{
  "abstract": "Mechanistic chromatography models are valuable for process development, but gradient-based parameter estimation and optimization can be hindered by computational cost and the effort of deriving objective-function gradients. To address this concern, a fully differentiable Julia chromatography solver, ChromOps.jl, is presented that combines high-order spatial discretization with discrete forward sensitivity analysis (DFSA). Two high-order spatial discretizations, finite difference summation-by-parts (FD-SBP) and the discontinuous Galerkin spectral element method (DG-SEM), are compared on a 6-component ion-exchange chromatography problem with steric mass action kinetics. Both attain their theoretical convergence rates on manufactured problems, while FD-SBP shows favourable work-precision performance for both forward (primal) and dual-valued simulations and is conceptually simpler to implement. DFSA scales as tDFSA approx 1.4(1+Np)tfwd for up to Np = 24 parameters propagated in a single ForwardDiff.jl chunk, i.e. about 1.4 forward solves per additional parameter. Because the primal and dual-valued solves share the same code path, DFSA provides accurate gradients of user-defined objectives without any hand-derived chain-rule code, enabling accessible gradient-based parameter estimation and optimization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Computers & Chemical Engineering 216 (2027), 109928",
  "arxiv": "2608.06131",
  "author_total": 3,
  "authors": [
    {
      "name": "Kristian Meyer"
    },
    {
      "name": "Maksym Ratajczyk"
    },
    {
      "name": "Christopher Rackauckas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/djghyvak2316n.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.06131",
  "fulltext_url": null,
  "id": "djghyvak2316n",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.06131",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.06131",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/djghyvak2316n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:19Z",
      "native_id": "2608.06131",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.06131"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models",
  "title_text": "ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:19Z",
  "url": "https://research.draconitic.com/paper/djghyvak2316n",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.compchemeng.2026.109928",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}