How could Artificial Intelligence be used to increase the potential of biorefineries in the near future? A review

Abstract

Innovation in digitalization and low-carbon technologies are leading the way for the production sector. In the context of the bioeconomy, a path is opening up for the integration of bio-based processes into the value chain as alternative production schemes to fossil fuel-based production models, although process modeling and optimization is needed as this approach is at an early stage of design and development. The large number of variables in the biorefinery cascade scheme presents the inherent difficulty of the optimization strategy, considering conditions that allow for higher productivity and revenues in parallel with lower environmental burdens. The implementation of artificial intelligence (AI) through techniques such as machine learning and predictive modeling could be considered as an efficient tool for process optimization. Such techniques require large amounts of historical data to identify effects, synergies and clusters of parameters; detect production anomalies; develop models for predictive, prescriptive or root cause analysis; and provide autonomous control.

In this sense, this critical review report aims to provide an overview of available reports that have considered AI for the evaluation of biorefinery production models, identifying its potentialities to enable better production strategies under the principles of sustainability and circular economy. This review aims to be useful for the development of further research on the implementation of digitalization of biorefinery processes. This work aims to identify the forefront innovations in the development of bio-based processes that meet efficiency and sustainability criteria in order to provide information of interest to policy makers, stakeholders and industry professionals