Prediction and control model of shale induced fracture leakage pressure

2021 ◽  
Vol 198 ◽  
pp. 108186
Author(s):  
Xiaopeng Zhai ◽  
Hui Chen ◽  
Yishan Lou ◽  
Huimei Wu
Keyword(s):  
Control Model ◽  
Prediction And Control ◽  
And Control

scholarly journals Furnace heat prediction and control model and its application to large blast furnace

2019 ◽  
Vol 38 (2019) ◽  
pp. 884-891
Author(s):  
Zhuang-nian Li ◽  
Man-sheng Chu ◽  
Zheng-gen Liu ◽  
Gen-ji Ruan ◽  
Bao-feng Li
Keyword(s):  
Blast Furnace ◽  
Control Model ◽  
Heat Index ◽  
Model Parameters ◽  
Stable Operation ◽  
Oxygen Balance ◽  
Large Blast ◽  
Large Blast Furnace ◽  
Prediction And Control ◽  
And Control

AbstractBlast furnace heat is the key to the blast furnace’s high efficiency and stable operation, and it is difficult to maintain a suitable temperature for large blast furnace operations. When designing the furnace heat prediction and control model, parameters with good reliability and measurability should be chosen to avoid using less accurate parameters and to ensure the accuracy and practicability of the model. This paper presents an effective model for large blast furnace temperature prediction and control. Using thermal equilibrium and the carbon-oxygen balance of the blast furnace’s high-temperature zone, the slag-iron heat index was calculated. Using the relation between the molten iron temperature and slag-iron heat index, the furnace heat parameter can be calculated while production conditions are changed,which can guide furnace heat control.

Study of Intelligent Prediction and Control of Workpiece Size in Traverse Grinding

2006 ◽  
Vol 304-305 ◽  
pp. 191-195
Author(s):  
Ning Ding ◽  
Long Shan Wang ◽  
Guang Fu Li ◽  
J.Z. Wang ◽  
Xiao Wei Chen
Keyword(s):  
Fuzzy Control ◽  
Prediction Model ◽  
Control Model ◽  
Control Precision ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Prediction And Control ◽  
Prediction Control ◽  
And Control ◽  
Traverse Grinding

A size intelligent prediction control model during traverse grinding is constructed. The model is composed of the neural network prediction model, the deformation optimal adaptive control system and fuzzy control model. Dynamic Elman network is used in the prediction model. The first and the second derivative of the actual amount removed from the workpiece are added into the network input, which can greatly improve the prediction accuracy. The flexible factor is introduced to the fuzzy control model, which can self-adapt and adjust the quantification factor and scale factor in the fuzzy control. Simulation and experiment verify that the developed prediction control model is feasible and has high prediction and control precision.

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

A case study of wear-type rail corrugation prediction and control using speed variation

2009 ◽  
Vol 325 (1-2) ◽  
pp. 85-105 ◽  
Author(s):  
P.A. Meehan ◽  
P.A. Bellette ◽  
R.D. Batten ◽  
W.J.T. Daniel ◽  
R.J. Horwood
Keyword(s):  
Rail Corrugation ◽  
Speed Variation ◽  
Prediction And Control ◽  
And Control
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