Statistical optimization strategies on waste substrates for solving high-cost challenges in biosurfactants production: a review

Abstract

Abstract Biosurfactants are bio-based amphiphilic molecules with extensive applications in various industries. These eco-friendly alternatives possess numerous advantages over chemical surfactants. However, high production costs hinder market competitiveness of biosurfactants. Production costs of synthetic surfactants range between $1-3/kg, while biosurfactants cost between $20-25/kg. Principal challenges hindering commercialization of biosurfactants are high costs of media constituents and downstream processing, accounting for 30% and 60-80% of production costs, respectively. Thus, cost-effective biosurfactant production would depend on the utilization of environment-friendly low-cost substrates and efficient product recovery. To this end, statistical tools such as Factorial Designs (FD) and Response Surface Methodology (RSM), are employed to optimize the production processes. FD as effective screening models comprise Plackett-Burman Design (PBD) and Taguchi design; and involves quantification of various significant factor effects including the main effect and level of dependency of one factor on the level of one or more factors. RSM predicts appropriate proportions of media constituents and optimal culture conditions; and is reportedly effective in reducing production cost and consequently, market price. Central Composite Design (CCD) and Box-Behnken Design (BBD) are common RSM for optimizing biosurfactants production. CCD assesses the relationship between one factor or more and a set of experimental variables. BBD is considered more proficient than CCD as it requires fewer experimental runs. Most recently, Artificial Neural Network which uses artificial intelligence-based tools to predict biosurfactant production using dependent variables of the process is gaining attention.