AI-Driven Strategies for Automated Microalgae Production Systems: Enhancing Yield, Efficiency, and Commercial Viability

Abstract

Microalgal bioproducts hold significant promise for sustainable applications, but the high production costs associated with cultivation systems remain a major barrier to commercialization. The optimal growth of microalgae depends on a range of factors, including light, temperature, pH, nutrients, and mixing. Each strain of microalgae has unique cultivation requirements, necessitating tailored systems to maximize yield. Traditional cultivation systems, such as open ponds, photobioreactors, and fermentation tanks, each have distinct advantages and limitations. Open ponds rely on natural conditions but are difficult to control, while photobioreactors offer precise environmental control for maximum yield, albeit at higher costs. Fermentation tanks, which exploit heterotrophic growth, reduce operating expenses compared to photobioreactors. Machine learning-driven automation and smart technologies offer a promising strategy to reduce these costs by optimizing cultivation processes through electronic data logging, automated control, and minimal human intervention. This review explores the role of automation in the cultivation, harvesting, extraction, and post-processing of microalgae. It examines recent advancements in automated technologies and their potential to enhance the efficiency and scalability of microalgal bioproduct production. Key considerations for automation in microalgae production include system requirements, technological advancements, and integration across various stages of the production chain. The review emphasizes the mechanical aspects of automation, focusing on technologies that can be automated to improve yield and reduce operational costs, and highlights the need for further innovation in this field to facilitate the widespread commercialization of microalgae-based products.