| Title | Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review |
|---|---|
| ID_Doc | 15244 |
| Authors | Oruganti, RK; Biji, AP; Lanuyanger, T; Show, PL; Sriariyanun, M; Upadhyayula, VKK; Gadhamshetty, V; Bhattacharyya, D |
| Title | Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review |
| Year | 2023 |
| Published | |
| 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 indus-trialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regard-ing 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, deci-sion 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 indus-tries, 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 in-sightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae. |
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