SAGA: a decision support system for air pollution management around a coal-fired power plant

2009 ◽  
Vol 38 (4) ◽  
pp. 444 ◽  
Author(s):  
Jose A. Souto ◽  
Marcos Hermida ◽  
Juan J. Casares ◽  
Jose L. Bermudez
Keyword(s):  
Air Pollution ◽  
Decision Support ◽  
Power Plant ◽  
Decision Support System ◽  
Support System ◽  
Pollution Management

Framework for Operational Reliability Program Development, Implementation, and Management

2003 ◽  
Author(s):  
E. Ricky Odoom
Keyword(s):  
Decision Support ◽  
Power Plant ◽  
Decision Support System ◽  
Program Development ◽  
Support System ◽  
Implementation Strategies ◽  
Integrated Approach ◽  
Operational Reliability ◽  
Operating Reliability ◽  
Process Plants

A methodology is proposed in this paper for the development, implementation, and management of Operational Reliability program suitable for power plant as well as other process plants. The methodology is a comprehensive integrated approach combining safety aspects and the operating reliability of plant’s systems. The framework for the methodology is divided into seven modules. Implementation strategies for the Operational Reliability program are discussed followed by a proposed Decision Support System for managing the program.

A decision support system for identifying abnormal operating procedures in a nuclear power plant

2012 ◽  
Vol 249 ◽  
pp. 413-418 ◽  
Author(s):  
Min-Han Hsieh ◽  
Sheue-Ling Hwang ◽  
Kang-Hong Liu ◽  
Sheau-Farn Max Liang ◽  
Chang-Fu Chuang
Keyword(s):  
Decision Support ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Decision Support System ◽  
Support System ◽  
Nuclear Power

Resilient control of a decision support system for emergency simulation at nuclear power plant

2011 ◽  
Author(s):  
Fernanda Bruno dos Santos ◽  
Ana Carolina Gama e Silva Assaife ◽  
Marcos Roberto da Silva Borges ◽  
Jose Orlando Gomes ◽  
Paulo Victor Rodigues de Carvalho
Keyword(s):  
Decision Support ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Decision Support System ◽  
Support System ◽  
Nuclear Power ◽  
Resilient Control

Thermal Power Plant Bidding Strategies IDSS Analysis and Design

2013 ◽  
Vol 717 ◽  
pp. 899-903
Author(s):  
Hong Fei Sun ◽  
Wei Hou ◽  
Yan Yan Wang ◽  
Rui Ling Lu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Power Plant ◽  
Decision Support System ◽  
Support System ◽  
Bidding Strategies ◽  
Intelligent Decision Support System ◽  
Analysis And Design ◽  
Intelligent Decision ◽  
Intelligent Decision Support

The bidding decision-making of power plant was a complicated system engineering which had high complexity and real-time, so power plant must rely on the support of information technology to complete the bidding. Therefore, how to use all kinds of advanced information technologies had become one of topics which broad power enterprises must face. This paper established a kind of intelligent decision support system (IDSS) which was based on multi-agent intelligent decision support system and combined with the data warehouse, data mining and on-line analysis processing. Compared with the former information management, IDSS had so many advantages in auxiliary support for decision-making. If it was applied to power plant bidding strategies or other semi-structural and unstructured problems in power market, it would provide good auxiliary support for them.

Leveraging Fog Computing and Deep Learning for Building a Secure Individual Health-Based Decision Support System to Evade Air Pollution

2020 ◽  
pp. 380-406
Author(s):  
Chitra P. ◽  
Abirami S.
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Decision Support ◽  
Health Status ◽  
Air Quality ◽  
Decision Support System ◽  
Support System ◽  
Quality Index ◽  
Air Quality Index ◽  
Individual Health

Globalization has led to critical influence of air pollution on individual health status. Insights to the menace of air pollution on individual's health can be achieved through a decision support system, built based on air pollution status and individual's health status. The wearable internet of things (wIoT) devices along with the air pollution monitoring sensors can gather a wide range of data to understand the effect of air pollution on individual's health. The high-level feature extraction capability of deep learning can extract productive patterns from these data to predict the future air quality index (AQI) values along with their amount of risks in every individual. The chapter aims to develop a secure decision support system that analyzes the events adversity by calculating the temporal health index (THI) of the individual and the effective air quality index (AQI) of the location. The proposed architecture utilizes fog paradigm to offload security functions by adopting deep learning algorithms to detect the malicious network traffic patterns from the benign ones.

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