Design of a Preventive Clearing Blocked System Using PLC Control

2014 ◽  
Vol 556-562 ◽  
pp. 1290-1293
Author(s):  
Xiang Lin Zhang
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Film Coating ◽  
Neural Element ◽  
Bp Network ◽  
Network Training ◽  
Pharmaceutical Tablet ◽  
Rolling Surface ◽  
Key Factor ◽  
Bp Neural Network Model

Film coating machine by way of spraying the coating solution onto the drug is in a continuous rolling surface. The tablet tends to produce tape smaller even if the spray flow blockage. Then the tablet will crack due to high temperature. Therefore, pharmaceutical tablet coating spray flow rate is a key factor affecting the quality. This article first proposes preventive clearing blocked in the pipeline but not blocking the upcoming clogged. Air purge starts immediately. PLC real-time acquisition is carried out by the pressure at both ends of the pipeline, the pipeline flow liquid contents, when the pressure is detected. The flow reaches a critical point obtained by BP network training, immediately starting the high-pressure air purge. The value of real-time detection uses PLC gun pipeline pressure, flow, etc. It combined weights of the neural element BP neural network model that was trained by the PLC programming. Gun pipe blockage and promptly started high pressure alarm air purge, preventive clearing blocked. Experiments show that preventive clearing blocked pipe blockage can promptly remove the gun.

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

Real-time Downhole Monitoring and Logging Reduced Mud Loss Drastically for High-Pressure Gas Wells in Tarim Basin, China

2008 ◽  
Author(s):  
ShunChang Wang ◽  
Xinquan Zheng ◽  
Chun Jiang Zheng ◽  
Bailin Wu ◽  
YiMing Jiang ◽  
...  
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Tarim Basin ◽  
Gas Wells ◽  
Mud Loss ◽  
Downhole Monitoring

scholarly journals Data-driven Adaptive Thresholding Model for Real-time Valve Delay Estimation in Digital Pump/Motors

2020 ◽  
Author(s):  
Abdallah Chehade ◽  
Farid Breidi ◽  
Keith Scott Pate ◽  
John Lumkes
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Domain Knowledge ◽  
Operating Conditions ◽  
Delay Model ◽  
Hydraulic Systems ◽  
Pressure Transducers ◽  
Statistical Behavior ◽  
Shift Detection ◽  
And Performance

Valve characteristics are an essential part of digital hydraulics. The on/off solenoid valves utilized on many of these systems can significantly affect the performance. Various factors can affect the speed of the valves causing them to experience various delays, which impact the overall performance of hydraulic systems. This work presents the development of an adaptive statistical based thresholding real-time valve delay model for digital Pump/Motors. The proposed method actively measures the valve delays in real-time and adapts the threshold of the system with the goal of improving the overall efficiency and performance of the system. This work builds on previous work by evaluating an alternative method used to detect valve delays in real-time. The method used here is a shift detection method for the pressure signals that utilizes domain knowledge and the system’s historical statistical behavior. This allows the model to be used over a large range of operating conditions, since the model can learn patterns and adapt to various operating conditions using domain knowledge and statistical behavior. A hydraulic circuit was built to measure the delay time experienced from the time the signal is sent to the valve to the time that the valve opens. Experiments were conducted on a three piston in-line digital pump/motor with 2 valves per cylinder, at low and high pressure ports, for a total of six valves. Two high frequency pressure transducers were used in this circuit to measure and analyze the differential pressure on the low and high pressure side of the on/off valves, as well as three in-cylinder pressure transducers. Data over 60 cycles was acquired to analyze the model against real time valve delays. The results show that the algorithm was successful in adapting the threshold for real time valve delays and accurately measuring the valve delays. 

Innovative Minimally Intrusive Sensor Technology Development for Versatile Affordable Advanced Turbine Engine Combustors

2002 ◽  
Author(s):  
Neil Goldstein ◽  
Carlos A. Arana ◽  
Fritz Bien ◽  
Jamine Lee ◽  
John Gruninger ◽  
...  
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Real Time ◽  
Gas Turbines ◽  
High Performance ◽  
Chemical Species ◽  
Sensor Technology ◽  
Spectral Signature ◽  
Temperature Pattern ◽  
Hot Gas

The feasibility of an innovative minimally intrusive sensor for monitoring the hot gas stream at the turbine inlet in high performance aircraft gas turbine engines was demonstrated. The sensor uses passive fiber-optical probes and a remote readout device to collect and analyze the spatially resolved spectral signature of the hot gas in the combustor/turbine flowpaths. Advanced information processing techniques are used to extract the average temperature, temperature pattern factor, and chemical composition on a sub-second time scale. Temperatures and flame composition were measured in a variety of combustion systems including a high pressure, high temperature combustion cell. Algorithms for real-time temperature measurements were developed and demonstrated. This approach should provide a real-time temperature profile, temperature pattern factor, and chemical species sensing capability for multi-point monitoring of high temperature and high pressure flow at the combustor exit with application as an engine development diagnostic tool, and ultimately, as a real-time active control component for high performance gas turbines.

Real-Time Prediction of Choke Health Using Production Data Integrated with AI

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Large Scale ◽  
Production Systems ◽  
Rate Of Change ◽  
Production Data ◽  
Ann Model ◽  
Sand Production ◽  
Data Set ◽  
Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

Social Networking Engagement and Crisis Communication Considerations

2017 ◽  
pp. 130-155
Author(s):  
Mitchell Marovitz
Keyword(s):  
High Pressure ◽  
Social Networking ◽  
Real Time ◽  
Crisis Communication ◽  
High Speed ◽  
Crisis Communications ◽  
Digital Resources ◽  
Formative Measurement ◽  
Organizational Communications ◽  
The Creation

This chapter covers crisis communication, focusing on the crucial activities before, during, and after a crisis occurs. An environmental scan identifies risks an organization may encounter. Prioritizing these risks informs the creation of crisis communications plans for each risk. These plans include a strategy to guide all organizational communications efforts. Timeline, budget, products, digital resources, and formative measurement all must be considered. Practice is essential. These actions can prepare an organization and its people to manage high-pressure, high-speed activities in real time. Once the crisis is controlled, it is time to evaluate the plan and institute adjustments as required.

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