Investigating the Feasibility of Guar Gum-based Foams for Insulation Applications Using Regression Analysis

Guar gum is commonly utilized in the pharmaceutical, cosmetic, and food industries. However, its use as a foam material for insulation purposes in construction fields has not been extensively 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 produced 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 insulation 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 regression model was able to accurately predict the results, with an R2 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. Keywords: Foam, guar gum, thermal conductivity, regression, insulation