A

Profile-aware prediction and resource-constrained optimization of malachite green photodegradation using Pd-BiFeO₃

Abstract

Machine-learning models for photocatalysis can appear overly accurate when time points from the same experiment enter training and test subsets. A 2 wt% Pd-BiFeO₃/malachite-green dataset was aggregated to 200 profile-time observations and evaluated using row-wise and profile-aware five-fold validation. CatBoost reached R² = 0.983 on the row-wise hold-out, whereas support-vector regression gave the strongest grouped generalization (mean R² = 0.865). Discrete constrained screening indicated that increasing catalyst loading from 1.0 to 1.5 g L⁻¹ reduced the predicted time to 90% degradation from 242 to 210 min and nominal irradiation-energy input by 13.4%. Profile-aware validation is essential for credible optimization.

Authors 7

  1. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

  2. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

  3. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

  4. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

  5. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

  6. RWTH Aachen University

    Affiliation as printed

    IME Process Metallurgy and Metal Recycling , RWTH Aachen University , Intzestrasse 3 , 52056 Aachen , Germany

  7. University of Belgrade

    Affiliation as printed

    Technical Faculty in Bor , University of Belgrade , V.J. 12 , 19210 Bor , Serbia

Cited by 0 stored of 0

References 6

6 results