Real-Time Drilling Parameter Optimization System Increases ROP by Predicting/Managing Bit Wear

2011 ◽  
Author(s):  
Yashodhan Keshav Gidh ◽  
Hani Ibrahim ◽  
Arifin Purwanto
Keyword(s):  
Real Time ◽  
Parameter Optimization ◽  
Optimization System ◽  
Bit Wear ◽  
Drilling Parameter

Drillbit Optimization System: Real-Time Approach to Enhance Rate of Penetration and Bit Wear Monitoring (Russian)

2015 ◽  
Author(s):  
Alexey Valisevich ◽  
Alexey Ruzhnikov ◽  
Ivan Bebeshko ◽  
Ricardo Moreno ◽  
Maxim Zhentichka ◽  
...  
Keyword(s):  
Real Time ◽  
Rate Of Penetration ◽  
Optimization System ◽  
Bit Wear ◽  
Wear Monitoring

Drillbit Optimization System: Real-Time Approach to Enhance Rate of Penetration and Bit Wear Monitoring

2015 ◽  
Author(s):  
Alexey Valisevich ◽  
Alexey Ruzhnikov ◽  
Ivan Bebeshko ◽  
Ricardo Moreno ◽  
Maxim Zhentichka ◽  
...  
Keyword(s):  
Real Time ◽  
Rate Of Penetration ◽  
Optimization System ◽  
Bit Wear ◽  
Wear Monitoring

New Approach in Real-Time Bit Wear Prediction

2010 ◽  
Author(s):  
Behrad Rashidi ◽  
Geir Hareland ◽  
Andrew Wu
Keyword(s):  
Real Time ◽  
Wear Prediction ◽  
New Approach ◽  
Bit Wear

A real-time process optimization system for injection molding

2009 ◽  
Vol 49 (10) ◽  
pp. 2031-2040 ◽  
Author(s):  
Dequn Li ◽  
Huamin Zhou ◽  
Peng Zhao ◽  
Yang Li
Keyword(s):  
Injection Molding ◽  
Real Time ◽  
Process Optimization ◽  
Time Process ◽  
Optimization System

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

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