Investigating the feasibility of guar gum based foams for insulation applications using regression analysis

Abstract

Guar gum is commonly utilized in the pharmaceutical, cos

metic, and food industries. However, its use as a foam material

for insulation purposes in construction fields has not been exten

sively studied, especially with regards to machine learning. This

study aimed to investigate the potential use of foams produced

from biopolymers for insulation and to estimate their properties

using two different regression analyses. The foams were produ

ced using a simple and quick procedure involving a mixture of

guar gum, cellulose, and boric acid in different proportions, and

then dried in the oven. The results of the produced foams showed

promising features such as low density, low thermal conductivity,

and good mechanical properties, which are highly desirable in in

sulation materials. A regression model was developed to analyze

the effects of the components used in the foam formulation and 

to provide an estimated method for future research. The regres

sion model was able to accurately predict the results, with an R

squared value of up to 0.99, allowing for more quantitative data

to be obtained with fewer experimental results. Furthermore, it

was found that guar gum had the most significant effect on the

properties of the foams.