Expert Systems for Process Control Rooms

1986 ◽  
Vol 19 (9) ◽  
pp. 239-244 ◽  
Author(s):  
R A E Sargeant
Keyword(s):  
Expert System ◽  
Process Control ◽  
Expert Systems ◽  
Real Time ◽  
Large Scale ◽  
Oil And Gas ◽  
Computer Software ◽  
Cost Savings ◽  
Control Rooms ◽  
The Uk

An expert system is a computer software technique which is best (but not necessarily) implemented using languages and hardware systems from the artificial intelligence stable. The software technique offers the potential to encapsulate the experience and knowledge from many human experts and to effectively communicate it to other experts. The knowledge is mostly expressed in simple rules from which the expert system makes inferences that lead (to other rules and ultimately) to solutions to problems. The feasibility of building real time expert systems for applications in control rooms of process plants has been proven. Companies with sharp forward plans are investing in such systems now in order to obtain early benefits. The benefits can manifest themselves in improved security of production which is frequently directly quantifiable as cost savings. In 1984 the author led the team which provided one of the first practical demonstrations in the UK of a real time expert system for process control room applications. Here he reflects on practical issues of the pioneering exercise and some of the experience obtained whilst evaluating feasibility of large scale applications with European oil and gas companies. (Fig 1)

Expert Systems Methodology for Real-time Process Control

1987 ◽  
Vol 20 (5) ◽  
pp. 279-286 ◽  
Author(s):  
R.L. Moore ◽  
L.B. Hawkinson ◽  
M. Levin ◽  
A.G. Hofmann ◽  
B.L. Matthews ◽  
...  
Keyword(s):  
Process Control ◽  
Expert Systems ◽  
Real Time ◽  
Time Process ◽  
Systems Methodology

Operator Aids and Expert Systems in User Computer Interfaces

1986 ◽  
Vol 30 (11) ◽  
pp. 1082-1085
Author(s):  
William R. Nelson
Keyword(s):  
Expert System ◽  
Process Control ◽  
Expert Systems ◽  
Research Program ◽  
Nuclear Reactor ◽  
Human Life ◽  
Decision Aids ◽  
The United States ◽  
Nuclear Industry ◽  
Control Operators

Recent events have demonstrated the potential for catastrophic accidents at process control facilities, resulting in severe economic damages or loss of human life. Human operators play a central role in the outcome of accidents in process control plants, because of their responsibility to make decisions regarding the appropriate corrective actions needed to control the event. In recent years, researchers have been attempting to apply Artificial Intelligence (AI) methods for developing computer-based decision aids for process control operators. Much of this research activity has taken place within the nuclear industry. In order to assess the implications of expert systems for nuclear reactor operators, the United States Nuclear Regulatory Commission (USNRC) has sponsored a research program at the Idaho National Engineering Laboratory (INEL). Included in this program have been the development of a prototype expert system for nuclear reactor operators, as well as two experiments to measure the effects of the expert system on operator performance in simulated accident conditions. This paper briefly summarizes the experience gained during this research program and assesses the potential future of expert system decision aids for process control operators.

Towards the development of a real-time expert system for process control

1986 ◽  
Author(s):  
F. Grzesiak ◽  
X. Tang ◽  
M. Marrakchi ◽  
D. Willaeys
Keyword(s):  
Expert System ◽  
Process Control ◽  
Real Time

Planning for Unknown in The New Age of Digital: A Paradigm for Offshore Oil and Gas Risk Assessment and Management

2021 ◽  
Author(s):  
Hamed Hamedifar ◽  
Herve Wilczynski
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Large Scale ◽  
Oil And Gas ◽  
Cost Savings ◽  
Service Companies ◽  
Digital Work ◽  
Oil And Gas Operations ◽  
The Impact ◽  
Risk Assessment And Management

Abstract Major Oil and Gas operators and service companies look to undertake large scale digital transformations aimed at producing integrated, connected, and intelligent enterprises. These transformations require accelerating the journey to the cloud to modernize the entire application portfolio. By transitioning to the cloud, firms enjoy improved data analytics which allow for evolution to next generation digital work environment. This shift, however, comes with workforce challenges. Employees in all categories and at most levels will require significant cross- and up-skilling to take full advantage of the digital transformation. As vendors, suppliers, service companies, and operators move products and equipment around an expanding ecosystem of assets, security threats are likely to increase due to further geopolitical instability. Data based decision making, which enables the optimization of assets and automation of operations to minimize workforce risk exposure must be implemented with consideration of enterprise risk reduction (across the asset and workforce operational risk life cycle). As Oil and Gas operations become more geographically dispersed and diverse, they are exposed to new and evolving risk factors which can directly impact value. These risk factors make asset acquisition, development, management, and maintenance all more challenging. Analyses of risk in a digital foundation risk-based platform is most valuable at the earliest stages of asset development in determining whether to proceed with the planned development through to end-of-life decommissioning. Successful firms must create an end-to-end digital roadmap which delineates between technical and transactional activities and outlines effective stakeholder engagement at each project stage. The fundamental thesis of this paper is that although risk can be mitigated and reduced through the introduction of digital tools into oil and gas operations, it can never be completely removed. Furthermore, while industry research on the impact of digitalization usually rely heavily on cost savings, optimization, and health, safety, and environment (HSE) related cases, they typically fail to consider the contribution of digitalization on risk assessment and management. This paper argues that we need to move away from the focus on cost savings, process optimization, and HSE metrics improvement metrics. This paper sets up a mechanism for developing risk-based strategies for implementation of digital solutions.

Knowledge Acquisition for Large-Scale Expert Systems in Transportation

1998 ◽  
Vol 1651 (1) ◽  
pp. 59-65
Author(s):  
Jeffrey L. Adler ◽  
Eknauth Persaud
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Traditional Knowledge ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Problem Domain ◽  
Client Server ◽  
Server Application

One of the greatest challenges in building an expert system is obtaining, representing, and programming the knowledge base. As the size and scope of the problem domain increases, knowledge acquisition and knowledge engineering become more challenging. Methods for knowledge acquisition and engineering for large-scale projects are investigated in this paper. The objective is to provide new insights as to how knowledge engineers play a role in defining the scope and purpose of expert systems and how traditional knowledge acquisition and engineering methods might be recast in cases where the expert system is a component within a larger scale client-server application targeting multiple users.

Information Management Practice of Cold Rolling Progress Based on "High-Speed Management"

2012 ◽  
Vol 572 ◽  
pp. 160-164
Author(s):  
Sheng Hui Jia ◽  
Lei Guo ◽  
Wei Xiang Wang
Keyword(s):  
Process Control ◽  
Cold Rolling ◽  
Production Process ◽  
Real Time ◽  
Information Management ◽  
High Speed ◽  
Large Scale ◽  
Management Practice ◽  
Speed Management ◽  
Time Evaluation

A unit of high-speed, large-scale, continuous, automated PCM, which is used for production process information management practice, is introduced in this paper. According to the practice of cold rolling equipment and "High-speed Management," advanced management methods and tools are embedded to process control, so as to achieve online control of "quality flow", real-time evaluation and improvement.

Process control using the G2 Real-Time Expert System

2003 ◽  
Author(s):  
R.L. Moore ◽  
H. Rosenhof ◽  
G. Stanley
Keyword(s):  
Expert System ◽  
Process Control ◽  
Real Time
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