The present invention relates to a method for preparing carbon tetrafluoride by means of photocatalysis. The method comprises: 1) filling a reaction tube nest of a shell and tube reactor with a TO-PTC catalyst, wherein the reaction tube nest is made of a transparent material; (2) raising the temperature to preheat the shell and tube reactor to a reaction temperature; and (3) introducing a reaction gas into the shell and tube reactor, and controlling the flow of the reaction gas, so as to carry out a catalytic fluorination reaction, wherein illumination is applied to the shell and tube reactor at any stage from step 1) to step 3) to activate the photocatalytic activity of the TO-PTC catalyst, and illumination is maintained throughout the whole reaction process of step 3). By means of a direct catalytic fluorination method, the direct preparation of tetrafluoromethane can be achieved, the outflow of intermediates and impurities is reduced, the acquisition rate of the target product is significantly increased, actual levels of preparation difficulty, energy consumption during preparation, etc., are reduced, and both the utilization rate of materials and the fluorination efficiency of methane can be significantly increased.
The present invention belongs to the field of catalysts, and particularly relates to a photocatalyst for methane fluorination. The catalyst is a titanium-based titanium peroxide complex particle material, and has catalytic activity towards methane under the illumination conditions of a wavelength of 390-450 nm, and a microscopic morphology shown as a spherical or near-spherical granular structure, with a villi-shaped nanostructure and/or a quantum dot nanostructure growing on the surface of the granular structure. The catalyst of the present invention can achieve an effective catalytic promotion effect for the perfluorination of methane, such that tetrafluoromethane can be directly and effectively prepared, the actual preparation difficulty, preparation energy consumption, etc. are reduced, and the utilization rate of materials and the methane fluorination efficiency can both be significantly improved.
B01J 21/06 - Silicium, titane, zirconium ou hafniumLeurs oxydes ou hydroxydes
B01J 31/16 - Catalyseurs contenant des hydrures, des complexes de coordination ou des composés organiques contenant des complexes de coordination
B01J 35/10 - Catalyseurs caractérisés par leur forme ou leurs propriétés physiques, en général solides caractérisés par leurs propriétés de surface ou leur porosité
C07C 17/10 - Préparation d'hydrocarbures halogénés par remplacement par des halogènes d'atomes d'hydrogène
C07C 19/08 - Composés acycliques saturés contenant des atomes d'halogène contenant du fluor
3.
PRODUCTION MANAGEMENT CONTROL SYSTEM FOR PREPARATION OF ELECTRONIC GRADE HEXAFLUOROBUTADIENE
The present application relates to the field of intelligent production, and specifically discloses a production management control system for the preparation of electronic grade hexafluorobutadiene. On the basis of an artificial intelligence control technology, a deep neural network model is used to respectively extract dynamic implicit association feature information of the dropping rate of diiodooctafluorobutane and the dropping rate of a Grignard reagent, as well as the column reactor temperature, the column top cooling temperature, and the heating power of a thermocouple in temporal dimension, and a heating power value of the thermocouple at a current time point is comprehensively adjusted by using the feature information, so that the production of electronic grade hexafluorobutadiene can be effectively managed to improve the preparation efficiency and the energy utilization rate.
A quality inspection system (100) and method for electronic-grade hexafluorobutadiene. The method specifically comprises: first acquiring, by means of a gas chromatograph (T), a gas chromatogram of electronic-grade hexafluorobutadiene (H) to be subjected to measurement (S110); then applying a first convolutional neural network model that includes a saliency detector to the gas chromatogram, so as to obtain a gas-chromatography feature map (S120); next, applying a residual dual-attention mechanism model to the gas-chromatography feature map, so as to obtain an enhanced gas-chromatography feature map to serve as a decoded feature map (S130); and finally, applying a decoder to the decoded feature map, so as to obtain a decoded value, wherein the decoded value is the water content of said electronic-grade hexafluorobutadiene (S140). In this way, the water content of electronic-grade hexafluorobutadiene can be accurately measured in an intelligent manner, such that the product quality of the electronic-grade hexafluorobutadiene is ensured.
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
The present invention relates to a preparation method for hexafluorobutadiene. The method comprises: 1) performing temperature raising and pressure increase reactions on chlorotrifluoroethylene, and then performing a thermal polymerization reaction on same to obtain a precursor; 2) performing fractional distillation on the precursor and obtaining a low-boiling-point intermediate, and recovering a residual solution; 3) adding the intermediate into ethanol, performing a reflux reaction under a zinc catalyst to collect a gas product, and then mixing the collected gas product with hydrogen iodide, performing high-temperature ring opening, and performing condensation recovery to obtain a condensate solution; and 4) mixing the condensate solution with the residual solution in step 2), reacting with chlorine water under photocatalysis, adding the mixture into diethylene glycol monobutyl ether, performing a heating reaction under a zinc catalyst, performing condensation reflux to remove impurities, and enabling the gas product to pass through a high-temperature porous solid catalyst to obtain hexafluorobutadiene. The preparation process can increase the yield of hexafluorobutadiene to at least 85%, and greatly improves the preparation effect.
C07C 17/23 - Préparation d'hydrocarbures halogénés par déshalogénation
C07C 17/357 - Préparation d'hydrocarbures halogénés par des réactions n'influençant pas le nombre d'atomes de carbone ou d'halogène dans les molécules par déshydrogénation
C07C 19/10 - Composés acycliques saturés contenant des atomes d'halogène contenant du fluor et du chlore
C07C 17/04 - Préparation d'hydrocarbures halogénés par addition d'halogènes à des hydrocarbures halogénés non saturés
C07C 19/08 - Composés acycliques saturés contenant des atomes d'halogène contenant du fluor
C07C 17/354 - Préparation d'hydrocarbures halogénés par des réactions n'influençant pas le nombre d'atomes de carbone ou d'halogène dans les molécules par hydrogénation
C07C 23/06 - Hydrocarbures halogénés monocycliques à cycle à quatre chaînons
C07C 17/281 - Préparation d'hydrocarbures halogénés par des réactions comportant un accroissement du nombre des atomes de carbone dans le squelette par des réactions d'addition d'hydrocarbures halogénés uniquement d'un seul composé
C07C 17/383 - SéparationPurificationStabilisationEmploi d'additifs par distillation
B01J 35/10 - Catalyseurs caractérisés par leur forme ou leurs propriétés physiques, en général solides caractérisés par leurs propriétés de surface ou leur porosité
6.
GAS MONITORING SYSTEM FOR HEXAFLUOROBUTADIENE STORAGE PLACE AND MONITORING METHOD THEREOF
A gas monitoring system for a hexafluorobutadiene storage place and a monitoring method thereof, relating to the field of intelligent gas monitoring. Deep mining is performed on gas concentration values at a plurality of predetermined time points respectively from the aspects of local correlation features and global correlation features by means of a deep learning-based convolutional neural network model, and on the basis of fusing the two types of features, a self-attention-based data-intensive loss function is introduced to train a deep neural network model architecture, so as to improve the parameter adaptive variability of a measurement data local correlation feature matrix and a measurement data global correlation feature matrix for a classification objective function by means of the adaptive dependence of the local correlation features and the global correlation features on different data-intensive objects, thereby improving the classification accuracy of the fused classification feature matrix. In this way, the concentration of hexafluorobutadiene gas in the storage place can be accurately monitored.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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