Optimising microalgae upscaling for sustainable biomass production using statistical design: a Box-Behnken, RSM and ANOVA-based approach

Abstract

Although microalgae have been widely studied for biofuel production, most optimisation studies focus on synthetic media or small scale trials, limiting real world applications. This study targets that research gap by characterising four industrial wastewater streams (sugar, dairy, brewery and sewage) and three high lipid algal strains (Chlorella vulgaris, Scenedesmus sp. and Spirulina platensis), identifying attractive lipid composition of 15.9 %, 28.55 % and 9.05 % respectively and identifying the best suitable wastewater and its adaptability. This is done by applying the Box-Behnken Design (BBD) with Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) to optimise cultivation parameters (pH, light illumination period, wastewater growth medium ratio). The optimised conditions (pH 8.3, 18-hour light, 2:2 BBM-to-sewage ratio, 5 % CO2, 7.5 L/min aeration) achieved 0.79/day at 1 L bench scale conditions, and 0.577/day at 30 L pilot reactor, validating the challenges experienced when upscaling. Additional enhancement from the nitrogen and CO2 achieved 0,752/day. Critical parameter interactions, particularly between light illumination and growth medium composition, were identified (p < 0.0001). Unlike previous studies, this work optimised using real sewage wastewater, offering a practical and validated framework for decentralised wastewater treatment with biofuel production systems.