Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines

David Ghelardi; Kamyar Zeinalipour; Laure Barriere; Marco Gori

REVIEW 3 major objections 5 minor 18 references

Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that K-nearest neighbors predicts CO and NOx emissions from a steady-state gas-turbine dataset more accurately than seven alternative models, making it the strongest candidate for integration into a Predictive Emission…

desk verdict A competent industrial case study whose own Table 1 contradicts its headline claim that KNN consistently beats everything. read the letter →

arxiv 2501.17865 v1 pith:654WUI62 submitted 2025-01-14 eess.SP stat.AP

Kamyar Zeinalipour , Laure Barriere , David Ghelardi , Marco Gori This is my paper · ORCID

classification eess.SPstat.AP

keywords gasturbineemissionsCOandNOxpredictionpredictiveemissionmonitoringsystemmachinelearningcomparisonK-nearestneighborsLSTMGRUXGBooststeady-stateoperation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that machine learning can predict carbon monoxide and nitrogen oxide emissions from gas turbines accurately enough to support predictive emission monitoring, and that one algorithm, K-nearest neighbors, does the job best. The evidence is a comparison of eight models on 113,675 steady-state operating records collected between August 2019 and April 2021, judged by normalized MSE, RMSE, MAE, and MAPE on a held-out test set. A sympathetic reader would care because a model that forecasts emissions from existing process sensors could lower the cost of continuous emissions monitoring and allow operators to adjust combustion in real time for regulatory compliance. The paper also claims that non-linear models decisively beat linear ones, with plain linear regression failing badly on CO.

What carries the argument

The argument is carried by a side-by-side evaluation protocol: all models train on the same 113,675-row steady-state dataset, each with tailored preprocessing (standardization for most models, raw features for trees and XGBoost, a sliding window for LSTM) and hyperparameters tuned to minimize validation MSE, and are then ranked by normalized MSE, RMSE, MAE, and MAPE on the test set. Within that protocol the decisive mechanism is KNN's distance-weighted averaging over its four closest operational neighbors: it predicts an emission value by finding past operating points similar to the current one, rather than fitting a global formula, which suits the complex, non-linear emission patterns the paper finds in the data.

What would settle it

Run all eight models on the same 113,675 rows with several random and chronological train/test splits, keeping the stated preprocessing and hyperparameters: if the NOx MAPE distributions of KNN and GRU overlap substantially across splits, the paper's superiority claim is not stable.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that the K-nearest neighbors regressor, using four distance-weighted neighbors, posts the best overall accuracy on this dataset: the lowest MSE, RMSE, MAE, and MAPE for NOx (0.00030, 0.01741, 0.00864, and 1.76%) and the lowest MAE and MAPE for CO (0.01020 and 6.91%). One nuance in the table is that GRU has the lowest CO MSE and RMSE, so KNN's claim to the top rests mainly on NOx plus the CO error metrics that penalize typical rather than large errors. The authors read the pattern as consistent superiority and conclude KNN is highly suitable for integration into a Predictive Emission Monitoring System, with GRU, LSTM, and XGBoost also performing well and linear models, especially linear regression at 66.3% MAPE on CO, judged inadequate.

Load-bearing premise

The entire ranking depends on a single train/test split that the paper never describes, so the reported margin, about two tenths of a percentage point in NOx MAPE, might not survive a different split.

Editorial extensions

If this is right

KNN is accurate enough on both pollutants to be a leading candidate for integration into Predictive Emission Monitoring Systems.
Non-linear models as a class dominate linear ones on this data, with Linear Regression's 66.3% MAPE on CO marking it as unsuited to the task.
GRU, LSTM, and XGBoost are credible alternatives, with GRU attaining the lowest CO MSE and RMSE.
Accurate ML-based prediction allows operators to shift from after-the-fact compliance checks to real-time emission adjustments.
The tabulated metrics provide a benchmark of eight models on a common dataset for future PEMS research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the paper reports a single train/test split whose construction is never described, the small margins between KNN and GRU (about 0.2 percentage points in NOx MAPE) could invert under resampling; testing across several splits is a direct next step.
The dataset covers only steady-state full-load operation, so the ranking says nothing about startup, shutdown, or transient regimes, where emission spikes are largest; extending the comparison to those regimes is the most consequential test.
GRU's win on CO MSE and RMSE while KNN wins on CO MAE and MAPE shows the 'best model' verdict depends on which error metric regulators weight, so a deployment choice would need a metric aligned with regulatory limits.
The recurrent models were given only 100 training epochs at fixed learning rates, so the comparison may under-represent what LSTM and GRU could reach with proper tuning.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know this paper before citing it as evidence that KNN is the best model for gas turbine emission prediction. It is not. The paper's own Table 1 shows GRU has lower CO MSE (0.00036 vs 0.00041) and RMSE (0.01896 vs 0.02033) than KNN; KNN only wins on MAE and MAPE for CO. The authors even acknowledge this in Section 4.3, then the conclusion says KNN 'consistently outperformed other models.' That overgeneralization is the load-bearing flaw. It is not a deep methodological problem, but it is a real claim-vs-evidence mismatch.

What the paper does well: it is an honest, applied comparison of eight off-the-shelf models on a real industrial dataset (113,675 steady-state rows from a Baker Hughes turbine). Hyperparameters are reported in enough detail to reproduce the models on your own data, and the related work is sensible, citing the public UCI benchmark and more recent deep forest work. That gives the paper value as a practitioner's data point, not as a research advance.

The soft spots are the usual ones for a private-data benchmark: no train/test split description, no error bars, no statistical tests, and no code or data released. So the model ranking is not independently verifiable, and the paper gives no sense of whether the differences between KNN, GRU, and XGBoost are meaningful or noise. You should also be careful that the CO conclusion depends on preferring MAE/MAPE over squared error without justification. If squared error matters for your PEMS, GRU wins on CO.

Who is this for? Engineers working on PEMS for similar turbines who want a quick sanity check on model families. It is not for researchers looking for new methods or reproducible benchmarks. A serious referee could send this back with a request to fix the conclusion and report the experiment setup; it is not a desk-reject caliber paper, but it is not a strong accept either.

Referee Report

3 major / 5 minor

Summary. The paper reports a comparative evaluation of eight machine learning models (Linear Regression, SVM, Decision Trees, XGBoost, MLP, LSTM, GRU, and KNN) for predicting CO and NOx emissions from gas turbines, using a proprietary 113,675-row steady-state dataset. The authors describe preprocessing, per-model hyperparameters, and report normalized MSE, RMSE, MAE, and MAPE on a test set. The abstract and conclusions claim that KNN consistently outperformed all other models and is therefore most suitable for predictive emission monitoring systems (PEMS).

Significance. If the claimed ranking were rigorously established, the paper could be a useful applied benchmark for PEMS model selection. The use of a real industrial dataset is a potential strength, and the comparison across both traditional and deep learning models is appropriate in scope. However, the significance is severely limited by the fact that the paper's central claim is contradicted by its own reported results for CO, and by the absence of any reproducible experimental protocol (no train/test split, no error bars, no statistical tests, no code or data release). As it stands, the paper does not provide a defensible basis for preferring KNN over the other models, so its practical and scientific contribution is marginal.

major comments (3)

[Section 5 (Conclusion) and Table 1] The conclusion that "KNN consistently outperformed other models" is contradicted by Table 1 for CO emissions. For CO, GRU achieves lower MSE (0.00036 vs 0.00041) and lower RMSE (0.01896 vs 0.02033) than KNN, while KNN only wins on MAE (0.01020 vs 0.01062) and MAPE (6.90689% vs 8.34426%). Section 4.3 itself acknowledges that GRU achieves the lowest MSE and RMSE for CO. Therefore the central claim is not supported by the paper's own data; the authors would need to either weaken the conclusion to a per-metric, per-pollutant statement or provide a principled justification for why MAE/MAPE should be decisive over squared-error metrics.
[Section 4.1 and 4.2 (Data Collection and Training Setup)] The experimental protocol is underspecified to the point of unreproducibility. The paper never states how the 113,675 rows were split into training, validation, and test sets, whether the split respects temporal order (which matters for time-series data), what window size was used for the LSTM and GRU inputs, whether the target variables were standardized, or how the reported 'normalized' metrics were computed. Without these details, the ranking in Table 1 cannot be independently checked, and the results could change under a different split or preprocessing choice.
[Section 4.3 (Results and Discussion)] The paper reports a single run per model with no confidence intervals, standard deviations, or statistical significance tests. Several metrics are very close: for NOx, KNN's MSE (0.00030) and GRU's MSE (0.00032) differ by about 6%, which may well be within run-to-run noise. The claim that KNN 'consistently' outperforms is therefore not statistically established. The authors should report multiple runs with different seeds, error bars, or at least a significance test (e.g., paired bootstrap or Wilcoxon) to support the ranking.

minor comments (5)

[Throughout] There are numerous formatting and typographical issues: 'T raining Setup' in Section 4.2, 'T able 1' in Section 4.3, LaTeX artifacts such as '$num_epochs' and '$learning_rate' in the GRU paragraph, and an incomplete sentence in the Decision Trees paragraph where the model name is truncated to 'G'.
[Figure 2] Figure 2 is described as a 'histogram plot' but the subplots are bar charts of metric values per model; the caption should be corrected and the y-axis labels are missing.
[Section 3 (Methodology)] The description of KNN says it is used for 'classification problems,' but in this paper it is used as a regressor; this should be corrected for clarity.
[Section 4.1] The dataset is described as containing 13 numerical variables, but the paper does not list them or cite a public source; if the dataset is proprietary, this should be stated explicitly, while a full feature list would help readers assess the modeling setup.
[Equation (2)] The MAPE formula is undefined when yi = 0; since emission measurements can be near zero, the paper should discuss how such cases were handled in the normalized target space.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: standard supervised learning benchmark with no self-citation chain or definitional reduction; the internal inconsistency in the conclusion is a correctness issue, not circularity.

full rationale

The paper is an empirical model comparison. Each model is trained on historical gas-turbine data and evaluated on a test set using standard metrics (MSE, RMSE, MAE, MAPE). Hyperparameters are tuned on a validation set by minimum MSE, which is a conventional fitting procedure, not a hidden circular step. There is no theoretical derivation that reduces to its own inputs, and no load-bearing self-citation: the references are standard ML and PEMS literature, not prior work by this paper's authors invoked to justify the central claim. The claim that KNN 'consistently outperformed other models' is contradicted by the paper's own Table 1 for CO, where GRU has lower MSE and RMSE, but that is an internal correctness/consistency problem, not circularity. The unreported train/test split and lack of code are reproducibility concerns, not definitional equivalence. Accordingly, no circular step can be exhibited with quotations, and the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities or physical constants. Its central claim rests on a proprietary dataset, an unreported train/test split, and hyperparameters tuned on an unspecified validation set. The key unstated premises are that steady-state ABC mode data represents operational reality and that a single split yields a stable model ranking.

free parameters (4)

KNN n_neighbors = 4
Selected on validation MSE for both CO and NOx; no search range reported (Section 4.2).
KNN weights = distance
Chosen for both pollutants; no comparison with 'uniform' weights reported (Section 4.2).
SVM C (CO) = 100
Tuned on validation MSE; no grid search details (Section 4.2).
GRU/LSTM window size = unspecified
Windowing for recurrent models is mentioned but the window length is never stated (Section 4.1).

assumptions (4)

domain assumption The 113,675 recorded rows under ABC mode are a representative sample of operational conditions.
Section 4.1 states data was recorded under steady-state full-load conditions; no evidence this generalizes to transient or other modes.
domain assumption A single, unspecified train/test split yields a stable model ranking.
Sections 3.1 and 4.2 describe validation-based tuning but never describe the split; no repeated splits or cross-validation.
domain assumption The 13 numerical variables are sufficient predictors for CO and NOx.
No feature selection or ablation is reported; the paper assumes sensor variables capture emission drivers (Section 4.1).
domain assumption Validation MSE is a reliable proxy for test performance.
Hyperparameters were chosen by minimum validation MSE (Section 4.2), but no description of the validation set or its similarity to test is given.

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Cite this review

Pith. "Pith review of Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines." pith.science (2026). https://pith.science/paper/654WUI62

@misc{pith2026250117865,
  author       = {Pith},
  title        = {Pith review of: Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/654WUI62}},
  note         = {Machine review of arXiv:2501.17865}
}

read the original abstract

This paper addresses the environmental impacts linked to hazardous emissions from gas turbines, with a specific focus on employing various machine learning (ML) models to predict the emissions of Carbon Monoxide (CO) and Nitrogen Oxides (NOx) as part of a Predictive Emission Monitoring System (PEMS). We employ a comprehensive approach using multiple predictive models to offer insights on enhancing regulatory compliance and optimizing operational parameters to reduce environmental effects effectively. Our investigation explores a range of machine learning models including linear models, ensemble methods, and neural networks. The models we assess include Linear Regression, Support Vector Machines (SVM), Decision Trees, XGBoost, Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and K-Nearest Neighbors (KNN). This analysis provides a comparative overview of the performance of these ML models in estimating CO and NOx emissions from gas turbines, aiming to highlight the most effective techniques for this critical task. Accurate ML models for predicting gas turbine emissions help reduce environmental impact by enabling real-time adjustments and supporting effective emission control strategies, thus promoting sustainability.

Figures

Figures reproduced from arXiv: 2501.17865 by the authors.

**Figure 1.** A typical industrial gas turbine used in power generation. The comparative analysis conducted in this study aims to identify which ML models not only predict emissions most accurately but also adapt effectively to real-time data, thereby providing actionable insights that can influence immediate operational decisions. This could improve how industries approach emission reduction, shifting from reactive to preemptiv… view at source ↗

**Figure 2.** Normalized performance of various ML models on CO and NOx emission prediction. trend where the GRU achieves the lowest MSE and RMSE (0.00036 and 0.01896 respectively), yet KNN leads with the smallest MAE and MAPE (0.01020 and 6.90689% respectively), reinforcing its efficacy across both pollutants. Models such as XGBoost, Multilayer Perceptron (MLP), and LSTM show better accuracy than more traditional approaches li… view at source ↗

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Si, M., Tarnoczi, T., Wiens, B. & Du, K. Development of predictive emissions monitoring system using open source machine learning library–Keras: A case study on a cogeneration unit.IEEE Access. 7 pp. 113463-113475 (2019)

work page 2019

[2] [2]

An experimentally verified NOx emission model for gas turbine com- bustors

Hung, W. An experimentally verified NOx emission model for gas turbine com- bustors. Turbo Expo: Power For Land, Sea, And Air . 79771 pp. V01BT02A009 (1975)

work page 1975

[3] [3]

& Mongia, H

Rizk, N. & Mongia, H. Semianalytical correlations for NOx, CO, and UHC emis- sions. (1993)

work page 1993

[4] [4]

& Chen, K

Chien, T., Chu, H., Hsu, W., Tseng, T., Hsu, C. & Chen, K. A feasibility study on the predictive emission monitoring system applied to the Hsinta power plant of Taiwan Power Company.A&WMA 53, 1022-1028 (2003)

work page 2003

[5] [5]

& Han, C

Lee, Y., Kim, M. & Han, C. Application of multivariate statistical models to pre- diction of NO x emissions from complex industrial heater systems. Journal Of Environmental Engineering. 131, 961-970 (2005)

work page 2005

[6] [6]

& Gomes, J

Saiepour, M., Schofield, N., Leden, B., Niska, J., Link, N., Unamuno, I. & Gomes, J. Development and assessment of predictive emission monitoring systems (PEMS) modelsinthesteelindustry. Proc. Iron Steel Technol. Conf.. 2pp.1121-1132(2006)

work page 2006

[7] [7]

& Mertens, J

Vanderhaegen, E., Deneve, M., Laget, H., Faniel, N. & Mertens, J. Predictive emissions monitoring using a continuously updating neural network.Turbo Expo: Power For Land, Sea, And Air . 43970 pp. 769-775 (2010)

work page 2010

[8] [8]

& Others Predicting CO and NOxemissions from gas turbines: novel data and abenchmark PEMS..TURKISH J

Kaya, H., Tüfekci, P., Uzun, E. & Others Predicting CO and NOxemissions from gas turbines: novel data and abenchmark PEMS..TURKISH J. Electr. Eng. Com- put. Sci.. 27, 4783-4796 (2019)

work page 2019