Roasting temperature distribution control using multi-agent reinforcement learning
Bibliographic record
Abstract
As the initial stage of the zinc smelting process, the roasting process plays an important role in the entire smelting process. The temperature distribution in the roasting process directly determines final product quality, production efficiency, and operation safety. Due to the fact that the roasting temperature often shows nonlinear and spatial-temporally varying dynamics, the process control of such a complex dynamic temperature field is challenging. This paper proposes a new approach employing multi-agent reinforcement learning to facilitate roasting temperature distribution control. First, state, action and reward are defined for the roasting process to formulate the roasting process as a Markov decision process (MDP). Then, to enhance the robustness of the reinforcement learning (RL)-based controller (exploration capability), we propose a multi-agent deep deterministic policy gradient (multi-DDPG) algorithm. The proposed method and the baseline method were run independently for several times via simulation case study. By comparing the overall performance and the worst performance, multi-DDPG showed more stable performance. In the future, it may be possible to achieve temperature distribution control for real roasting 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".