Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries

In view of the global climate change concerns, the society is approaching towards the development of 'green' and renewable energies for sustainable future. The non-renewable fossil fuels may be largely replaced by renewable energy sources, which could facilitate sustainable growth, energy development and lessen the reliance on conventional energy sources. The traditional methods employed in biorefineries to estimate the data values for the biofuel production systems are often complicated, time-consuming and labour-intensive. Modern machine learning (ML) technologies hold enormous potential in managing high-dimensional complex scientific tasks and improving decision-making in energy distribution networks and systems. The data-driven probabilistic ML algorithms could be applied to smart biofuel systems and networks that may reduce the cost of experimental research while providing accurate estimates of product yields. The current review demonstrates a thorough understanding of the application of different ML models to regulate and monitor the production of biofuels from waste biomass through prediction, optimization and real-time monitoring. The in-depth analysis of the most recent advancements in ML-assisted biofuel production methods, including thermochemical and biochemical processes is provided. Moreover, the ML models in addressing the issues of biofuel supply chains, case studies, scientific challenges and future direction in ML applications are also summarized.

Automated summary

In response to global climate change concerns, the society is striving towards the development of renewable and sustainable energies. Machine learning technologies show great potential in managing complex scientific tasks and improving decision-making in energy distribution networks. By applying data-driven probabilistic machine learning algorithms, accurate estimates of biofuel product yields can be obtained, reducing the cost of experimental research. This review provides a comprehensive understanding of the application of different machine learning models in regulating and monitoring biofuel production from waste biomass.