@inproceedings{eprints17626,
            year = {2023},
       booktitle = {6th International Conference on Science and Sustainable Development},
          author = {Bowofoluwa Sharon Abimbola and Eze Frank Ahuekwe and Elughi Gift Nzubechi and Olubukola Oziegbe and M. I. Oniha},
           title = {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 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.},
        keywords = {Biosurfactants, Statistical Optimization, Waste Substrates.},
             url = {http://eprints.covenantuniversity.edu.ng/17626/}
}