The production of C_(2)H_(3)Cl from CH_(3)Cl(MCTV)represents a promising non-petroleum route for synthesizing C_(2)alkenes from C_(1)molecules.Exploration of new MCTV catalysts is crucial for advancing sustainable che...The production of C_(2)H_(3)Cl from CH_(3)Cl(MCTV)represents a promising non-petroleum route for synthesizing C_(2)alkenes from C_(1)molecules.Exploration of new MCTV catalysts is crucial for advancing sustainable chemical production.In this study,we present NaVO_(3)as a surface-confined coupling center for·CH_(2)Cl radicals,demonstrating its superior performance in the selective coupling of methyl chloride to synthesize vinyl chloride.By incorporating NaVO_(3)onto the surface of CeO_(2),the catalyst enables effective capture of·CH_(2)Cl radicals during the CH_(3)Cl oxidative pyrolysis and their subsequent conversion into C_(2)H_(3)Cl.We experimentally validate the capability of highly dispersed Na-VO_(3)to controllably couple·CH_(2)Cl radicals through in-situ synchrotron-based vacuum ultraviolet photoionization mass spectrometry.The results demonstrate that the dispersion of NaVO_(3)on the catalyst surface has a considerable impact on the reaction efficiency of·CH_(2)Cl radicals and the overall MCTV performance.This discovery holds substantial implications for the controlled C_(1)radical transformation and provides a guidance for the design of catalysts for sustainable production of C_(2)H_(3)Cl.展开更多
The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture ...The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture time series and modal features,which leads to prediction hysteresis and poor prediction stabil-ity.Aiming at the above problems,this paper proposes a time-series and modal fea-tureenhancementmethodbasedonadual-stageself-attentionmechanism(DATT),and a time series prediction method based on a gated feedforward recurrent unit(GFRU).On this basis,the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented.Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved.Compared with the traditional prediction model,the DATT-GFRU neural network has a smaller aver-age error of model prediction results,stable prediction performance,and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.展开更多
文摘The production of C_(2)H_(3)Cl from CH_(3)Cl(MCTV)represents a promising non-petroleum route for synthesizing C_(2)alkenes from C_(1)molecules.Exploration of new MCTV catalysts is crucial for advancing sustainable chemical production.In this study,we present NaVO_(3)as a surface-confined coupling center for·CH_(2)Cl radicals,demonstrating its superior performance in the selective coupling of methyl chloride to synthesize vinyl chloride.By incorporating NaVO_(3)onto the surface of CeO_(2),the catalyst enables effective capture of·CH_(2)Cl radicals during the CH_(3)Cl oxidative pyrolysis and their subsequent conversion into C_(2)H_(3)Cl.We experimentally validate the capability of highly dispersed Na-VO_(3)to controllably couple·CH_(2)Cl radicals through in-situ synchrotron-based vacuum ultraviolet photoionization mass spectrometry.The results demonstrate that the dispersion of NaVO_(3)on the catalyst surface has a considerable impact on the reaction efficiency of·CH_(2)Cl radicals and the overall MCTV performance.This discovery holds substantial implications for the controlled C_(1)radical transformation and provides a guidance for the design of catalysts for sustainable production of C_(2)H_(3)Cl.
基金This work is financially supported by:The National Key R&D Program of China(No.2020YFB1712600)The Fundamental Research Funds for Central University(No.3072022QBZ0601)+1 种基金The National Natural Science Foundation of China(No.62272126)The National Natural Science Foundation of China(No.61872104).
文摘The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture time series and modal features,which leads to prediction hysteresis and poor prediction stabil-ity.Aiming at the above problems,this paper proposes a time-series and modal fea-tureenhancementmethodbasedonadual-stageself-attentionmechanism(DATT),and a time series prediction method based on a gated feedforward recurrent unit(GFRU).On this basis,the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented.Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved.Compared with the traditional prediction model,the DATT-GFRU neural network has a smaller aver-age error of model prediction results,stable prediction performance,and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.
