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基于机器学习的预测碳水化合物生产率在连续种植的Chlorella vulgaris

J V Ferro¹, C E de Farias Silva¹, B M V da Gama¹

¹Technology Center, Federal University of Alagoas, Brazil.

Bioresource technology

|January 23, 2026

概括

机器学习模型可以准确地预测微藻生物质和碳水化合物生产率在连续栽培中. 随机森林展示了卓越的性能,为实时生物过程优化提供了一个"虚拟传感器".

科学领域:

生物技术是生物技术.
藻类生物技术藻类生物技术
机器学习应用机器学习应用

背景情况:

持续的微藻种植在预测碳水化合物生产率方面存在挑战,这是由于对压力的复杂代谢反应造成的.
准确的预测对于优化工业规模的生物工艺和确保高效的生物质和碳水化合物产量至关重要.

研究的目的:

应用和评估各种机器学习 (ML) 模型来预测Chlorella vulgaris中的生物质和碳水化合物生产率.
确定在连续微藻种植中实时监测和控制的最有效的ML算法.

主要方法:

利用来自连续培养Chlorella vulgaris的145个实验数据集.
评估了线性 (多线性回归,,LASSO) 和非线性 (随机森林,人工神经网络,支向量的回归) ML模型.
综合营养,环境和操作变量;利用网格搜索和5倍交叉验证优化模型.

主要成果:

非线性ML模型在预测生产率方面明显优于线性模型.
随机森林模型实现了高精度:生物质R2为0.9072,碳水化合物生产率R2为0.9304.
经过优化后的模型表现出稳健性,并防止过度装配,表明可靠的预测能力.

结论:

机器学习,特别是随机森林,为预测微藻生产率提供了一个强大的工具.
机器学习模型可以作为"虚拟传感器",用于实时生物过程控制和优化.
这种方法减少了对耗时分析的依赖,使工业环境中能够立即进行操作调整.

关键词:

人工神经网络的人工神经网络进行交叉验证.在网格搜索中搜索.微藻类是一种微藻类.随机的森林随机的森林在回归模型中,回归模型是指回归模型.支持向量的回归.

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