Economic risk assessment of PM10 in coal-based industrial region and its management strategy

2021 ◽  
Vol 14 (23) ◽  
Author(s):  
Aishwarya Mishra ◽  
Sanjay Kumar Palei ◽  
Netai Chandra Karmakar ◽  
Mrutyunjaya Mishra
2014 ◽  
Vol 8 ◽  
pp. 16-31 ◽  
Author(s):  
Diane Arjoon ◽  
Yasir Mohamed ◽  
Quentin Goor ◽  
Amaury Tilmant

2021 ◽  
Author(s):  
Jaden C. Miller ◽  
Spencer C. Ercanbrack ◽  
Chad L. Pope

Abstract This paper addresses the use of a new nuclear power plant performance risk analysis tool. The new tool is called Versatile Economic Risk Tool (VERT). VERT couples Idaho National Laboratory’s SAPHIRE and RAVEN software packages. SAPHIRE is traditionally used for performing probabilistic risk assessment and RAVEN is a multi-purpose uncertainty quantification, regression analysis, probabilistic risk assessment, data analysis and model optimization software framework. Using fault tree models, degradation models, reliability data, and economic information, VERT can assess relative system performance risks as a function of time. Risk can be quantified in megawatt hours (MWh) which can be converted to dollars. To demonstrate the value of VERT, generic pressurized water reactor and boiling water reactor fault tree models were developed along with time dependent reliability data to investigate the plant systems, structures, and components that impacted performance from the year 1980 to 2020. The results confirm the overall notion that US nuclear power plant industry operational performance has been improving since 1980. More importantly, the results identify equipment that negatively or positively impact performance. Thus, using VERT, individual plant operators can target systems, structures, and components that merit greater attention from a performance perspective.


2016 ◽  
Vol 85 (1) ◽  
pp. 605-618 ◽  
Author(s):  
Fabian Rodriguez ◽  
Theofilos Toulkeridis ◽  
Washington Sandoval ◽  
Oswaldo Padilla ◽  
Fernando Mato

2011 ◽  
Vol 92 (10) ◽  
pp. 2723-2733 ◽  
Author(s):  
Judith S. Hannak ◽  
Sarah Kompatscher ◽  
Michael Stachowitsch ◽  
Jürgen Herler

Author(s):  
Kongtae Ra ◽  
Joung-Keun Kim ◽  
Jung-Moo Lee ◽  
Seung-Yong Lee ◽  
Eun-Soo Kim ◽  
...  

2009 ◽  
Vol 13 (4) ◽  
pp. 312-327 ◽  
Author(s):  
Edgar Sanchez-Zazueta ◽  
Francisco Javier Martinez-Cordero

2021 ◽  
Vol 13 (14) ◽  
pp. 7830
Author(s):  
Min-Yuan Cheng ◽  
Mohammadzen Hasan Darsa

Construction project schedule delay is a worldwide concern and especially severe in the Ethiopian construction industry. This study developed a Construction Schedule Risk Assessment Model (CSRAM) and a management strategy for foreign general contractors (FGCs). 94 construction projects with schedule delay were collected and a questionnaire survey of 75 domain experts was conducted to systematically select 22 risk factors. In CSRAM, the artificial neural network (ANN) inference model was developed to predict the project schedule delay. Integrating it with the Garson algorithm (GA), the relative weights of risk factors with rankings were calculated and identified. For comparison, the Relative Importance Index (RII) method was also applied to rank the risk factors. Management strategies were developed to improve the three highest-ranked factors identified using the GA (change order, corruption/bribery, and delay in payment), and the RII (poor resource management, corruption/bribery, and delay in material delivery). Moreover, the improvement results were used as inputs for the trained ANN to conduct a sensitivity analysis. The findings of this study indicate that improvements in the factors that considerably affect the construction schedule can significantly reduce construction schedule delays. This study acts as an important reference for FGCs who plan to enter or work in the Ethiopian construction industry.


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