Multi-parameter kinetic optimization of Chlorella vulgaris for enhanced CO₂ sequestration and biomass production

The microalga Chlorella vulgaris is a promising biocatalyst for carbon dioxide (CO2) sequestration and biomass generation; however, its growth kinetics are often simplified through single-factor models that neglect co-limiting effects among key environmental and nutrient variables. This study developed a multi-parameter kinetic framework that simultaneously incorporates six interacting parameters: temperature, light intensity, and concentrations of CO2, oxygen (O2), nitrogen (N), and phosphorus (P), to optimise the specific growth rate (μ). A MATLAB-based optimisation routine evaluated over 59 million parameter combinations, and predicted an optimal μ of 0.3101 day-1 at 26 °C, 240 µmolm-2s-1 light intensity, and nutrient concentrations of 300 mg/L CO2, 0.013 mg/L O2, 7.8 mg/L N, and 1 mg/L P. The framework captures synergistic interactions between growth parameters through three-dimensional response surfaces, while uncertainty analysis identified CO2 concentration and temperature as the most influential factors governing μ. By improving the prediction and optimisation of growth kinetics, the model has potential to enhance volumetric biomass productivity and reduce energy intensity in photobioreactor systems. This contributes to lowering operational emissions and improving the economic profitability of microalgal cultivation, particularly for CO2 bio-fixation within a circular bioeconomy context. However, as the model relies on literature-derived kinetic constants, the predicted optimum should be interpreted as a theoretical estimate within a feasible operating range. The framework therefore provides a basis for experimental validation, and further process optimisation towards more sustainable and resource-efficient microalgal production systems.