Predicting NOx emissions in Biochar Production Plants using Machine Learning

Bernardo del Campo; David Barreno; Erdem Yilmaz; Jochen Weber; Juan Chimbo; Lissete Davila; Marius K\"oppel; Mattia Cerrato; Niklas Witzig; Tim Klausmann

REVIEW 4 major objections 6 minor 24 references

Predicting NOx emissions in Biochar Production Plants using Machine Learning

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A standard Random Forest Regressor trained only on machine states predicts NOx emissions from biochar pyrolysis reactors accurately enough to act as a surrogate in numerical optimization, with an R2 score of 0.97 on one reactor and 0.84…

desk verdict A useful proof-of-concept for NOx soft-sensing in biochar plants, with an unvalidated optimization step that should be clearly labeled as illustrative. read the letter →

arxiv 2412.07881 v1 pith:ULRLX5V6 submitted 2024-12-10 eess.SP cs.LG

Marius Köppel , Niklas Witzig , Tim Klausmann , Mattia Cerrato , Tobias Schweitzer , Jochen Weber , Erdem Yilmaz , Juan Chimbo

show 3 more authors

Bernardo del Campo Lissete Davila David Barreno

This is my paper · ORCID

classification eess.SPcs.LG

keywords NOxemissionpredictionbiocharproductionpyrolysisreactorrandomforestregressionsurrogatemodelnumericaloptimizationIoTmonitoringcontinuous

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

Biochar plants are scaling up quickly, but their pyrolysis reactors are hard to control and most do not measure nitrogen oxide (NOx) emissions continuously. This paper argues that a standard Random Forest Regressor—an ensemble of decision trees whose predictions are averaged—can learn the mapping from machine settings such as valve positions, ventilator settings, and oxygen flows to sensor outputs well enough to predict NOx in real time. On two reactors from different manufacturers, the trained model reached R2 values of 0.97 and 0.84. The same model then serves as a surrogate in a numerical optimizer that minimizes predicted NOx while keeping other outputs like CO2 and O2 within user-set bounds. If the claim holds, continuous emission monitoring and operating-point optimization become feasible on ordinary IoT hardware without installing new NOx sensors.

What carries the argument

The central object is a Random Forest Regressor, an ensemble of decision trees whose output is the average of the individual trees' predictions. The model is trained with machine states as input features and sensor readings—NOx, CO2, O2, temperature—as target values. It carries the argument by acting as a data-driven surrogate for the reactor: it maps control settings to emissions, allowing a numerical optimizer to search for settings that lower predicted NOx under user-set constraints. On the IoT device, the deployed model is simplified to a maximum tree depth of 2 to fit the device's limited resources.

What would settle it

On either physical reactor, take the operating point suggested by the optimizer, hold it long enough for conditions to stabilize, measure NOx with an external reference sensor, and compare to the model's prediction and to the current baseline. If measured NOx does not decrease, or the prediction error at the suggested point is far larger than the training error, the surrogate cannot be trusted to guide real emission reductions.

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Extended reading notes

Core claim

The central claim is that the complex physics of a pyrolysis machine can be approximated from operational data by a random forest, and that this approximation is precise enough to stand in for the machine during optimization. The paper demonstrates this by training the model on historical machine states from two commercial reactors, the PYREG and ARTi machines. It reports strong prediction of measured NOx, with R2 = 0.97 on two months of PYREG data and R2 = 0.84 on two days of externally measured ARTi data. Using the model as a surrogate, the optimizer walks the predicted response surface toward lower NOx while keeping CO2 and O2 within user-defined ranges. The authors present the result as a proof of concept: the same pipeline can be retrained every two hours on an IoT device and can later be extended to other target outputs such as biochar yield.

Load-bearing premise

The load-bearing premise is that the model stays accurate at the new operating points chosen by the optimizer, even though those points can lie outside the historical data it was trained on and no physical test has confirmed that the proposed settings actually reduce NOx.

Editorial extensions

If this is right

Plants without continuous NOx sensors can get near-real-time NOx estimates from readings they already collect, enabling continuous emission monitoring.
A numerical optimizer can use the surrogate to suggest operating points that minimize predicted NOx while satisfying user-set CO2 and O2 constraints.
Because the pipeline works on two reactors from different manufacturers, the approach is not tied to one machine design.
Retraining the model every two hours on the IoT device keeps it fresh as operating conditions drift.
The same surrogate machinery can be extended to other production goals, such as maximizing biochar yield while controlling emissions.

Reading between the lines

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

The optimizer's suggested point is chosen from model predictions only; until a physical reactor run verifies that emissions actually drop, the predicted optimum could be an artifact of extrapolation.
A natural next experiment is to run the optimized settings on the ARTi or PYREG reactor, measure NOx with an external sensor, and compare against baseline—this would directly test whether the surrogate stays accurate off-distribution.
The difference between the two R2 scores suggests that data quantity and coverage, rather than the method itself, currently limit performance, so collecting more labelled NOx data could close the gap.
Because random forests provide no smooth gradient, the optimizer's path is step-like; incorporating uncertainty estimates or active learning would give more reliable guidance at the edges of the training region.

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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

Here's the short version: this is a workshop-quality proof-of-concept, not a benchmark paper. The genuinely new bit is applying a standard random forest to NOx prediction on biochar pyrolysis reactors, and they show it on two machines from different manufacturers. That's a real gap in the literature, and the paper says so plainly.

What works: the pipeline is simple and sensible—train on historical states, retrain every two hours on the IoT device, evaluate on a two-week test period. The two demonstrations (R2=0.97 on PYREG, 0.84 on ARTi) are credible as evidence that NOx can be predicted from machine states in this setting. The choice of random forest is justified by prior uses in industrial control, and the caveats about limited ARTi data are honest.

Where it's soft: the evaluation details are thin. The R2 values are reported without stating whether they come from a held-out time split, cross-validation, or the in-sample fit. With time-series data and a model retrained every two hours, the risk of leakage or autocorrelation inflating the numbers is real. The optimization figure (3c) is the biggest gap: it walks an optimization path over model predictions only, and no one validates that the suggested operating point actually lowers NOx on either machine. Since a random forest is piecewise constant and doesn't extrapolate, the optimizer can find points at leaf boundaries that satisfy the constraints but are physically meaningless. The paper should have either run a confirmation experiment or explicitly labeled the optimization as a hypothetical illustration.

Minor: the constraints in the text are written confusingly (0% < O2 < 10% and 0% < O2 < 20%—presumably one is CO2), and there are typos like 'PRYEG' in the section heading.

Overall, the predictive claim is plausible and the direction is useful, but the optimization claim is not yet demonstrated. The paper is honest about being a 'preliminary test,' and the authors clearly know the difference.

Who this is for: practitioners in biochar or process industries who want a cheap soft sensor. It deserves a serious referee if the venue expects rigorous evaluation, but as a workshop paper it's fine as a poster.

Recommendation: I would accept it for peer review at a workshop or short-paper venue, but for a full journal I'd ask for a proper train/test split, error bars, and an on-machine validation of the optimizer's recommendation.

Referee Report

4 major / 6 minor

Summary. The paper proposes a Random Forest Regressor to predict NOx emissions from machine states (control settings) in two biochar pyrolysis reactors, PYREG and ARTi. It reports R2 values of 0.97 and 0.84, respectively, and shows an optimization path over the surrogate's predictions to minimize NOx under constraints on O2 and CO2 (Figure 3c). The authors claim that this approach can enable continuous NOx monitoring and surrogate-based process optimization on standard IoT devices. The paper is a short proof-of-concept based on historical sensor data from two plants with different manufacturers.

Significance. If the predictive performance were rigorously established, the paper would provide a useful proof-of-concept for a real industrial gap: continuous NOx monitoring and operating-point optimization in biochar plants without dedicated NOx sensors. The dual-plant demonstration and the IoT deployment framing are positive features, as is the use of a standard, easily interpretable model. However, the evaluation methodology is insufficiently described, and the optimization component is not validated against either physical reactor. As it stands, the paper supports a correlation claim, not the stronger claim that the proposed surrogate can reliably guide emission-reducing control decisions. The contribution is incremental but potentially relevant to the applied machine-learning and process-control communities.

major comments (4)

[Section 4, Figures 3a and 3b] The reported R2 scores (0.97 for PYREG, 0.84 for ARTi) are stated without specifying whether they come from a held-out test set, cross-validation, or the training data. Given that the data are time series and the deployed model is retrained every two hours on recent data (Section 3), random splitting or in-sample evaluation could substantially inflate R2 via temporal autocorrelation. Please describe the exact evaluation protocol (e.g., temporal split, walk-forward validation, or cross-validation with leakage prevention) and report the corresponding R2, RMSE, MAE, and the number of test samples for both reactors.
[Section 4, Figure 3c] The optimization path is computed entirely from the Random Forest surrogate's predictions; no experiment on the PYREG or ARTi reactor confirms that the suggested operating point actually reduces NOx. Since Random Forest regression is piecewise constant and does not extrapolate reliably outside the training support, the optimizer may select out-of-distribution machine states whose predicted low NOx is an artifact of the model. The manuscript should either provide an experimental validation of the optimized settings (even a brief plant test) or explicitly limit the claim to predictive modeling and describe the optimization as illustrative.
[Section 3] The relationship between the server-side pre-trained model and the deployed IoT model is ambiguous. The text states that the pre-trained model is simplified to a maximum tree depth of 2 for the IoT device, but it is unclear which model produced the R2 figures in Section 4 and which model was used for the optimization in Figure 3c. Please specify the model version (hyperparameters, training data, evaluation split) for each reported result, and clarify whether the optimized operating points are within the training distribution of the deployed model.
[Section 4, ARTi result] The NOx measurements for the ARTi reactor cover only two days of operation. This limited dataset makes the reported R2 of 0.84 statistically fragile, yet the discussion does not address the number of independent samples, the temporal coverage, or confidence intervals. Please report the sample size, the time span, and the variability of the performance metric (e.g., error bars or a small cross-validation study) to support the generalization claim.

minor comments (6)

[Section 2, heading] The heading misspells 'PYREG' as 'PRYEG'; please correct this typo.
[Section 4, optimization paragraph] The sentence 'The optimization was done by constraining the prediction of O2 and CO2 of the Random Forest Regressor should be constrained by 0% < O2 < 10 % and 0% < O2 < 20 %' is grammatically incomplete and appears to contain a typo: the first inequality likely refers to CO2, not O2, matching the caption of Figure 3c.
[Section 3, evaluation of Figure 2b] The text says the model was first evaluated using 'consistently available sensor data' and shows a temperature prediction in Figure 2b; please clarify whether this prediction is in-sample or out-of-sample, as this affects the interpretation of the figure.
[Figure 2b caption] The caption describes reactor temperature as 'a key feature of how much NOx is produced'; for precision, consider rephrasing to 'a key predictor of NOx production'.
[References] The reference for the global biochar market report incorrectly merges two entries: 'European Biochar Industry' and 'International Biochar Initiative'; please split or correct the author listing.
[Figure 1 caption] The caption says 'we encounter the reader to visit the websites'; this should be 'we encourage the reader'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the ML surrogate and its use in optimization are empirical regression steps, not derivation-by-construction.

full rationale

This paper makes an empirical claim: a Random Forest Regressor trained on machine states predicts NOx emissions with reported R2 values of 0.97 and 0.84. No quantity in the paper is defined in terms of the target it is supposed to predict, and no derivation chain presupposes its conclusion. The surrogate is fit to historical sensor data and used as an objective in a numerical optimization, which is standard model-based optimization rather than circular reasoning. The lack of physical validation of the optimized operating point is a soundness or generalization concern about extrapolation, not a circularity concern. The only self-referential sources are the company website citations for PYREG and ARTi, used merely to describe the reactor designs, and these are not load-bearing for any claimed result. A minor validation-design caveat exists: the R2 figures are reported without an explicit temporal train/test split, so they may partly reflect in-sample fit rather than true prediction; however, this is a correctness-risk issue, not evidence that the prediction is equivalent to its inputs by construction. Overall, the derivation is self-contained and no circular step can be quoted from the text.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or free constants. Its load-bearing assumptions are about data sufficiency, generalization of the fitted model, and validity of surrogate-based optimization. Hyperparameters are the only free parameters, and their final values are not reported.

free parameters (1)

Random forest hyperparameters = Not fully reported; grid searched n_estimators 10-100, min_samples_split 2-5, min_samples_leaf 1-3, final IoT model…
Hyperparameters were chosen via five-fold CV to minimize MSE; the final configuration and its CV score are not provided, so the reader cannot assess overfitting or reproduce the exact model.

assumptions (3)

domain assumption Random forest regressors trained on historical machine-state and sensor data generalize to unseen operating conditions.
The entire monitoring claim rests on this; Section 3 trains on historical data and evaluates on continuous operation without reporting a formal held-out test split.
domain assumption The measured machine states (valves, ventilators, oxygen flow, temperature) are sufficient to determine NOx emissions, i.e., no unmeasured confounders dominate.
If feedstock quality, ambient conditions, or sensor drift drive NOx, the fitted R2 overstates deployable prediction; Section 2 describes feedstocks and operational settings but provides no causal or ablation evidence.
domain assumption Optimizing the surrogate model produces operating points that reduce actual NOx on the machine.
Figure 3c shows an optimization path only over model predictions; no on-machine confirmation of reduced NOx is reported. This assumption is structurally separate from the predictive R2 claim.

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Cite this review

Pith. "Pith review of Predicting NOx emissions in Biochar Production Plants using Machine Learning." pith.science (2026). https://pith.science/paper/ULRLX5V6

@misc{pith2026241207881,
  author       = {Pith},
  title        = {Pith review of: Predicting NOx emissions in Biochar Production Plants using Machine Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ULRLX5V6}},
  note         = {Machine review of arXiv:2412.07881}
}

read the original abstract

The global Biochar Industry has witnessed a surge in biochar production, with a total of 350k mt/year production in 2023. With the pressing climate goals set and the potential of Biochar Carbon Removal (BCR) as a climate-relevant technology, scaling up the number of new plants to over 1000 facilities per year by 2030 becomes imperative. However, such a massive scale-up presents not only technical challenges but also control and regulation issues, ensuring maximal output of plants while conforming to regulatory requirements. In this paper, we present a novel method of optimizing the process of a biochar plant based on machine learning methods. We show how a standard Random Forest Regressor can be used to model the states of the pyrolysis machine, the physics of which remains highly complex. This model then serves as a surrogate of the machine -- reproducing several key outcomes of the machine -- in a numerical optimization. This, in turn, could enable us to reduce NOx emissions -- a key regulatory goal in that industry -- while achieving maximal output still. In a preliminary test our approach shows remarkable results, proves to be applicable on two different machines from different manufacturers, and can be implemented on standard Internet of Things (IoT) devices more generally.

Figures

Figures reproduced from arXiv: 2412.07881 by the authors.

**Figure 2.** Figure 2a: Overview of the used machine learning pipeline. The grey boxes represent the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3a and 3b: Show the prediction of NOx value using historical data from the PYREG [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

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Kevin Anderson and Glen Peters. The trouble with negative emissions. Science, 2016. doi:10.1126/science.aah4567

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