scholarly journals A Framework for Learning System for Complex Industrial Processes

2021 ◽  
Author(s):  
Moksadur Rahman ◽  
Amare Desalegn Fentaye ◽  
Valentina Zaccaria ◽  
Ioanna Aslanidou ◽  
Erik Dahlquist ◽  
...  
Keyword(s):  
Driving Forces ◽  
Operating Cost ◽  
Optimal Operation ◽  
Learning System ◽  
Process Efficiency ◽  
Soft Sensors ◽  
Industrial Plants ◽  
Control Optimization ◽  
Learning System Architecture ◽  
And Control

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.

Industrial Maintenance Strategies

2006 ◽  
Author(s):  
Sajjad Akbar
Keyword(s):  
Operating Cost ◽  
Critical Factor ◽  
Integrated System ◽  
Maintenance Cost ◽  
Technological Advancement ◽  
Industrial Plants ◽  
Breakdown Time ◽  
Reduced Costs ◽  
Logistic Support ◽  
And Control

Industrial plants have become more complex due to technological advancement. This has made the task of maintenance more difficult. The maintenance costs in terms of resources and downtime loss are so high that maintenance function has become a critical factor in a plant’s profitability. Industry should devote as much forethought to the management of maintenance function as to production. Maintenance has grown from an art to a precise, technical engineering science. Planning, organizing scheduling and control of maintenance using modern techniques pays dividends in the form of reduced costs and increased reliability. The magnitude and the dimension of maintenance have multiplied due to development in the engineering technologies. Production cost and capacities are directly affected by the breakdown time. Total operating cost including the maintenance cost plays an important role in replacement dimension. The integrated system approach would bring forth the desired results of high maintenance standards. The standards once achieved and sustained, would add to the reliability of the plan and relieve heavy stresses and strains on the engineering logistic support.

scholarly journals Fuel metering and control optimization based on combustion state diagnosis

2020 ◽  
Vol 594 ◽  
pp. 012013
Author(s):  
X H Wang ◽  
H T Chen ◽  
X X Zhu ◽  
J L Zhang ◽  
W L Liu ◽  
...  
Keyword(s):  
Control Optimization ◽  
And Control ◽  
State Diagnosis

A robust, multi-solution framework for well placement and control optimization

2021 ◽  
Author(s):  
Mohammad Salehian ◽  
Morteza Haghighat Sefat ◽  
Khafiz Muradov
Keyword(s):  
Well Placement ◽  
Control Optimization ◽  
And Control

Control Proxy Functions for Sequential Design and Control Optimization

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Diane L. Peters ◽  
P. Y. Papalambros ◽  
A. G. Ulsoy
Keyword(s):  
Simultaneous Optimization ◽  
Sequential Optimization ◽  
Control Optimization ◽  
Smart Products ◽  
System Solution ◽  
Optimal System Design ◽  
Problem Formulations ◽  
And Control ◽  
Sequential Strategy ◽  
Selection Of

Optimal system design of “smart” products requires optimization of both the artifact and its controller. When the artifact and the controller designs are independent, the system solution is straightforward through sequential optimization. When the designs are coupled, combined simultaneous optimization can produce system-optimal results, but presents significant computational and organizational complexity. This paper presents a method that produces results comparable with those found with a simultaneous solution strategy, but with the simplicity of the sequential strategy. The artifact objective function is augmented by a control proxy function (CPF), representing the artifact’s ease of control. The key to successful use of this method is the selection of an appropriate CPF. Four theorems that govern the choice and evaluation of a CPF are given. Each theorem is illustrated using a simple mathematical example. Specific CPFs are then presented for particular problem formulations, and the method is applied to the optimal design and control of a micro-electrical mechanical system actuator.

scholarly journals Range Automatic Adjusted Current Transformer Primary Winning

2021 ◽  
Vol 12 (3) ◽  
pp. 3142-3147
Author(s):  
Amirov Sultan Fayzullayevich Et.al
Keyword(s):  
Correction Factor ◽  
Driving Forces ◽  
Operating Mode ◽  
Current Transformer ◽  
Measuring Range ◽  
Secondary Current ◽  
Measuring Devices ◽  
The Difference ◽  
Protection And Control ◽  
And Control

The article discusses the issue of introducing a correction factor for protection and control devices, as the value of the secondary current in a certain range of the auto-adjustable current transformer does not correspond to the value of the secondary current in another range determined by the difference of magnetic driving forces generated by the components of the primary current. Alternatively, an algorithm has been developed to account for the measurement error in this condition in an automatic system that controls the operating mode of the current transformer. It was also found that the output data should be transmitted taking into account the correction factor in order to ensure the proper operation of the protection and measuring devices when the current transformer is switched to another measuring range in the measuring range.

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