Sequestration of CO₂ using microalgae is one of the imperative, cost effective and environment‐friendly processes for carbon sequestration and for production of biomass. In the present study, a cyanobacterial consortium was used for the sequestration of CO₂ at diverse initial CO₂ concentrations (5–30%), pH of the medium (7–11) and inoculum sizes (5–12.5%). The cyanobacterial consortium showed sustainability in growth upto 30% CO₂. The overall CO₂ fixation capacity was found as 0.0692 and 0.071 mol CO₂/g algae at 15% CO₂, pH 9 and at 10% and 12.5% inoculum, respectively, after 14 days. Characterization of dry biomass before and after CO₂ sequestration was done using Fourier Transform Infrared spectroscopy. Various algorithm of artificial neural network (ANN) model and a number of activation functions were tried to arrive at best ANN model for such process. Finally, Particle swarm optimization was used for optimization of input variables for maximum sequestration of CO₂.

中文翻译：

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使用蓝细菌财团封存二氧化碳和大分子生产的参数研究：实验，建模和优化

使用微藻隔离CO ₂是用于碳隔离和生物质生产的当务之急，经济高效且环境友好的过程之一。在本研究中，使用了CO的多价螯合蓝藻财团₂在不同的初始CO ₂浓度（5-30％），介质（7-11）和接种物尺寸（5-12.5％）的pH值。蓝藻财团显示出可持续的增长，最高可达30％CO ₂。在14天后，在15％CO ₂，pH 9和在10％和12.5％接种量下​​，总的CO ₂固定能力分别为0.0692和0.071mol CO ₂ / g藻类。CO ₂之前和之后的干燥生物质的表征使用傅里叶变换红外光谱法进行螯合。尝试使用各种人工神经网络（ANN）模型算法和许多激活函数来获得用于此过程的最佳ANN模型。最后，粒子群优化用于优化输入变量，以最大程度地隔离CO ₂。