The Canadian Journal of Chemical Engineering’s final issue of the year, the December issue, is now available. It features seventeen new articles, including four from our Environment, Renewable Resources and Green Processes subject area.

The first issue highlight, “Design and fabrication of non‐noble metal catalyst‐based air‐cathodes for metal‐air battery,” features research out of the National Research Council Canada and Shanghai University (China). In this article, the authors describe their work on the development and creation of a new method for forming the air-cathode, which decreases the cost of materials and the manufacturing process. This air-cathode can then be used to create a metal-air battery. Their research demonstrates the effectiveness of this method and it is a promising method for a continuous industrial manufacturing process.

In “Simultaneous fault detection and isolation using semi‐supervised kernel nonnegative matrix factorization,” authors from Northeastern University (China) develop a novel nonlinear process monitoring approach, which was the second issue highlight. They use a simultaneous fault detection and isolation approach, which they developed with the aid of a semi‐supervised kernel nonnegative matrix factorization algorithm. The algorithm allows them to leverage both labelled and unlabelled samples to facilitate high algorithm performance. They tested this approach with numerical and penicillin fermentation process case studies, which demonstrate that their approach outperforms existing methods. 

Researchers from Nanchang University (China) and the Athlone Institute of Technology (Ireland) published an issue highlights article, “Chemical process fault diagnosis based on enchanted machine-learning approach” in this month’s issue of Can. J. Chem. Eng. Their research takes on the complex task of fault diagnosis in the chemical industry by using machine-learning. They used support vector machine recursive feature elimination to minimize redundancies, and then applied the trained probabilistic neural network for fault diagnosis. The modified bat algorithm optimized the parameter smooth factor in the network, allowing for a better classification effect. The researchers tested their model using the Tennessee Eastman process data set and found that their combination method allows for a significant improvement in accuracy.

The final issue highlight features work from the Beijing University of Chemical Technology (China) and Tsinghua University (China). The article, “Effective separation of aromatic hydrocarbons by pyridine-based deep eutectic solvents,” demonstrates how the authors designed and synthesized pyridine-based deep eutectic solvents with n-ethylpyridinium bromide and two HBDs (n-formyl morpholine and levulinic acid). They studied two ternary systems with liquid-liquid extraction, and equilibrium was possible in ten minutes. The authors then used the NRTL model to check their experimental LLE data and demonstrated that they correlated well.