One novel approach is the usage of artificial intelligence (AI) and/or machine learning (ML) algorithms to monitor, estimate, and manage the risks in microalgal processes. Artificial intelligence (AI) allows computers to simulate the intelligence of humans and machine learning is an artificial intelligence subfield. The basic idea of machine learning is to use inductive analysis to expand the connections between input and output, which are subsequently used to impact decisions in new scenarios. The input variables are pH, carbon dioxide level, inoculum, illumination, temperature, and nutrient level, whereas the outputs are biomass and bioproduct yields in microalgal processes. Enormous quantities of data generated by sensor monitoring systems may be employed as inputs to optimize parameters in AI/ML models. Therefore, AI/ML algorithms can forecast biomass/bioproduct production.

MLAs have recently been used in the research of microalgal processes, although this is still in ithe early stages for industrial applications. Approximately 75% of the energy consumed is wasted during drying step; however, using ML models might dramatically cut costs while improving output. In particular, artificial neural networks (ANNs) are mostly used for predicting microalgal growth, the support vector machine (SVM) algorithm is chosen for microalgal wastewater treatment. On the other hand, the genetic algorithm (GA) is utilized to optimize biomass and bioproduct production and the random forest (RF) algorithm performs better when determining whether microalgal populations are dead or living. In general, the reliability of machine learning models improves as data availability rises.

Using AI/ML models may help achieve microalgal production objectives through a more sustainable, smart, and economical approach. Using AI/ML-powered smart systems, such as 3D-printed, real-time optical density monitoring instruments and an Internet of Things (IoT) enabled by smartphones, could help microalgal processes make better decisions.