{
  "abstract": "Methyl acetate production via direct carbonylation of dimethyl ether and carbon monoxide involves strong nonlinear interactions among reactor operation, recycle structure, and energy consumption, making conventional simulation-based optimization computationally demanding. In this work, a data-driven framework integrating steady-state process simulation, surrogate modeling, and Bayesian optimization was developed for process intensification. A dataset of 2730 steady-state simulations was generated using a Python-based automated process simulation workflow and a structured design of experiments across the operating domain. Surrogate models were trained to predict carbon conversion yield (C-yield) and cost of specific energy consumption (C-SEC), with neural network-based models showing the best predictive performance and robustness. These models were integrated into a multi-objective Bayesian optimization framework to identify optimal operating regions. Results indicate that high C-yield (up to ~95%) can be achieved with reduced energy cost through lower recycle flow rates and compression demand. Pareto analysis revealed a nonlinear trade-off between C-yield and C-SEC, where further improvements near the conversion limit require disproportionately higher energy input. Validation against rigorous simulations showed deviations below 3% for C-yield and 10% for C-SEC.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03673",
  "author_total": 4,
  "authors": [
    {
      "name": "Trung Ngoc Trinh"
    },
    {
      "name": "Trung Thanh Nguyen"
    },
    {
      "name": "Nikolaos Tsakoumis"
    },
    {
      "name": "Roman Tschentscher"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1vffm43mxg6t2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03673",
  "fulltext_url": null,
  "id": "1vffm43mxg6t2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03673",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03673",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1vffm43mxg6t2.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.03673",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03673"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "A Simulation-Driven Digital Workflow Integrating Bayesian Optimization for Early-Stage Process Design: A Methyl Acetate Production Case Study",
  "title_text": "A Simulation-Driven Digital Workflow Integrating Bayesian Optimization for Early-Stage Process Design: A Methyl Acetate Production Case Study",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/1vffm43mxg6t2",
  "venue": "arXiv",
  "versions": []
}