{
  "abstract": "Microalgae-based wastewater treatment has the potential to advance tertiary phosphorus recovery while producing biomass for resource valorization. The development of reliable near-term prediction models can help reduce system upsets, support proactive process control, and optimize resource utilization. In this study, Light Gradient-Boosting Machine (LGBM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) models were comparatively evaluated for predicting effluent ammonium, nitrate, and orthophosphate concentrations and biomass dynamics in a full-scale (568 m 3 ·d -1 ) microalgal system using 25 months of operational and microalgal community structure data. LSTM consistently achieved the highest prediction accuracy while effectively capturing system upset events. SHapley Additive exPlanations (SHAP) analysis provided interpretable insights into feature importance across both feature and temporal dimensions, with SHAP-guided feature reduction substantially reducing computational requirements without compromising predictive performance. Benchmarking against the mechanistic process model further identified the complementary application niches of mechanistic and data-driven approaches, providing practical guidance for model selection.",
  "abstract_shown": true,
  "affiliations": [
    "Grainger College of Engineering, Department of Civil and Environmental Engineering",
    "University of Illinois Urbana–Champaign",
    "Institute for Sustainability, Energy, and Environment"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ga-Yeong Kim",
      "orcid": "0000-0002-9664-1923"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "JooMan Kim"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jeremy S. Guest"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6d4q4sztyxby2.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009222/v1",
  "fulltext_url": null,
  "id": "6d4q4sztyxby2",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009222/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2105|Algal biology and biofuel production"
    ]
  ],
  "openalex": "W7213984044",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009222/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/6d4q4sztyxby2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009222",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009222/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Data-driven models for performance prediction in a microalgae-based wastewater treatment process",
  "title_text": "Data-driven models for performance prediction in a microalgae-based wastewater treatment process",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/6d4q4sztyxby2",
  "venue": "ChemRxiv",
  "versions": []
}