{
  "abstract": "The choice between traditional machine learning and deep learning for industrial time series prediction remains unsettled, particularly when feature engineering is accounted for. Using 10,000 operational samples from a 660 MW coal-fired boiler, we systematically compare support vector machine (SVM), LSTM, XGBoost, Random Forest, and MLP for NOx emission prediction. Using a 139-dimensional representation comprising the current and first-to-fourth-order lagged values of 27 operational variables, together with four autoregressive NOx lag terms, SVM achieves RMSE = 10.124 mg/Nm3 and R2 = 0.934, outperforming LSTM (20.203, 0.738), XGBoost (12.405, 0.901), Random Forest (12.475, 0.900), and MLP (32.236, 0.332). A fair re-evaluation─training LSTM, Transformer, TCN, and BiLSTM on raw 20-step sequences without manual lag engineering─confirms that deep architectures remain less competitive than SVM with explicit feature engineering (best DL model: fair LSTM, RMSE = 18.541, R2 = 0.779). The HAC-adjusted Diebold–Mariano tests (all one-sided p < 8.1 × 10–7), bootstrap confidence intervals, and Friedman–Nemenyi analysis provide convergent statistical evidence of SVM’s superiority. SHAP analysis identifies O2 as the dominant predictor (SHAP = 10.987), and time-series cross-validation confirms SVM’s robustness (mean RMSE = 6.062, std = 1.804). Single-objective differential evolution achieves a 21.16% NOx reduction at 600 MW load. A data-driven efficiency surrogate replaces hand-crafted formulas in multiobjective NSGA-II optimization, producing a scenario-dependent NOx-efficiency Pareto front whose sensitivity to coal calorific value is explicitly evaluated. These findings demonstrate that on industrial data sets of approximately 10,000 samples, domain-informed feature engineering can enable traditional methods to match or exceed deep learning at lower computational cost.",
  "abstract_shown": true,
  "affiliations": [
    "Southeast University , , ,",
    "Chinese Institute of Coal Science , ,",
    "Nanjing University , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Peixuan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Dining Ma"
    },
    {
      "affs": [
        1
      ],
      "name": "Shicong Jiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Ruiqi Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/94htsbjdjmssg.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c09413",
  "fulltext_url": "https://europepmc.org/articles/PMC13688542",
  "id": "94htsbjdjmssg",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/11/41/64143/5444325/Machine-Learning-Based-NOx-Prediction-and",
  "language": "eng",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ]
  ],
  "openalex": "W7220407500",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c09413",
  "pmcid": "PMC13688542",
  "pmid": "42924025",
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/94htsbjdjmssg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:39Z",
      "native_id": "10.1021/acsomega.6c09413",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/11/41/64143/5444325/Machine-Learning-Based-NOx-Prediction-and"
    },
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42924025",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42924025"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Air Quality Monitoring and Forecasting",
      "subfield": "environmental-science"
    }
  ],
  "title": "Machine Learning-Based NOx Prediction and Combustion Optimization for a Coal-Fired Boiler: The Role of Feature Engineering",
  "title_text": "Machine Learning-Based NOx Prediction and Combustion Optimization for a Coal-Fired Boiler: The Role of Feature Engineering",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/94htsbjdjmssg",
  "venue": "ACS Omega",
  "versions": []
}