Machine Learning and Artificial Intelligence for Algal Cultivation, Harvesting Techniques, Wastewater Treatment, Nutrient Recovery, and Biofuel Production and Optimization

The applications of algae, such as in biofuel production, carbon capture and utilization, and assorted microalgae production due to their high nutrient content, wastewater treatment, and bioremediation, make algae cultivation extremely popular in industries. Various process parameters such as algal characteristics and operating conditions of the process affect the yield and productivity. The operating conditions and feedstock characteristics are directly correlated to the product. For example, bio-oil yield produced is directly related to ultimate and proximate analysis; similarly wastewater treatment with algae is dependent upon the organic and inorganic content in the water. Therefore, it is essential to optimize these processes to enhance productivity and identify the efficient method for producing high-quality products with minimal wastage. This can be accomplished by using machine learning (ML), one of the most recently developed tools for modeling a process with multiple inputs to predict output accurately without conducting tedious experiments. ML is widely applied in predictive modeling for growth optimization, nutrient recovery, real-time decision support systems, quality control in algal biomass, energy efficiency optimization, and many more. The incorporation of ML is playing a critical role in the evolution of these algae farming applications. This chapter examines the different applications of artificial intelligence (AI) and ML-based algorithms for product enhancement, process optimization, and gaining important insights into algal biotechnology.