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

• Type: Another Publication in an International Scientific Journal
• Date: 2025

Authors

Freitas, GR (Author) Other; Badenes, S (Author) Other; Oliveira, R (Author) Other; Martins, FG (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: PROCESSES

Final page: 2956

Indexing

ISI Web of Knowledge - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 42