High-dimensional sparse recipe formulation in process industries involves nonlinear association of various covariates, bounded by sparsity constraints and multi-criteria tolerances.
Traditional physics-based classical methods (such as Kubelka-Munk) fail to generate sparse candidate recipes properly due to their simplified assumptions and industrial process variations.
This study proposes a hybrid physics-informed uncertainty-aware framework that is structured into three modules:
- Module A, a multi-target forward quality model;
- Module B, a sparse inverse reconstructor;
- Module C, a feasibility-constrained ranking layer.
Then it has been validated in an industrial textile dyeing context with recipe-grouped cross-validation under multi-illuminant quality assessment comprising process and production information, target reflectance, K/S values, and spectral descriptors.
Histogram Gradient Boosting (HGB) with spectral-only features ($R_λ$, K/S) and Gradient Boosting Regressor (GBDT) with recipe-process features outperforms other configurations with out-of-Fold MAE of 0.1655 and 0.1523 in the forward quality model.
For inverse reconstruction, a component-wise median blend achieved a recipe-vector MAE of 0.0295.
Exploratory conditional treatment effect (CATE) suggests an observational association that alkali adjustment reduces error for red and pale shades (CATE = -0.196).
This framework provides a human-in-the-loop decision-support tool that minimizes costly laboratory trials across industrial formulation domains.