COUPLING STATISTICAL DESIGN AND DIFFUSION-BASED MODELING TO INVESTIGATE DRYING KINETICS AND EFFECTIVE MOISTURE TRANSPORT

Resumo

Drying is a key unit operation in the processing of industrial residues, ensuring microbiological stability and preserving attributes relevant for subsequent applications. This study proposes a two-step framework integrating statistical optimization and mechanistic modeling to investigate and improve the drying performance of eggshell waste. First, a 2³ full factorial design evaluated the effects of temperature, particle size, and sample mass on total water loss, identifying temperature, sample mass, and their interaction as the main determinants. Second, drying kinetics under optimal conditions were analyzed using six empirical thin-layer models and a mechanistic model based on Fick’s second law of diffusion. Multi-parameter empirical models, such as Page, Midilli–Kucuk, and Verma, provided excellent fits (R² > 0.98), while the Fickian model yielded a physically consistent effective moisture diffusivity (D_eff = 4.77 × 10⁻¹¹ m².s⁻¹). However, diffusivities estimated from empirical model constants differed by several orders of magnitude from the mechanistic value, highlighting equifinality: distinct models may fit experimental data but lack physical interpretability. This integrated approach supports process optimization and provides insights for design, scale-up, and industrial application.