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

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