Dalian University of Technology

China Petroleum Chemical Corporation

Fushun Research Institute of Petroleum and Petrochemicals, Sinopec Corp.

Shenyang Sinochem Agrochemicals R&d Co., Ltd.

Dalian Research Institute of Petroleum and Petrochemicals, Sinopec Corp.

Jiangsu Yangnong Chemical Co., Ltd.

Fushun Research Institute of Petroleum and Petrochemicals, Inopec Corp.

Bo Qin

Kuo Liu

Yongqing Wang

Wei Liu

Fenglai Wang

Yanze Du

Mingrui Shen

Haibo Liu

Minghua Guan

Mengmeng Niu

Lingsheng Han

Hang Gao

Renjie Huang

Gang Wang

Mingxin Wang

Lin Chen

Shuang Liang

Fan Zhang

Bin Li

Bing Jiang Sun

Huibin Yang

Dongliang Cui

Xu Li

Haining Liu

Xiwen Zhang

Jiakun Wu

Junwu Ying

Yongquan Gan

Hongjuan Ma

Zhizhi Zhang

Wei Han

Yan Cheng

Zhengmao Lu

Heying Pei

Honghui Wang

Zaiyou Ban

XiaoPing Zhang

Xiaoping Zhang

Bing Sun

Zheng Wang

Chang Liu

An alkene-containing amide compound of formula (I) and agriculturally acceptable salts thereof can be used as herbicides.

Alkene-containing amide compound and application thereof

An on line prediction method of part surface roughness based on SDAE-DBN algorithm. The tri-axis acceleration sensor is adsorbed on the rear bearing of the machine tool spindle through the magnetic seat to collect the vibration signals of the cutting process, and a microphone is placed in the left front of the processed part to collect the noise signals of the cutting process of the machine tool; the trend term of dynamic signal is eliminated, and the signal is smoothed; a stacked denoising autoencoder is constructed, and the greedy algorithm is used to train the network, and the extracted features are used as the input of deep belief network to train the network; the real-time vibration and noise signals in the machining process are input into the deep network after data processing, and the current surface roughness is set as output by the network.

On-line prediction method of surface roughness of parts based on SDAE-DBN algorithm

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

Prediction method of part surface roughness and tool wear based on multi-task learning

A toolholder matched with the internal jet cooling spindle for cryogenic coolant is mainly composed of a hollow toolholder body, a high-performance thermal insulation structure and a bidirectional sealing structure. They can guide the cryogenic coolant from the spindle to the internal cooling channel of cutting tool and realize the cryogenic thermal insulation and dynamic sealing. The high-performance thermal insulation structure inside the toolholder employs the material with a low thermal conductivity and a low linear expansion coefficient to restrain the low temperature impact of cryogenic coolant on the toolholder and spindle, to ensure the dimensional accuracy and assembly accuracy of the toolholder. The bidirectional sealing structure in the toolholder uses the ultra-low temperature resistant seal rings to prevent the cryogenic coolant from leaking towards the spindle and the cutting tool, to ensure the stability of the coolant transport.

Toolholder matched with the internal jet cooling spindle for cryogenic coolant

A spindle thermal error compensation method which is insensitive to the disturbance of the cooling system is provided, belonging to the technical field of error compensation in numerical control machine tools. First, the spindle model coefficient identification test, based on multi-state speed variable, is performed; after which, based on the correlation analysis between temperature and thermal error, the temperature measurement point, significantly correlated with the axial thermal error of the spindle, is determined. Next, a spindle thermal error model is established, which is insensitive to the cooling system disturbance. In addition, the coefficients in the model are identified under constraint condition, according to the nonlinear quadratic programming algorithm. Finally, based on the OPC UA communication protocol, the compensation value, as calculated by the model, is input to the numerical control system, in order to realize the compensation of the spindle thermal error.

Spindle thermal error compensation method insensitive to cooling system disturbance

A self-adaptive compensation method for feed axis thermal error, which belongs to the field of error compensation in NC machine tools. First, based on laser interferometer and temperature sensor, the feed axis thermal error test is carried out; following, the thermal error prediction model, based on the feed axis thermal error mechanism, is established and the thermal characteristic parameters in the model are identified, based on the thermal error test data; next, the parameter identification test is carried out, under the preload state of the nut; next, the adaptive prediction model is established, based on the thermal error prediction model, while the parameters in the measurement model are identified; finally, adaptive compensation of thermal errors is performed, based on the adaptive error prediction model, according to the generated feed axis heat.

Self-adaptive compensation method for feed axis thermal error

A modified Y-type molecular sieve contains 0.5-2 wt. % of NaO based on the total amount of the modified Y-type molecular sieve. In the modified Y-type molecular sieve, the ratio between the total acid amount measured by pyridine and infrared spectrometry and total acid amount measured by n-butyl pyridine and infrared spectrometry is 1-1.2. The total acid amount measured by pyridine and infrared spectrometry of the modified Y-type molecular sieve is 0.1-1.2 mmol/g. The acid center sites of the molecular sieve of the modified Y-type molecular sieve are distributed in the large pore channels. The molecular sieve is used in the hydrocracking reaction process of a wax oil.

Modified Y-type molecular sieve and preparation method thereof, hydrocracking catalyst and preparation method thereof, and method for hydrocracking hydrocarbon oil

The present disclosure provides a hydrocracking catalyst, a method for preparing the same and a use of the same, and a method for hydrocracking catalytic diesel oil. The catalyst comprises a support, an active metal component, and carbon, wherein, based on the total weight of the catalyst, the content of the support is 60 to 90 wt %, the content of the active metal component calculated in metal oxides is 15 to 40 wt %, and the content of carbon calculated in C element is 1 to 5 wt %; measured with an infrared acidimetric estimation method, the acid properties of the hydrocracking catalyst are: the total infrared acid amount is 0.4 to 0.8 mmol/g, wherein, the infrared acid amount of strong acid with desorption temperature greater than 350&#xb0; C. is 0.08 mmol/g or lower, and the ratio of the total infrared acid amount to the infrared acid amount of strong acid with desorption temperature greater than 350&#xb0; C. is 5 to 50.

Hydrocracking catalyst, preparation method and use thereof, and method for hydrocracking catalytic diesel oil

The present invention discloses a modified Y molecular sieve, a preparation method and a use of the modified Y molecular sieve, a supported catalyst, and a hydrocracking method. The silica-alumina mole ratio in the surface layer of the modified Y molecular sieve is 20-100:1, and the silica-alumina mole ratio in the body phase of the modified Y molecular sieve is 8-30:1. When a hydrocracking catalyst prepared from the modified Y molecular sieve is used for hydrocracking, the hydrocracking catalyst has higher reactivity and higher nitrogen tolerance. The hydrocracking catalyst prepared from the modified Y molecular sieve is suitable for use for increasing the yield of diesel oil, increasing the yield of chemical materials, and catalyzed hydrogenation conversion of diesel oil, etc.

Modified Y molecular sieve and preparation method and use thereof, supported catalyst, and hydrocracking method

Disclosed are a novel NU-85 molecular sieve having a specific surface area ranging from about 405 m/g to about 470 m/g and a pore volume ranging from about 0.27 cm/g to about 0.35 cm/g, and processes for preparing the NU-85 molecular sieve.

Nu-85 molecular sieve having a large pore volume and processes for preparing the same

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Liaoning, China

Bo Qin