Integration of design and control for renewable energy systems with an application to anaerobic digestion: A deep deterministic policy gradient framework
Bibliographic record
Abstract
In recent years, the urgent need to develop sustainable processes to curb the effects of climate change has gained global attention and led to the transition into green technologies, such as Anaerobic Digestion Systems (AD). As these technologies present a complex dynamic behavior, there is a motivation to seek for new ways to optimize these systems. This study presents a Deep Deterministic Policy Gradient (DDPG) strategy for integration of process design and control. DDPG is a state-of-the-art reinforcement learning algorithm used to search for optimal solutions. The proposed approach considers stochastic disturbances and parametric uncertainty. Also, a penalty function included in the reward function is considered to account for process constraints. The proposed approach was tested in AD systems involving Tequila vinasses. Two reactor AD configurations were explored under multiple scenarios using the proposed DDPG strategy. While the two-stage AD system required a larger capital investment in exchange of higher amounts of biogas being produced, the single-stage AD system required less investment in capital costs in exchange of producing less biogas and therefore lower revenues than the two-stage system. The results showed that DDPG was able to identify optimal design and control profiles thus making it an attractive method for optimal process design and operations management of renewable systems.
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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 | 0.001 | 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".