Gated Recurrent Knowledge-Guided Attention Network with Adaptive Graph Structure Learning for Forecasting in Chemical Processes
Bibliographic record
Abstract
The prediction of key variables in chemical processes presents significant challenges due to the intricate dynamic relationships among the variables. Most existing graph-based methods for chemical processes rely on a static topological graph structure derived from predefined variable relationships and only consider pairwise relationships between variables, which are inconsistent with practical scenarios. To overcome these limitations, this paper proposes the gated recurrent knowledge-guided attention network with adaptive graph structure learning (GRKAT-GSL), an end-to-end recurrent graph neural network for knowledge-guided graph attention calculation based on the learned topological graph structure. First, variables are embedded using different convolution kernels to extract features, which are then utilized to construct a topological graph as the input to the graph recurrent neural network. Then, a graph attention calculation method, the knowledge-guided attention mechanism (KGAM), is proposed to calculate the relationships between connected nodes in the graph to guide the transmission of information between nodes in the recurrent network. Experimental results on the Tennessee Eastman (TE), fluid catalytic cracking (FCC), and METR-LA data sets demonstrate that GRKAT-GSL outperforms other methods. Furthermore, the analysis of the experimental results indicates that the proposed method is capable of capturing complex relationships in chemical processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".