Comparing classic time series models and state-of-the-art time series neural networks for forecasting as-fired liquor properties
Bibliographic record
Abstract
The properties of as-fired black liquor dictate kraft recovery boiler operation. If these properties could be forecasted, operations could be adjusted to optimize boiler performance. Here, we compare the performances of classic time series models and two state-of-the-art time series neural networks for forecasting as-fired liquor heating value, viscosity, and boiling point rise at a Canadian mill. Additionally, we show that, like classic time series models, autoregressive neural networks can be regarded as functions of unknown disturbances, which is useful in comparing model complexities. Our results show that classic time series models can accurately forecast as-fired liquor properties and that classic time series models perform comparably to state-of-the-art time series neural networks. We suspect this is due to the high autocorrelation of mill data that results from frequent measurements relative to long residence times. This autocorrelation is suspected to attenuate the cross-correlations between upstream disturbances and as-fired liquor properties. As a result, neural networks, which are useful for accommodating non-linear cross-correlations and dynamics, struggle to outperform classic time series models and may not always be appropriate for forecasting chemical process parameters.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".