The atmospheric CO₂ concentration has been increasing meaningfully in recent years and is involved in climate change. The conventional approaches to reduce atmospheric CO₂ need significant area of storage related with high costs of monitoring, operation, and maintenance. The microalgae based CO₂ capture is an environmentally sustainable choice and the captured CO₂ is not required to dispose further. However, culture conditions of microalgae are very important to maximize CO₂ biofixation rate. Therefore, this study is aimed to investigate the factors of CO₂ biofixation rate utilizing Chlorella vulgaris microalgae in an in-house fabricated microreactor (a hybrid microfluidic-differential carbonator, μ-DC). Initially, the microalgal capability of biofixation was investigated at different independent variables: volume % of CO₂, light intensity, and inlet ratio of microalgae to media. The effects of these variables were analyzed using full factorial design (FFD) and found that light intensity had less impact compared to others, while both CO₂ concentration and mciroalgae to media ratio were found to be significant factors. The response surface methodology (RSM) with face centered central composite design (FC−CCD) and desirability function based approaches were then used for both single- and multi-objective optimization. In multi-objective optimization, the optimized conditions were: 6% CO₂ in air and 0.018 microalgae to media ratio, at which specific growth rate (SGR) of 0.766 d⁻¹, cell counts of 24.36 $x$ 10³ and CO₂ biofixation rate of 0.2416 gL⁻¹d⁻¹ with high overall desirability value (D > 0.7). The values of the coefficient of determination (R²) for the fitted models were found to be more than 80 %. The predictive models were evaluated further based on other performance measuring indicators or error terms (e.g., relative error, mean absolute error, root mean square error) and these values were found apparently to be low, indicating that the model predictions were closer to the experimental results. ANOVA analyses showed that all developed models were statistically significant (p- values <0.05). The results are in good agreement with literature reports.

近年来，大气中的CO ₂浓度显着增加，并且参与了气候变化。减少大气中CO ₂的常规方法需要大量的存储区域，这与监视，操作和维护的高成本有关。基于微藻的CO ₂捕获是环境可持续的选择，并且捕获的CO ₂不需要进一步处理。然而，微藻的培养条件对于最大化CO ₂生物固定率非常重要。因此，本研究旨在探讨寻常小球藻对CO ₂生物固定率的影响。内部制造的微反应器中的微藻类（混合微流体-碳酸化器，μ-DC）。最初，在不同的独立变量下研究了微藻的生物固定能力：CO _2的体积％，光强度和微藻与介质的入口比。使用全因子设计（FFD）分析了这些变量的影响，发现光强度与其他因素相比影响较小，而CO ₂浓度和微藻类与培养基的比例是重要的因素。然后将具有面心中央复合设计（FC-CCD）和基于合意函数的方法的响应面方法（RSM）用于单目标和多目标优化。在多目标优化中，优化的条件是：空气中6％的CO ₂和0.018微藻与培养基的比率，在该条件下，比生长速率（SGR）为0.766 d ^-1，细胞数为24.36$X$10 ³和0.2416 gL ^-1 d ^-1的CO ₂生物固定率具有较高的总体期望值（D> 0.7）。发现拟合模型的确定系数（R ²）的值大于80％。根据其他性能测量指标或误差项（例如，相对误差，平均绝对误差，均方根误差）进一步评估了预测模型，发现这些值显然较低，这表明模型的预测更接近于实验结果。方差分析表明，所有已开发的模型均具有统计学意义（p值<0.05）。结果与文献报道吻合良好。