Various Cu/ZnO/Al2O3 catalysts have been synthesized by different aluminum emulsions as aluminum sources and their pertormances tor methanol synthesis from syngas have been investigated. The influences of preparation ...Various Cu/ZnO/Al2O3 catalysts have been synthesized by different aluminum emulsions as aluminum sources and their pertormances tor methanol synthesis from syngas have been investigated. The influences of preparation methods of aluminum emulsions on physicochemical and catalytic properties of catalysts were studied by XRD, SEM, XPS,N2 adsorption-desorption techniques and methanol synthesis from syngas. The preparation methods of aluminum emulsions were found to influence the catalytic activity, CuO crystallite size, surface area and Cu0 surface area and reduction process. The results show that the catalyst CN using the aluminum source prepared by addition the ammonia into the aluminum nitrate （NP） exhibited the best catalytic performance for methanol synthesis from syngas.展开更多
Wetting is one of the omnipresent phenomena governed via natural laws. Moreover, surface wettability at non-ambient temperature especially at high temperature (30°C to 90°C) is of great importance in many in...Wetting is one of the omnipresent phenomena governed via natural laws. Moreover, surface wettability at non-ambient temperature especially at high temperature (30°C to 90°C) is of great importance in many industrial processes. In this study, Si wafers with various structures were fabricated to investigate wettability at different temperatures. Three shapes with micro-pillar structured surfaces were designed and fabricated. Pillar-structured surfaces were fabricated by photolithography and ICP etching. The temperature-dependent wettability of single-phase regime droplets was characterized using contact angle measurements. The wetting behavior of a water droplet was observed.展开更多
Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of t...Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.展开更多
文摘Various Cu/ZnO/Al2O3 catalysts have been synthesized by different aluminum emulsions as aluminum sources and their pertormances tor methanol synthesis from syngas have been investigated. The influences of preparation methods of aluminum emulsions on physicochemical and catalytic properties of catalysts were studied by XRD, SEM, XPS,N2 adsorption-desorption techniques and methanol synthesis from syngas. The preparation methods of aluminum emulsions were found to influence the catalytic activity, CuO crystallite size, surface area and Cu0 surface area and reduction process. The results show that the catalyst CN using the aluminum source prepared by addition the ammonia into the aluminum nitrate （NP） exhibited the best catalytic performance for methanol synthesis from syngas.
文摘Wetting is one of the omnipresent phenomena governed via natural laws. Moreover, surface wettability at non-ambient temperature especially at high temperature (30°C to 90°C) is of great importance in many industrial processes. In this study, Si wafers with various structures were fabricated to investigate wettability at different temperatures. Three shapes with micro-pillar structured surfaces were designed and fabricated. Pillar-structured surfaces were fabricated by photolithography and ICP etching. The temperature-dependent wettability of single-phase regime droplets was characterized using contact angle measurements. The wetting behavior of a water droplet was observed.
基金supports of Special Foundation for State Major Basic Research Program of China(Grant No.2021YFD2101000).
文摘Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.
