For large-scale microalgae cultivation, modelling can play a significant role in predicting and improving biomass generation. Existing kinetic models for outdoor cultivation have drawbacks of reproducibility due to the involvement of uncontrollable environmental factors such as solar intensity, air temperature, relative humidity, dilution rate, etc., affecting the dependent variables such as culture temperature, dissolved oxygen concentration and pH, regardless of the availability of adequate nutrients in the medium. Thus, a kinetic model was proposed here to predict the biomass concentration accurately for all the given variables in terms of specific growth rates. Chlorella minutissima was cultivated in open raceway ponds to calibrate the proposed model, and biomass concentration was estimated as a function of time along with the cultural and environmental variables. Sensitivity analysis demonstrated that the estimated biomass concentration was highly sensitive to the specific growth rate of solar intensity followed by the air temperature. The model was evaluated using C. minutissima experimental data, which exhibited a prediction efficiency of 99.5% with an R² value of 0.96. Further, the model validation was performed with experimental data obtained for outdoor cultivation of Scenedesmus accuminatus, Tetradesmus obliquus, Chlorella variabilis, Scenedesmus acutus, and Chlorella pyrenoidosa from the available literature, which showed the mean absolute percentage errors of 0.38, 9.1, 7.38, 9.5, and 1.72%, respectively. It has also been observed that as the availability of input variables increases, the accuracy of biomass concentration prediction increases. Thus, this model is recommended for forecasting microalgal biomass production.

对于大规模微藻栽培，建模可以在预测和改进生物量生成方面发挥重要作用。现有的室外培养动力学模型由于涉及不可控的环境因素，如太阳强度、气温、相对湿度、稀释率等，影响培养温度、溶解氧浓度和pH等因变量，存在重现性的缺陷，无论培养基中是否有足够的养分。因此，这里提出了一个动力学模型，以根据特定增长率准确预测所有给定变量的生物量浓度。小球藻_在开放的跑道池塘中养殖以校准所提出的模型，并估计生物量浓度随时间以及文化和环境变量的变化。敏感性分析表明，估计的生物量浓度对太阳强度的比增长率以及随后的气温高度敏感。该模型使用C. minutissima实验数据进行评估，其预测效率为 99.5%，R ²值为 0.96。此外，模型验证是根据室外栽培的斜方栅藻、斜生栅藻、变异小球藻、尖形栅藻获得的实验数据进行的, 和Chlorella pyrenoidosa来自现有文献，显示平均绝对百分比误差分别为 0.38、9.1、7.38、9.5 和 1.72%。还观察到，随着输入变量的可用性增加，生物量浓度预测的准确性增加。因此，该模型被推荐用于预测微藻生物量生产。