{
  "abstract": "Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.",
  "abstract_shown": true,
  "affiliations": [
    "Gujarat Biotechnology University"
  ],
  "article_type": "Confirmatory results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "A. B. Behlim"
    },
    {
      "name": "A. Tilewale"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Patel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ek0x571d5eaw6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.21.753176",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753176v1.full",
  "id": "ek0x571d5eaw6",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753176v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "2207|Fault Detection and Control Systems"
    ]
  ],
  "openalex": "W7214069184",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753176v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ek0x571d5eaw6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:08Z",
      "native_id": "10.64898/2026.09.21.753176",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753176v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Toward Digital-Twin-Enabled Bioprocess Monitoring: Fault-Inclusive Soft Sensing of Penicillin Concentration Under Process Deviations",
  "title_text": "Toward Digital-Twin-Enabled Bioprocess Monitoring: Fault-Inclusive Soft Sensing of Penicillin Concentration Under Process Deviations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:08Z",
  "url": "https://research.draconitic.com/paper/ek0x571d5eaw6",
  "venue": "bioRxiv",
  "versions": []
}