Design of dual-motor driving control system of belt conveyor

In view of problems of low efficiency, difficult to guarantee speed synchronization and power balance of double-motor for that traditional dual-motor driving control system of belt conveyor adopts drive method of hydraulic coupler or liquid-viscosity driving device, a design scheme of dual-motor driving control system of belt conveyor based on direct torque control strategy was put forward. The system adopts one rectifier and two contravariant combined converter to drive the belt conveyor with master-slave control mode, and the master inverter adjusts output torque according to rated output power ratio of the two motor, so as to the system can run stably with a given speed. The simulation results show that the system realizes synchronous control of speed and torque of the double motor of belt conveyor, and implements power balance with good dynamic and static performance

Design of 3D visualization management platform for intelligent coal preparation plant based on coal preparation information model

At present, in the intelligent construction of coal preparation plant, there is little research and development on 3D visualization management centering on the whole life cycle from design, construction to production and operation and maintenance. There is also little research on the deep integration of 3D visualization and big data cloud platform. In view of the above problems, based on the building information model (BIM), this paper improves it and puts forward coal preparation information model (CPIM). The proposed model aims at the problem that BIM only considers the information model of coal preparation buildings and structures. BIM can not meet the needs of intelligent coal preparation plant construction. Combined with the big data cloud platform technology, a 3D visualization management platform for intelligent coal preparation plant based on CPIM (the platform consists of infrastructure layer, data sources layer, basic platform, application layer and business display layer) is designed. The data standard of coal washing engineering is constructed. The data access, management, storage, analysis and sharing of the whole life cycle of coal preparation design, construction, production and operation and maintenance are realized. The key technologies of the platform implementation of BIM lightweight engine, CPIM big data sub-platform construction, CPIM whole life cycle application, standard formulation of digital construction of coal washing project and software system independent research and development and domestic hardware adaptation are analyzed. Taking a coal preparation plant of China Coal Group as the research object, based on CPIM big data sub-platform and BIM 3D engine sub-platform, the CPIM whole life cycle 3D visualization application platform is constructed. And the localization adaptation of hardware is realized. The field application shows that the platform can carry out effective data acquisition, unified processing, storage, analysis and application for the design, construction and operation of coal preparation plant. The platform opens up the data channel of the coal washing industry. The platform realizes the 3D visualization management covering the whole life cycle of coal preparation plant design, construction, and production operation and maintenance

Dynamic evaluation of support quality of hydraulic support in space-time region

The support process of hydraulic support is a dynamic change process in time and space regions. At present, the evaluation of support quality of hydraulic support mostly focuses on the static characteristics of support. There is little research on the dynamic change of support column pressure. In order to solve the above problems, a dynamic evaluation model of support quality of hydraulic support in the space-time region based on improved LeNet-5 network is established by using the deep learning method. Firstly, the column pressure data of hydraulic support in working face is preprocessed (missing value filling, abnormal value processing, screening, sorting, etc.) to obtain more complete pressure data hydraulic support. Secondly, the preprocessed column pressure data of the hydraulic support is arranged according to time and space. The important characteristics are extracted such as initial setting force, circulating end-resistance, time-weighted resistance, and spatial distribution condition of resistance, which reflect the support condition of the hydraulic support in intelligent mining working face. The pressure time-sequence and the pressure space-sequence are combined into the total space-time pressure matrix in 2D space-time region. Thirdly, according to the support requirements of the working face, the support quality of the space-time region into seven types, namely, support quality preliminary deterioration, support quality continuous deterioration, support quality deep deterioration, support quality general maintenance, support quality preliminary optimization, support quality continuous optimization and support quality good maintenance. On the total space-time pressure matrix, the sliding window is used to intercept the sub-matrix with the given size at a certain interval. The sub-matrix is one-to-one corresponding to the seven support quality types in space-time regions to form samples and labels. Finally, the samples and the labels are input into the improved LeNet-5 network for training. The dynamic evaluation model of support quality of hydraulic support in space-time region is constructed, which evalues the support condition of hydraulic support in the region in real-time. The experimental results show that the model based on improved LeNet-5 network can be used to identify the dynamic effect of support quality in the working face, and provide the basis for the field operators to adjust the support state of the hydraulic support pertinently. The classification accuracy is 85.25%, which is 12% higher than that of the model based on LeNet-5 network. At the same time, the improved LeNet-5 network can converge to the optimal solution quickly in the training process, which accelerates the training speed of the network. The result verifies the advantages of the improved LeNet-5 network in evaluation of the support quality of hydraulic support in space-time region of intelligent working face

Manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face

The intelligent working face has abnormal working conditions such as lost support, uneven straightness, and support skew after the automatic following of hydraulic support. Therefore, manual regulation and control are needed. At present, the research lacks the knowledge discovery of manual regulation and control working conditions after the hydraulic support automation in the production process of the intelligent working face. This is not conducive for workers to quickly judge the number of hydraulic support requiring manual regulation and control. In order to solve the above problems, based on the identification of the number of hydraulic support that is not up to the standard after the hydraulic support automation, a manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face is put forward. Firstly, the historical data of the working face is analyzed. It is concluded that after the automatic following of the hydraulic support is finished, three characteristic values can be used as important characteristics for judging whether the hydraulic support carries out manual regulation and control after the automatic following of the hydraulic support. The characteristic values include the distance of the automatic following of the hydraulic support, the stroke variation of the pushing oil cylinder before and after the automatic following of the hydraulic support, and the absolute difference between the number of the hydraulic support at the position of the shearer and the number of the judged hydraulic support. According to the above conclusion, the structure of the manual control decision model after the hydraulic support cluster automation is proposed. The data acquisition module is used for providing the original data. The data preprocessing module prepares the original data by outlier processing, filtering, sorting and correlation analysis. The characteristic engineering module calculates and standardizes the above three characteristic values to provide a sample set for the classification model. After the classification model divides the sample set, the ID3 decision tree is used for classification. Finally, the number of hydraulic supports needing normal working conditions and the number of hydraulic supports nedeing manual control are output. The results of the model evaluation show that, compared with the traditional K-nearest neighbor (KNN), support vector machine (SVM), logical regression (LR) classification algorithms, the training set accuracy of the ID3 decision tree based classification model for the working conditions of hydraulic supports in the middle of the intelligent working face is 92.27%. The test set accuracy is 93.75%. The model can better distinguish the manual control hydraulic support number after automation

Switching control between variable frequency and power frequency of emulsion pump in coal mine

In view of problem of high impulse current when scheme of multiple pumps linkage with one frequency converter applied in coal mine emulsion pump station, asynchronous switching and synchronous switching control scheme between variable frequency and power frequency of emulsion pump were proposed. The field test results show that the synchronous switching control can avoid impulse current more effectively and truly realize free switching between variable frequency and power frequency

Research on stabilized pressure hydraulic fluid supply technology adapted to hydraulic support actio

In order to improve ability of hydraulic support following shearer in working face, coupling relationship among support action velocity, hydraulic system pressure and flow rate of hydraulic fluid supply was revealed by theory analysis of support hydraulic fluid supply process. Simulation model of hydraulic system on working face was established and correctness of the model was verified. Effect of hydraulic system pressure and flow rate on support action velocity was analyzed, and a stabilized pressure hydraulic fluid supply technology adapted to support action was proposed. The technology takes support action type and hydraulic system pressure limitation as criterion, and realizes slow and steady changing process of hydraulic system pressure with slight rise during support action by adjusting flow rate, so as to improve support action velocity. The technology was tested by use of multi-pump and multi-frequency hydraulic fluid supply system, and validity of the technology was verified

Design of lubricating oil temperature monitoring system of emulsion pump

In view of problem that current lubricating oil temperature monitoring method of emulsion pump can only identify oil temperature overrunning but cannot monitor abnormal temperature rise, an oil temperature monitoring method based on statistical process control was proposed according to statistical properties of oil temperature process data, and an implementation scheme of exponentially weighted moving average control chart applied to oil temperature monitoring was given. On the basis of the scheme, an oil temperature monitoring system of emulsion pump was designed. The practical application results show that the system can monitor oil temperature of emulsion pump in real time and send out fault alarm accurately

Method for extracting froth velocity of coal slime flotation based on image feature matching

The local static characteristics of coal slime flotation foam image are similar. The dynamic characteristics of flotation foam image are needed for judging some complex working conditions. The existing extraction method for the dynamic features of the froth velocity of coal slime flotation has the problems of insufficient accuracy, real-time performance and stability. In order to solve the above problems, a feature extraction method of froth velocity in coal slime flotation based on image feature matching is proposed. Firstly, the contrast limited adaptive histogram equalization (CLAHE) and block-matching and 3D filtering(BM3D) are used to preprocess the flotation froth image to improve the quality of the image and highlight the edge details of the image. Secondly, the accelerated-KAZE (AKAZE) algorithm of accelerated features in nonlinear scale space is used to detect the feature points of flotation froth features. Thirdly, on the basis of rough matching of froth image features by brute-force matching (BF), a grid-based motion statistics (GMS) algorithm is used to quickly and reliably distinguish correct and wrong feature matching. Finally, the method calculates the slime foam velocity according to the feature matching results. The foam velocity is taken as the measured value. The Kalman motion estimation method is used to iteratively modify the measured values to obtain more stable foam velocity characteristics of coal slime flotation. The experimental results show the following points. ① The AKAZE-GMS algorithm can solve the problem of feature point clustering well and keep more feature points as much as possible. This is because the preprocessed image is less affected by noise, has better contrast, and has more prominent edge features. ② Compared with SIFT (scale-invariant feature transform), SURF (speeded up robust features) and AKAZE, the AKAZE-GMS algorithm has a more uniform distribution of matching pairs, retains more correct matching pairs. The method achieves a matching accuracy of 99.99%. The running time is only 3.73 s. ③ The measured value of froth velocity directly calculated from the feature matching results fluctuates greatly. The velocity estimated value of the measured value corrected by Kalman motion estimation is more stable, which is more consistent with the real working condition

Design of automatic shunt control system in dense-medium separation process

In view of problems of poor accuracy and high medium consumption of manual control of shunt in dense-medium separation process , an automatic shunt control system in dense-medium separation process was designed, and opening prediction model of the shunt valve based on the least squares support vector machines was established. The parameters including suspension density, coal slime content, qualified medium tank level and opening degree of water supply valve are collected by sensors, which are used as input variables of opening prediction model of the shunt valve. Output value of opening degree of the shunt valve is calculated by the model and then is sent to PLC, so as to control operation of the shunt valve and realize automatic shunt control. The application results show that the density fluctuation of suspension liquid can be controlled in the range of ±0.005 g/cm3, and the ton coal dense-medium consumption is reduced by 0.216 kg

Integrated estimation model of clean coal ash content for froth flotation based on model updating and multiple LS-SVMs

Clean coal ash content, a prominent product index describing coal froth flotation, is difficult to be measured online. This constraint leads to a lack of timely guidance during operation and impedes the optimal operation of the coal flotation process. To solve this problem, considering the fluctuation of working conditions, the heterogeneity of raw coal and the variation of feed coal classes, an integrated estimation model of clean coal ash content for coal flotation based on model updating and multiple least squares support vector machines (LS-SVMs) is proposed. First, a single estimation model for a single class of coal based on LS-SVM is built, and the internal parameters are optimized by gravitational search algorithm (GSA). Second, the model updating strategy is designed to solve the problem of the decline in single model accuracy. Furthermore, a multiple LS-SVMs model formed by several single models for different classes of coal is studied along with the model switching mechanism to address the problem of model mismatch. Finally, an industrial experiment and evaluation are conducted. The mean relative error between the estimated and actual values is 3.32%, and the correlation coefficient is 0.9331. The estimation accuracy and adaptability of the integrated model can meet the industrial requirements