Application of multiple linear and polynomial regression in the sustainable biodegradation process of crude oil. (December 2022)

• Record Type: Journal Article
• Authors: Ajona, M.; Vasanthi, P.; Vijayan, D.S.
• Is Part Of: Sustainable energy technologies and assessments. Volume 54(2022)
• Publication Date: 2022-12
• Subjects: Multiple Linear Regression -- Polynomial Regression -- Crude oil contamination -- Bioremediation -- Microbial Strain; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102797
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Statistical regression models have been applied in various biotechnical applications to determine the relationship between response and conditional factors as well as to facilitate prediction. This research focuses on sustainable biodegradation process of crude oil using native and recombinant microbial strains to alleviate the significant environmental threat posed by crude oil contamination. This sustainable process is energy and cost-efficient compared to the traditional measures adopted in the extermination of the contaminated soil due to crude oil. The reaction of the wild and recombinant microbial strains to biodegradation rate was detected in the experimental study. These experimental data were used to develop multiple linear regression (MLR) and polynomial regression models (PR). The coefficient of determination, or R 2 value, was used to validate the constructed models. The PR model had a high R 2 of 0.863, whereas the MLR model had a low value of 0.495. The PR model provided the best match, and it also explains the impact of conditioning factors on the biodegradation rate. To summarize, the recombinant microbial strain yielded a better biodegradation rate with increasing incubation days at lower crude oil contents.