A Review of Intelligent Modeling for Microalgae Systems: Integrating Data Mining, Machine Learning, and Hybrid Approaches

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2025

Autores

Freitas, GR (Autor) Outra; Badenes, S (Autor) Outra; Oliveira, R (Autor) Outra; Martins, FG (Autor) FEUP

Revista

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Título: PROCESSES

Página Final: 2956

Indexação

ISI Web of Knowledge - 0 Citações

Outras Informações

ID Authenticus: P-01A-1G6

DOI: 10.3390/pr13092956

Abstract (EN): Despite the extensive research work on microalgae systems over the last decades, there is still a poor understanding of critical cultivation factors that could boost microalgae production economics. Extensive and systematic analysis of microalgae pilot and industrial production data could bring new insights into mechanisms and operational strategies for enhancing microalgae production systems. Recently, various machine learning methods have been employed within data mining workflows to accurately model microalgae growth under various process conditions. This review article provides a comprehensive analysis of data mining and machine learning methods in microalgae systems, with a focus on the effective application of artificial neural networks and deep learning models. It also highlights the importance of data acquisition techniques and real-time data availability that could foster the development of robust machine learning models. In addition, this paper delves into the field of hybrid modeling, a distinct approach that integrates the prior knowledge of mechanistic models with the descriptive power and adaptability of data-driven models. This synergy offers a robust framework to enhance production strategies, addressing critical challenges in scalability and efficiency, eventually paving the way for more sustainable and economical microalgae production systems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 42