Journal of Energy Science and Technology is an international, peer-reviewed open access journal dedicated to advancing research the field of energy science and technology. The journal provides a rapid publication process to ensure wide dissemination of high-quality articles to scientists, professionals, and interested individuals worldwide. Our goal is to serve as an efficient, reliable, and trusted platform for scholars and readers, publishing cutting-edge research in the field.
Abstract: Direct methanol fuel cells (DMFCs) face durability challenges that limit their widespread adoption. Traditional data-driven degradation models excel in short-term predictions but lack capability for long-term forecasting. To mitigate disturbances caused by the voltage rebound phenomenon in fuel cells, this paper proposes a hybrid prediction model that combines mechanistic modeling and machine learning. The core idea is to use mechanistic modeling to extract degradation-relevant internal impedance features from electrochemical impedance spectroscopy (EIS), and then leverage machine learning to predict the evolution of these features, thereby avoiding direct fitting of noisy voltage signals. Specifically, the model first fits EIS using a full-circuit equivalent circuit model (ECM) to extract key internal degradation parameters. It then combines these parameters with a convolutional neural network (CNN) to predict impedance parameter trends. Finally, by establishing a linear relationship between the predicted impedance and the output voltage, the remaining useful life (RUL) of the methanol fuel cell is reliably estimated. Comparison results between the hybrid prediction model, Gated Recurrent Unit (GRU) network, and iTransformer network show predicted failure threshold time errors of 1 hour, 11 hours, and 5 hours, respectively. These results demonstrate that the proposed hybrid model achieves the smallest time prediction error (1 hour), indicating superior performance for long-term prediction tasks. The hybrid model improves data tracking ability and prediction accuracy, and reduces dependence on large amounts of degradation data. This model provides a feasible solution for DMFC lifetime prediction.Abstract: Direct methanol fuel cells (DMFCs) face durability challenges that limit their widespread adoption. Traditional data-driven degradation models excel in short-term predictions but lack capability for long-term forecasting. To mitigate disturbances caused by the voltage rebound phenomenon in fuel cells, this paper proposes a hybrid prediction model t...Learn More
Abstract: Bio-isobutanol has emerged as an attractive next-generation biofuel and platform chemical due to its high energy density, low hygroscopicity, and compatibility with existing fuel infrastructures. Microbial production of isobutanol via engineered metabolic pathways has advanced significantly over the past decade; however, downstream separation remains one of the major bottlenecks limiting industrial implementation. The relatively low titers in fermentation broths (typically 5–30 g L⁻1), the presence of water-rich matrices, and the formation of azeotropes render conventional separation energy-intensive. Consequently, the development of efficient and cost-effective recovery technologies is critical for the commercialization of bio-isobutanol processes. This review provides a comprehensive overview of downstream separation strategies for bio-isobutanol recovery from fermentation broths. Key techniques including distillation, gas stripping, adsorption, liquid–liquid extraction, pervaporation, salting-out extraction, and hybrid integrated processes are discussed in terms of separation mechanisms, operational performance, and process integration potential. Particular emphasis is placed on emerging intensified approaches such as membrane-assisted extraction, in situ product removal, and hybrid separations combining thermodynamic and kinetic selectivity. Finally, the challenges and future perspectives toward energy-efficient and scalable bio-isobutanol separation technologies are discussed.Abstract: Bio-isobutanol has emerged as an attractive next-generation biofuel and platform chemical due to its high energy density, low hygroscopicity, and compatibility with existing fuel infrastructures. Microbial production of isobutanol via engineered metabolic pathways has advanced significantly over the past decade; however, downstream separation remai...Learn More
