Computer simulation of heuristic reinforcement-learning systems for nuclear power plant load changes control

1979 ◽  
Vol 18 (3) ◽  
pp. 339-352 ◽  
Author(s):  
Jacek Kitowski ◽  
Jacek Mościński
2022 ◽  
Vol 145 ◽  
pp. 104107
Author(s):  
JaeKwan Park ◽  
TaekKyu Kim ◽  
SeungHwan Seong ◽  
SeoRyong Koo

Author(s):  
Beth M. Plott ◽  
Shelly Scott-Nash ◽  
Bruce P. Hallbert ◽  
Angelia L. Sebok

An analytical approach to addressing the implications of nuclear power plant shift sizing is needed as an augmentation to the classical empirical approach. The research reported in this paper was to evaluate the feasibility and validity of one potential analytical approach as a means of evaluating the consequences of crew reduction on crew performance in a nuclear power plant setting. The approach selected for analysis was task network modeling and simulation using a tool named Micro Saint. Task network modeling allows the human factors engineer to extend the information from a task analysis and generate a computer simulation of crew performance that can predict critical task times and error rates. Through modeling, the current and proposed processes can be evaluated and analyzed in order to understand, identify, and test opportunities for process improvement or reengineering. For this effort, models of a conventional nuclear power plant during four extremely demanding scenarios were developed. Task analysis and timing data were collected at the Imatran Voima Nuclear Power Plant at Loviisa, Finland. The task analyses were collected over a two week period by interviewing reactor operators, reviewing procedures, and conducting walk-throughs. We then refined the models and incorporated workload modeling constructs. At the completion of the modeling effort, the models were executed and the data collected were used to predict crew performance in varying staffing conditions.


2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.


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