On the Cover
Given the complexity and intricate nature of biofuel and bioproduct production systems, the demand for swift and precise modeling tools is evident to design, optimize, monitor, and control these systems. Machine learning holds immense potential to revolutionize the biofuel and bioproduct industry by enhancing processes, boosting efficiency, and fostering the emergence of sustainable solutions. By scrutinizing extensive datasets and detecting patterns, machine learning algorithms can fine-tune crucial parameters like feedstock selection, process conditions, and genetic engineering, leading to heightened yields and superior product quality. Predictive modeling and real-time monitoring further empower researchers to make well-informed decisions, curtail expenses, and optimize process control. Moreover, machine learning aids in identifying novel applications for waste streams, thus augmenting sustainability efforts. Overall, machine learning enables researchers to forge ahead in developing more efficient and sustainable approaches to biofuel and bioproduct production, propelling the world toward an eco-friendly and renewable future. In the June 2023 Issue of Biofuel Research Journal, the application of these techniques in producing biohydrogen (DOI: 10.18331/BRJ2023.10.2.4) and lignocellulosic ethanol (DOI: 10.18331/BRJ2023.10.2.5) was critically reviewed, and the future trends in this domain were presented. Cover art by BiofuelResJ. ©2023.