Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning

In this work, the algal biomass productivity and its lipid content were explored using a database containing 4670 instances extracted from the experimental results reported in 102 published articles. First, the influences of critical factors such as microalgae species, cultivation conditions, light intensity, CO2 amount, nutrient concentrations, reactor type, stress conditions, cell disruption methods, and lipid extraction solvents on the biomass and lipid production were reviewed. Then, the database was analyzed using machine learning techniques; decision trees were utilized to determine the combination of variables leading to high biomass and lipid content while association rule mining was used to find the specific conditions leading to very high biomass and lipid levels. Decision tree analysis discovered 11 different combinations of variables leading to high biomass productivity and 13 combinations for high lipid content; whereas, association rule mining analysis helped to identify the levels of specific factors for very high biomass and lipid production. It was then concluded that machine learning methods can help to determine the best conditions for optimum biomass growth and lipid yield for microalgae to manufacture renewable biofuels, and this can guide the planning of new experimental works. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study analyzed a large database to investigate the factors influencing algal biomass and lipid production, and utilized machine learning techniques to determine the optimal conditions. Decision trees and association rule mining were used to identify specific combinations and conditions leading to high biomass and lipid content. The findings suggest that machine learning methods can effectively guide experimental works for producing renewable biofuels from microalgae.