This paper reviews the integration of artificial intelligence (AI) and machine learning in biorefineries and bioprocessing, with applications in biocatalysis, enzyme optimization, real-time monitoring, and quality assurance. AI contributes to predictive modeling and allows the precise forecasting of process outcomes, resource management, and energy utilization. AI models, including supervised, unsupervised, and reinforcement learning, support improvements in important bioprocess stages, such as fermentation, purification, and microbial biosynthesis. Digital twins and soft-sensing technologies enable real-time control and increase operational precision in complex bioprocess environments. Hybrid modeling integrates data-driven AI techniques with common scientific principles, improving scalability and adaptability under dynamic operational conditions. This review addresses challenges in AI implementation, such as data standardization, model transparency, and the need for interdisciplinary collaboration. The discussion concludes with future directions and sustainable AI strategies, highlighting the potential of AI to strengthen scalable, efficient, and environmentally sustainable biorefinery operations. These findings highlight how AI-driven methodologies improve operational efficiency, reduce resource waste, and facilitate sustainable innovation in bioprocesses, thereby strengthening sustainability within the bioeconomy.

This paper compares the annual performance of Integrated Solar Combined Cycles (ISCCs) using different solar concentration technologies: parabolic trough collectors (PTC), linear Fresnel reflectors (LFR) and central tower receiver (CT). Each solar technology (i.e. PTC, LFR and CT) is proposed to integrate solar energy into the combined cycle in two different ways. The first one is based on the use of solar energy to evaporate water of the steam cycle by means of direct steam generation (DSG), increasing the steam production of the high pressure level of the steam generator. The other one is based on the use of solar energy to preheat the pressurized air at the exit of the gas turbine compressor before it is introduced in the combustion chamber, reducing the fuel consumption. Results show that ISCC with DSG increases the yearly production while solar air heating reduces it due to the incremental pressure drop. However, air heating allows significantly higher solar-to-electricity efficiencies and lower heat rates. Regarding the solar technologies, PTC provides the best thermal results.