A Learning-Based Decision Tool towards Smart Energy Optimization in the Manufacturing Process
Why this work is in the frame
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Bibliographic record
Abstract
We developed a self-optimizing decision system that dynamically minimizes the overall energy consumption of an industrial process. Our model is based on a deep reinforcement learning (DRL) framework, adopting three reinforcement learning methods, namely: deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) algorithms, combined with a self-predicting random forest model. This smart decision system is a physics-informed DRL that sets the key industrial input parameters to optimize energy consumption while ensuring the product quality based on desired output parameters. The system is self-improving and can increase its performances without further human assistance. We applied the approach to the process of heating tempered glass. Indeed, the identification and control of tempered glass parameters is a challenging task requiring expertise. In addition, optimizing energy consumption while dealing with this issue is of great value-added. The evaluation of the decision system under the three configurations has been performed and consequently, outcomes and conclusions have been explained in this paper. Our intelligent decision system provides an optimized set of parameters for the heating process within the acceptance limits while minimizing overall energy consumption. This work provides the necessary foundations to address energy optimization issues related to process parameterization from theory to practice and providing real industrial application; further research opens a new horizon towards intelligent and sustainable manufacturing.
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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 | 0.000 | 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 it