has attracted considerable research efforts and has been widely applied in various industrial processes. This thesis aims at developing economic MPC (econ MPC) strategies to optimize and control the nonlinear mechanical pulping (MP) process with two high consistency (HC) refiners, which is one of the most energy intensive processes in the pulp and paper industry. It possesses substantial economic motives and environmental benefits to develop advanced control techniques to reduce the energy consumption of MP processes. We propose four econ MPC schemes for nonlinear MP processes. Firstly, assuming that all the state variables are directly measurable, two different econ MPC schemes are proposed by adding different penalties on the state and input to ensure the closed-loop stability and convergence. Secondly, to address the issue of state variable off-sets from the steady-state target induced by above schemes, we further propose a multi-objective economic MPC (m-econ MPC) strategy. An auxiliary MPC controller and a stabilizing constraint are incorporated into the econ MPC. The stability of econ MPC is then achieved by preserving the inherent stability of the auxiliary MPC controller. Thirdly, to remove the assumption that all state variables are measurable, a moving horizon estimator (MHE) is employed to estimate the unmeasurable states. We then propose a practical framework integrating the m-econ MPC and MHE. Finally, we develop a tractable approximation for stochastic MPC (SMPC) to handle uncertainties associated with state variables. It can largely reduce the conservativeness or numerical instability incurred in robust or chance constraints of the traditional SMPC. The effectiveness of the proposed algorithms is validated by simulation examples of a nonlinear MP process consisting of a primary and a secondary HC refiner. It is shown that the proposed m-econ MPC schemes can significantly reduce the energy consumption (approximately 10\%-27\%) and guarantee the closed-loop stability and convergence. Therefore, the proposed methodology presents a great promise on practically implementing m-econ MPC to save costs for MP processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".