Thermophysical properties prediction of brown seaweed (Saccharina latissima) using artificial neural networks (ANNs) and empirical models

Abstract

ABSTRACT Thermophysical properties such as thermal conductivity (k), bulk density (ρ), specific heat (C), and thermal diffusivity (D) are crucial for simulating dynamic thermal processes such as drying and temperature-controlled storage. In this study, the effects of product temperatures (T) and moisture contents (MC) on different thermophysical properties of kelp were studied. The thermal conductivity and thermal diffusivity of the samples were measured using a KD2 Pro dual-needle sensor for a temperature range of 30–70° C and moisture content varying from 6 to 90 g (100 g sample)−1. The particle density and the bulk densities of the samples were estimated using a pycnometer with toluene as a working liquid. The k, C, and D values were fitted with a regression equation considering the individual and interaction factors (MC × T). Moisture content and temperature of the sample have significant effects (p < 0.05) on its thermophysical properties. The thermophysical properties calculated using the Choi and Okos' model based on the mass fraction of individual components present in sugar kelp and temperature were significantly (p < 0.05) different as compared to the experimental values. An artificial neural network (ANN) model evaluated eight different configurations of neurons in a single hidden layer for 180 data. The prediction performances of the ANN were evaluated in terms of mean absolute error (MAE), mean relative error (MRE), standard error (SE) and coefficient of determination in MATLAB. Overall, the ANN model’s performance was superior to the Choi and Okos empirical model in predicting the thermophysical properties with a relatively high coefficient of determination and low MAE, MRE, and SE.