基于四个光区的人工神经网络对集胞藻 PCC 6803 光利用的预测和优化。
Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803.
机构信息
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
Military Representative Bureau of the Army Armaments Department in Nanjing, Nanjing 210000, PR China.
出版信息
Bioresour Technol. 2024 Feb;394:130166. doi: 10.1016/j.biortech.2023.130166. Epub 2023 Dec 8.
Light is crucial in microalgae growth. However, dividing the microalgae growth region into light and dark regions has limitations. In this study, the light response of Synechocystis sp. PCC 6803 was investigated to define four light regions (FLRs): light compensation region, light limitation region, light saturation region, and photoinhibition region. The proportions of cells' residence time in the FLRs and the number of times cells (NTC) passed through the FLRs in photobioreactors were calculated by using MATLAB. Based on the FLRs and NTC passed through the FLRs, a growth model was established by using artificial neural network (ANN).The ANN model had a validation R value of 0.97, which was 76.36% higher than the model based on light-dark regions. The high accuracy of the ANN model was further verified through dynamic adjustment of light intensity experiments.This study confirmed the importance of the FLRs for studying microalgae growth dynamics.
光是微藻生长的关键。然而，将微藻生长区域划分为光区和暗区存在局限性。在这项研究中，我们研究了集胞藻 PCC 6803 的光响应，以定义四个光区（FLRs）：光补偿区、光限制区、光饱和区和光抑制区。使用 MATLAB 计算了细胞在 FLRs 中的停留时间比例和细胞通过光生物反应器中 FLRs 的次数（NTC）。基于 FLRs 和 NTC 穿过 FLRs，使用人工神经网络（ANN）建立了一个生长模型。ANN 模型的验证 R 值为 0.97，比基于光暗区的模型高 76.36%。通过动态调整光照强度实验进一步验证了 ANN 模型的高精度。这项研究证实了 FLRs 对于研究微藻生长动力学的重要性。
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