A Novel Control Strategy for Pilot Controlled Proportional Flow Valve With Internal Displacement-Flow Feedback

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
He Wang ◽  
Xiaohu Wang ◽  
Jiahai Huang ◽  
Jun Wang ◽  
Long Quan
Keyword(s):  
Pressure Drop ◽  
Flow Rate ◽  
Control Strategy ◽  
Pid Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Internal Displacement ◽  
Valve Opening ◽  
Proportional Flow ◽  
Flow Valve

The present study is focused on the construction of a well-performing pilot controlled proportional flow valve with internal displacement-flow feedback. A novel control strategy for the valve is proposed in which the flow rate through the valve is directly controlled. The linear mathematical model for the valve is established and a fuzzy proportional–integral–derivative (PID) controller is designed for the flow control. In order to obtain the flow rate used as feedback rapidly and accurately in real-time, back propagation neural network (BPNN) is employed to predict the flow rate through the valve with the pressure drop through the main orifice and main valve opening, and the predicted value is used as the feedback. Both simulation and experimental results show that the predicted value obtained by BPNN is reliable and available for the feedback. The proposed control strategy is effective with which the flow rate through the valve remains almost constant when the pressure drop through the main orifice increases and the valve can be applied to the conditions where the independence of flow rate and load is required. For the valve with the proposed control strategy, the nonlinearity is less than 5.3%, the hysteresis is less than 4.2%, and the bandwidth is about 16 Hz. The static and dynamic characteristics are reasonable and acceptable.

Application of Neural Networks and PID in on Line of Real-Time Temperature Control System

2011 ◽  
Vol 110-116 ◽  
pp. 5009-5014
Author(s):  
Suleiman A. Elfandi ◽  
Mahmoud A. Osman ◽  
Nasser Ali ◽  
Nuri Agab
Keyword(s):  
Real Time ◽  
Pid Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Temperature Control System ◽  
Controlled System ◽  
Data Acquisition Card ◽  
On Line ◽  
Neural Network Algorithm ◽  
Velocity Form

The Heat Process Trainer PT326 (Feedback UK) model is obtained by using two different techniques one by the Ziegler-Nichols approximating method and the other with the system identification method. The Data acquisition card EC641 I/O PT326 is developed and tested. it can be used as an interface between PC and any similar controlled system. The “velocity” form of the PID controller algorithm is implemented in real time where this algorithm is an ideal of solving the bump less transfer mechanism between manual and automatic control operation and the implementation of anti-reset windup algorithm. The method of back propagation neural network algorithm online gives very good results with neither steady state error nor overshoots.

scholarly journals Optimization of fuel cell thermal management system based on back propagation neural network

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2975-2982
Author(s):  
Qianqian Ge ◽  
Cuncun Wei
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Thermal Management ◽  
Control Strategy ◽  
Management System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Current Control ◽  
Control Effect ◽  
Thermal Management System

Two thermal management control strategies, namely flow following current and power mode and back propagation neural network auto-disturbance rejection method, were proposed to solve significant temperature fluctuation problems, long regulation time, and slow response speed in fuel cell thermal management system variable load. The results show that the flow following current and power control strategy can effectively weaken the coupling effect between pump and radiator fan and significantly reduce the overshoot and adjustment time of inlet and outlet cooling water temperature and temperature difference reactor. Although the control effect of the neural network and strategy is insufficient under maximum power, the overall control effect is better than that of the flow following the current control strategy.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

Study of fluid flow through a channel deformed by piezoelement

2018 ◽  
Vol 13 (3) ◽  
pp. 1-10 ◽  
Author(s):  
I.Sh. Nasibullayev ◽  
E.Sh Nasibullaeva ◽  
O.V. Darintsev
Keyword(s):  
Fluid Flow ◽  
Pressure Drop ◽  
Boundary Conditions ◽  
Flow Rate ◽  
Contact Surface ◽  
Piezoelectric Element ◽  
Neumann Boundary ◽  
Differential Pressure ◽  
Physical Parameters ◽  
Flow Through

The flow of a liquid through a tube deformed by a piezoelectric cell under a harmonic law is studied in this paper. Linear deformations are compared for the Dirichlet and Neumann boundary conditions on the contact surface of the tube and piezoelectric element. The flow of fluid through a deformed channel for two flow regimes is investigated: in a tube with one closed end due to deformation of the tube; for a tube with two open ends due to deformation of the tube and the differential pressure applied to the channel. The flow rate of the liquid is calculated as a function of the frequency of the deformations, the pressure drop and the physical parameters of the liquid.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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