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Talk · International Conference on Industrial Engineering, Applications and Manufacturing · May 18, 2026

Modelling of the Spray-Drying Process of Pectin Concentrates Using an Ensemble of ML Models and Interpretable AI Methods

Sh. Е. Kholov, Mullosharaf K. Arabov, Zainiddin K. Muhiddin

Mullosharaf Arabov
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Abstract

This paper presents an approach to computer modelling and intelligent optimisation of the spray-drying process for pectin concentrates, employing an ensemble of machine learning (ML) methods and explainable artificial intelligence (XAI). Within the Industry 4.0 framework, the influence of four key control parameters-inlet and outlet temperatures, air flow rate, and solution feed rate-was investigated using a full factorial experimental design. To construct a predictive model for product yield, an ensemble of $\mathbf{1 2}$ ML algorithms was evaluated, with the gradient boosting model achieving the best performance ($\mathbf{R}^{\mathbf{2}}=$ 0.97, RMSE $=0.21 \mathrm{~g}$). Model interpretability was ensured through SHAP (SHapley Additive exPlanations) analysis, enabling a quantitative assessment of factor contributions. Using differential evolution optimisation, optimal process regimes were determined (inlet temperature $\mathbf{1 8 5 - 1 8 9}{ }^{\circ} \mathrm{C}$, solution feed rate $\mathbf{1 4 - 1 5 ~ m l} / \mathbf{m i n}$), leading to a $\mathbf{3 1. 5 \%}$ increase in product yield without degrading functional properties. The proposed ML-XAI framework offers a practical methodology for integration into industrial engineering systems, supporting the development of digital twins and intelligent process control.

Microencapsulation and Drying ProcessesMachine Learning in Materials ScienceFood Drying and Modelingspray dryingprocess optimizationensemble learning

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