Intelligent Utilization of a Unified Flow Pattern Prediction Model in Production System Optimization

1991 ◽  
Author(s):  
S. Arirachakaran ◽  
D.A. Papadimitriou ◽  
L.L. Jefferson ◽  
J.P. Brill ◽  
O. Shoham
Keyword(s):  
Prediction Model ◽  
Flow Pattern ◽  
Production System ◽  
System Optimization ◽  
Pattern Prediction

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

SPE Journal ◽  
2010 ◽  
Vol 15 (02) ◽  
pp. 417-425 ◽  
Author(s):  
Hui-June Park ◽  
Jong-Se Lim ◽  
Jeongyong Roh ◽  
Joo M. Kang ◽  
Bae-Hyun Min
Keyword(s):  
Production System ◽  
System Optimization ◽  
Genetic Approach ◽  
Gas Fields ◽  
Fuzzy Genetic

Production System Optimization of Gas Fields Using Hybrid Fuzzy-Genetic Approach

2006 ◽  
Author(s):  
Hui-june Park ◽  
Jong-Se Lim ◽  
Jeongyong Roh ◽  
Joo Myung Kang ◽  
Bae-hyun Min
Keyword(s):  
Production System ◽  
System Optimization ◽  
Genetic Approach ◽  
Gas Fields ◽  
Fuzzy Genetic

scholarly journals Construction of Intelligent Integrated Model Framework for the Workshop Manufacturing System Via Digital Twin

2021 ◽  
Author(s):  
Zhongyu Zhang ◽  
Zhenjie Zhu ◽  
Jinsheng Zhang ◽  
Jingkun Wang
Keyword(s):  
Real Time ◽  
Production System ◽  
Manufacturing System ◽  
System Optimization ◽  
Production Cycle ◽  
Model Framework ◽  
The Real ◽  
Digital Twin ◽  
Twin Model ◽  
Information Layer

Abstract With the drastic development of the globally advanced manufacturing industry, transition of the original production pattern from traditional industries to advanced intelligence is completed with the least delay possible, which are still facing new challenges. Because the timeliness, stability and reliability of them is significantly restricted due to lack of the real-time communication. Therefore, an intelligent workshop manufacturing system model framework based on digital twin is proposed in this paper, driving the deep inform integration among the physical entity, data collection, and information decision-making. The conceptual and obscure of the traditional digital twin is refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. A refined nine-layer intelligent digital twin model framework is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer and interface layer, scientifically managing the physical resources as well as the operation and maintenance of the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colony are applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

scholarly journals Data driven methodology for model selection in flow pattern prediction

Heliyon ◽  
2019 ◽  
Vol 5 (11) ◽  
pp. e02718 ◽  
Author(s):  
Juan Sebastian Hernandez ◽  
Carlos Valencia ◽  
Nicolas Ratkovich ◽  
Carlos F. Torres ◽  
Felipe Muñoz
Keyword(s):  
Model Selection ◽  
Flow Pattern ◽  
Data Driven ◽  
Pattern Prediction

scholarly journals Direct Use of Geothermal Resources for Circular Food Production

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 497 ◽  
Author(s):  
Ragnheidur Thorarinsdottir ◽  
Runar Unnthorsson
Keyword(s):  
Value Chain ◽  
Production System ◽  
Food Production ◽  
System Optimization ◽  
Production Unit ◽  
Heat Extraction ◽  
Geothermal Resources ◽  
Commercial Unit ◽  
Recirculating Aquaculture Systems ◽  
Direct Use

The objectives of the work are to increase the direct use of geothermal resources for circular food production systems. The focus is on circular agricultural production processes: combining recirculating aquaculture systems and hydroponics into one system, including water treatment and waste recovery processes. The main outputs are vegetables, fish, fertilizers and potentially, algae and biogas. These outputs can generate revenue streams that can cover the costs of heat extraction while supporting viable businesses. The results and conclusions from a pilot case that was conducted in Iceland in recent years are presented, and the next steps are discussed. The pilot setup is now in the process of expansion to a semi-commercial production unit. However, there are still scientific, technical and commercial challenges to be solved. The scientific challenges are interdisciplinary and relate mainly to the optimization of the overall production system. Optimization involves creating good environmental conditions for each production unit while maintaining optimal oxygen, carbon dioxide, relevant pH and temperature levels and supplying all necessary nutrients. Additionally, accumulation of salts or other unwanted substances must be prevented. The primary technical challenges are to develop the circular food production system for optimized production while controlling the expenditure of energy, water, nutrients and manpower resources. Optimization also involves careful choices of species and the integration of new ideas into the value chain, both of which increase the synergy between the different components of the system. Furthermore, energy efficiency needs to be improved through using excess heat for other parts of the system and developing enhanced heating and cooling cycles. The aim is to transform the semi-commercial unit into a showcase model for solving commercial challenges while presenting a feasible business model for installing and operating a geothermal well for circular food production, making the most use of all available resources, securing optimum production conditions and minimizing waste.

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