Dalian University of Technology

China Petroleum Chemical Corporation

Fushun Research Institute of Petroleum and Petrochemicals, Sinopec Corp.

China Petroleum & Chemical Corporation

Sinopec Dalian Research Institute of Petroleum and Petrochemicals Co., Ltd.

Wei Wang

Yang Liu

Wei Zhao

Qian Xue

Jun Zhao

Linqing Wang

Feng Jin

Zhongyang Han

Fan Zhou

Quanjie Liu

Huiqing Xu

Liming Jia

Xingxing Gao

Long Chen

Guanghui Yang

Zunzhao Li

Mingrui Liu

Xiaozhe Sun

Xiaolin Wang

Shihan Li

A disturbance device has two jet mixers, which are oppositely disposed in the horizontal direction; a mixing chamber, which is connected between the two jet mixers; and mixing pipes, which are connected below the mixing chamber. The mixing pipes comprise: a central pipe, which is a vertical straight pipe; multiple helical pipes, which are wound in multiple layers and provided outside the central pipe, the diameters of the multiple helical pipes gradually increasing from the inner to outer layers, and multiple flow deflector assemblies being provided at intervals in each helical pipe; and an outer sleeve, which is a straight pipe, the outer sleeve being sleeved outside the outermost helical pipe. A continuous gas separation system combines a hydrate-based process and a reverse osmosis process, using the disturbance device, enables continuous gas separation.

Continuous gas separation system combining hydrate-based process and reverse osmosis process and disturbance device

The present disclosure discloses a method for predicting oxygen load in iron and steel enterprises based on production plan, which relates to influencing factor extraction, neural network modeling and similar sequence matching technologies. The method uses the actual industrial operation data to first extract the relevant data such as the production plan and production performance of converter steel-making, analyze the influencing factors, and extract the main influencing variables of oxygen consumption. Then, the neural network prediction model of oxygen consumption of a single converter is established, the mean square error is taken as the evaluation index, and the predicting result of time granularity of a converter in the blowing stage is given. Finally, in combination with the information of smelting time and smelting duration of each device in the converter production plan, the prediction value of oxygen load in a planned time period is given.

Method for predicting oxygen load in iron and steel enterprises based on production plan

The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.

Method for construction of long-term prediction intervals and its structural learning for gaseous system in steel industry

A distributed industrial energy operation optimization platform which is capable of automatically constructing intelligent models and algorithms, is divided into three parts: a modeling terminal, a background service and a human-computer interface. The models like data pre-processing, energy generation-consumption-storage trend forecasting and optimal scheduling decision models are encapsulated in the modeling terminal as different visualization modules facing with multiple categories production scenarios, by dragging which the complex functional models can be realized conveniently. The background service is capable of automatically constructing the training samples and the production plans/manufacturing signals series according to the device model requirements of each edge side, interacts with the trained intelligent models through corresponding interfaces, and the computing results are saved in the specified relational database. The computing results are displayed through a friendly customer human-computer interface, and the real-time state of current working condition can also be adjusted.

Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms

The present invention provides a method for an optimal scheduling decision of an air compressor group based on a simulation technology, which belongs to the technical field of information. The present invention uses expert experience to construct an air compressor energy consumption model sample set, and applies a least squares algorithm to learn relevant parameters of an air compressor energy consumption model; uses maximum energy conversion efficiency and minimum economic cost based on an equivalent electricity as target functions, and applies the simulation technology and a depth first tree search algorithm to solve a multi-target optimal scheduling model of the air compressor group; and finally uses a fuzzy logic theory to describe the preferences of decision makers, and introduces the decision maker preference information into interactive decision making, thereby assisting production staff to formulate safe, economical, efficient and environmentally friendly operation schemes to achieve an operation mode of maximum resource utilization of the air compressor group. The method also has wide application value in different industrial fields.

Method for optimal scheduling decision of air compressor group based on simulation technology

The present disclosure belongs to the technical field of information, provides a short-term interval prediction method for photovoltaic power output, and is a short-term interval prediction method for photovoltaic power output based on a combination of a multi-objective optimization algorithm and a least square support vector machine. The present disclosure firstly proposes a similar day classification method considering both numerical value and pattern similarity to enhance the regularity of samples, then constructs an adaptive proportional interval estimation model based on dual-LSSVM model, and optimizes model parameters by using NSGA-II algorithms to realize the interval prediction of photovoltaic power output. Results obtained by the method have high accuracy, and computation efficiency meets actual application requirements. The method can also be popularized and applied in the fields of grid connection and scheduling of renewable energy sources.

Short-term interval prediction method for photovoltaic power output

The present invention provides a modified composite molecular sieve, and a preparation method and an application of the modified composite molecular sieve. The modified composite molecular sieve comprises SiOand a composite molecular sieve that comprises molecular sieve MCM-22 and crystalline molecular sieve selected from at least one of ZSM-22, ZSM-23 and ZSM-48, wherein, the molecular sieve MCM-22 covers around the crystalline molecular sieve. The present invention further provides a catalyst and an application of the catalyst. The catalyst comprises a carrier and a noble metal loaded on the carrier, wherein, the carrier comprises a modified composite molecular sieve that is the modified composite molecular sieve provided in the present invention or the modified composite molecular sieve obtained with the method provided in the present invention. The catalyst that utilizes the composite molecular sieve as a carrier not only can decrease the solidifying point of waxy raw oil, but also can improve the yield of liquid product, is especially applicable to the isomerization dewaxing process of lube distillate, and has an advantage of remarkably improving the viscosity index of lube base oil.

Modified composite molecular sieve and preparation method thereof, and paraffin isomerization catalyst

Growing community of inventors

Liaoning, China

Wei Wang