{
  "abstract": "Industrial chemical processes are nonlinear, large-scale, and strongly coupled, making them susceptible to disturbances that threaten operational safety. Conventional model-based and deep learning approaches often struggle with nonlinear dynamics, limited labeled fault data, detection latency, and diagnostic uncertainty. This study presents an intelligent process monitoring framework integrating autoencoder (AE)-based feature learning using denoising autoencoder (DAE), sparse autoencoder (SAE), and variational autoencoder (VAE) models with Dempster–Shafer theory (DST) for uncertainty-aware decision fusion. The framework was evaluated on a benchmark continuous stirred-tank reactor data set containing 12 fault scenarios and normal operating conditions. Baseline accuracies of 87% (DAE), 79% (SAE), and 74% (VAE) improved to 93%, 92%, and 90%, respectively, after DST integration. Following this integration, DAE-DST reduced the false alarm rate (FAR) to 1.41%, increased the G-mean to 0.911, and shortened the mean detection delay (MDD) to 0.56 min. The proposed framework provides a robust and computationally efficient solution for reliable real-time process monitoring in nonlinear chemical processes.",
  "abstract_shown": true,
  "affiliations": [
    "Universiti Sains Malaysia , , Engineering Campus Seri Ampangan , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Remigius Nnadozie Ewuzie"
    },
    {
      "affs": [
        0
      ],
      "name": "Shivaneswar Gunasekaran"
    },
    {
      "affs": [
        0
      ],
      "name": "Zainal Ahmad"
    },
    {
      "affs": [
        0
      ],
      "name": "Norazwan Md Nor",
      "orcid": "0000-0003-0404-1080"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/88tpcxw70g23a.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c08762",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736973/",
  "id": "88tpcxw70g23a",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08762/5456981/Autoencoder-Based-Dempster-Shafer-Decision-Making",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2207|Fault Detection and Control Systems"
    ]
  ],
  "openalex": "W7223391855",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c08762",
  "pmcid": "PMC13736973",
  "pmid": "42991878",
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/88tpcxw70g23a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:40Z",
      "native_id": "10.1021/acsomega.6c08762",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08762/5456981/Autoencoder-Based-Dempster-Shafer-Decision-Making"
    },
    {
      "first_seen": "2026-11-09T12:22:18Z",
      "native_id": "PMC13736973",
      "source": "pmc",
      "url": "https://doi.org/10.1021/acsomega.6c08762"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Autoencoder-Based Dempster-Shafer Decision-Making Framework for Intelligent Process Monitoring: Applications to Nonlinear Chemical Processes",
  "title_text": "Autoencoder-Based Dempster-Shafer Decision-Making Framework for Intelligent Process Monitoring: Applications to Nonlinear Chemical Processes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:18Z",
  "url": "https://research.draconitic.com/paper/88tpcxw70g23a",
  "venue": "ACS Omega",
  "versions": []
}