Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.

中文翻译：

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微藻培养系统的机器学习方法综述

微藻在水生环境中的生物质生产中发挥着至关重要的作用，并且因其在生产生物燃料、生物材料、生物活性化合物和生物基化学品方面的潜力而日益得到认可。解决粮食和燃料短缺等迫在眉睫的全球挑战的需要推动了这一日益增长的重要性。增强生物基产品的价值链需要实施先进的筛选和监测系统。该系统对于定制和优化栽培条件、确保最终所需产品的利润丰厚且高效生产至关重要。这反过来又强调了稳健的预测模型的必要性，以准确模拟初始培养阶段不同条件下的藻类生长，并模拟其在下游阶段的后续处理。为了实现这些目标，各种基于机械和机器学习的方法已被独立采用来建模和优化微藻过程。这篇综述文章彻底研究了文献中描述的用于建模、预测和监测生物能源、制药和食品工业等各种应用中的微藻生物量的技术。在强调每种方法的优点和局限性的同时，我们深入研究了新兴的混合方法领域，并对这种不断发展的方法进行了详尽的调查。我们探讨了当前阻碍混合技术实际实施的挑战，并从其他机器学习辅助领域的成功应用中汲取灵感，回顾了各种可行的解决方案来克服这些障碍。