Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

Bhattacharyya, Debraj (2023) Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review. Science of The Total Environment, 876. p. 162797. ISSN 0048-9697

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Abstract

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

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IITH Creators:

IITH Creators	ORCiD
Bhattacharyya, Debraj	http://www.orcid.org/0000-0002-5114-881X

Item Type:

Article

Uncontrolled Keywords:

Artificial intelligence; Biorefinery; Machine learning; Microalgae; Wastewater treatment; Anophthalmos with limb anomalies; Artificial Intelligence; Biofuels; Biomass; Biotechnology; Machine Learning; Microalgae; Water Purification; Bioconversion; Biofuels; Classification (of information); Cost effectiveness; Decision trees; Genetic algorithms; Internet of things; Large dataset; Learning algorithms; Learning systems; Microorganisms; Neural networks; Physiological models; Reclamation; Refining; Support vector machines; Uncertainty analysis; Wastewater treatment; Artificial intelligence learning; Biorefineries; Critical review; Environmental awareness; Fresh water resources; Learning tool; Machine-learning; Micro-algae; Performance; Water scarcity GEOBASE Subject Index algal community; artificial intelligence; artificial neural network; computer simulation; machine learning; paradigm shift; physiological response; pollutant removal; wastewater treatment; artificial intelligence; artificial neural network; bibliometrics; biomass; decision tree; depletion; genetic algorithm; learning algorithm; machine learning; microalga; nonhuman; random forest; Review; support vector machine; waste water management; wastewater; artificial intelligence; biotechnology; machine learning; water management; Microalgae

Subjects:

Artificial Intelligence
Civil Engineering
Civil Engineering > Water resources engineering

Divisions:

Department of Civil Engineering

Depositing User:

Mr Nigam Prasad Bisoyi

Date Deposited:

12 Nov 2023 04:48

Last Modified:

12 Nov 2023 04:48

URI:

http://raiithold.iith.ac.in/id/eprint/11736