Due to its cost effectiveness and eco-friendliness, oxydehydration of glycerol is currently attracting considerable attentions. In an attempt to develop efficient catalyst for the reaction, tungsten incorporated molybdenum vanadium mixed oxide (MoVW) catalysts were designed based on computational calculations and mechanistic insights. By incorporating tungsten into molybdenum vanadium mixed oxide structure, the catalysts are active and selective not only for the dehydration of glycerol but also the subsequent oxidation of acrolein to acrylic acid. Through DFT calculations, we confirmed that tungsten species induced change in electron density of neighboring atoms, which leads to selective production of acrylic acid. Structural characterization demonstrates that the structure of such MoVW catalysts is similar to that of DFT models. The incorporated tungsten species enhanced the acid and redox properties of the catalyst, leading to high selectivity for acrylic acid (30.5%). It not only induced but also stabilized the reduced oxidation states of molybdenum and vanadium atoms, as confirmed by XPS and DFT calculations. Hence, a stable and selective production of acrylic acid was achieved with full glycerol conversion for 110 h. MoVW catalytic system with an additional acid catalyst bed exhibited remarkable selectivity for acrylic acid (47.2%), suggesting its potential for practical applications.

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Hierarchical porous catalysts have recently attracted increasing interest because of the enhanced accessibility to active sites on such materials. In this context, previously reported hierarchically mesoporous ASN and ASPN materials are evaluated by applying them to the dehydration of glycerol, and demonstrate excellent catalytic performance. In addition, a comprehensive understanding of the effects of pore structures and the acid properties on the reaction through comparative studies with microporous HZSM-5 and mesoporous AlMCM-41 is provided.

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Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.

A facile and efficient approach to prepare hierarchically and radially mesoporous nano-catalysts with tunable acidic properties has been successfully developed. The nanospheres show excellent catalytic performance for the acid catalysed reactions, i.e. cracking of 1,3,5-triisopropylbenzene and hydrolysis of sucrose.

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