RELIABILITY BASED OPTIMIZATION OF TECHNICAL SPECIFICATION OF FRONTLINE SYSTEMS OF NUCLEAR POWER PLANTS USING MULTI-OBJECTIVE APPROACH

2012 ◽  
Vol 19 (01) ◽  
pp. 1250002
Author(s):  
G. SRINIVAS ◽  
A. K. VERMA ◽  
A. SRIVIDYA ◽  
SANJAY KUMAR KHATTRI
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Technical Specification ◽  
Multi Objective Optimization ◽  
Potential Core ◽  
Multi Objective ◽  
Genetic Algorithm Approach ◽  
Technical Specifications

Technical Specifications define the limiting conditions of operation, maintenance and surveillance test requirements for the various Nuclear Power plant systems in order to meet the safety requirements to fulfill regulatory criteria. These specifications impact even the economics of the plant. The regulatory approach addresses only the safety criteria, while the plant operators would like to balance the cost criteria too. The attempt to optimize both the conflicting requirements presents a case to use Multi-objective optimization. Evolutionary algorithms (EAs) mimic natural evolutionary principles to constitute search and optimization procedures. Genetic algorithms are a particular class of EA's that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection and recombination (or cross-over). In this paper we have used the plant insights obtained through a detailed Probabilistic Safety Assessment with the Genetic Algorithm approach for Multi-objective optimization of Surveillance test intervals. The optimization of Technical Specifications of three front line systems is performed using the Genetic Algorithm Approach. The selection of these systems is based on their importance to the mitigation of possible accident sequences which are significant to potential core damage of the nuclear power plant.

A Multi-Objective Optimization of the Reactor Power Plant

2018 ◽  
Author(s):  
Chen Lei ◽  
Jia Zhen ◽  
Wang Cong ◽  
Gong Zili ◽  
Liao Yi ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Mathematical Models ◽  
Steam Generator ◽  
Nuclear Power ◽  
Sensitivity Analyses ◽  
Immune Memory ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective

From the view of practical engineering application, a compacter nuclear power plant is expected. The weight and the volume of a nuclear power plant can be reduced by optimal selection of the operational parameters. In this work, a thermal-hydraulic model of the reactor, mathematical models of the reactor vessel, the main pipe, the pressurizer, the steam generator, the turbine and the condenser were established for the Qinshan-I nuclear power plant based on the related technical materials. The responses of the optimal targets to the changes of the design variables were studied by the sensitivity analyses. The non-dominated solution front of the nuclear power plant was obtained by means of the immune memory clone constrained multi-objective optimization algorithm. The study shows that the component mathematical models are reliable for the optimization process, the distribution of the non-dominated solution is decided by the steam generator secondary pressure. The volume and the weight of the system could be at least reduced by 23.0% and 9.5%, respectively.

Integration of Risk Insights Into Nuclear Power Plant Operations

2008 ◽  
Author(s):  
Glen E. Schinzel
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Technical Specification ◽  
Management Program ◽  
South Texas ◽  
Functional Requirements ◽  
Plant Operator ◽  
Practical Applications ◽  
Technical Specifications

Today’s nuclear plant operator is challenged to safely operate a complex power plant while prudently managing the business aspects with efficiency. Risk insights provide a ready tool to aid today’s operators in effectively performing both of these sometimes contradictory tasks with a sound basis. While plants possess and maintain Probabilistic Risk Assessment (PRA) models, other regulatory applications are readily available to aid the nuclear operator. Some of these tools include 10CFR 50.69 Risk-informed Categorization and Treatment of Structures, Systems, and Components for Nuclear Power Reactors, Industry Initiative 4(b) Risk Managed Technical Specifications, and Industry Initiative 5(b) Risk-informed Surveillance Test Intervals. This paper will introduce each of these risk-informed tools and will discuss practical applications of these insights at the South Texas Project nuclear power plant. These insights are readily translatable to other nuclear power facilities. 10CFR 50.69 permits a risk-informed categorization of selected structures, systems, and components. For components determined to be Low Safety Significant, many of the current regulatory controls can be reduced while maintaining reasonable confidence that these ‘Low-ranked’ components continue to perform their design functional requirements. South Texas Project was the industry’s proto-type pilot for this effort. Initiative 4(b) is a risk-informed, configuration-based approach to managing Technical Specification allowed out of service times. The limiting, deterministic allowed outage times are replaced with a Configuration Risk Management Program which uses risk threshold values to determine the length of time a Technical Specification piece of equipment can remain out of service. An imposed back-stop of 30 days is used to limit the allowed outage time. This approach was approved for South Texas Project in July 2007, and South Texas Project was the industry pilot plant for this effort. Initiative 5(b) is a risk-informed approach to Technical Specification surveillance test intervals. This approach allows surveillance test intervals to be removed from Tech Specs and placed in an owner-controlled program. Once relocated, a blending of probabilistic and deterministic insights is used to assess proposed extensions of surveillance test intervals. Once implemented, a feedback process is relied upon to validate the acceptability of the revised testing interval. This approach was piloted by the Limerick Nuclear Station, and South Texas Project submitted a request in October 2007 to the NRC to pursue this initiative. The above risk insights have proven very effective at South Texas Project, and could aid other nuclear operators in making well-founded, informed decisions. Risk insights also allow a Station’s limited resources to be focused on those activities and equipment which are of greatest safety significance. These insights are valuable for current licensees, and may be very beneficial to apply toward new nuclear construction.

scholarly journals Optimal design of a nuclear power plant condenser control system based on multi-objective optimization algorithm

2020 ◽  
Vol 35 (2) ◽  
pp. 95-102
Author(s):  
Chen Zhi ◽  
Yiliang Li ◽  
Huang Ke ◽  
Xiao Kai
Keyword(s):  
Control System ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Optimization Algorithm ◽  
Nuclear Power ◽  
Optimization Method ◽  
Control Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Control Optimization

A condenser control system of a nuclear power plant consists of a pressure control system, a condensate water sub-cooling degree control system and a water level control system. The existing control optimization methods can hardly take into account all the performance indices of the three control systems at the same time. To solve this problem, this paper presents a control optimization method based on a multi-objective optimization algorithm. This method takes control parameters as optimization objects, and takes the performance of step response as optimization objectives. The multi-objective particle swarm optimization algorithm based on Pareto dominance concept is used to solve the optimization problem. This enables obtaining of high-quality control parameters. Simulation results confirm the feasibility and effectiveness of this control optimization method.

Fuzzy multi-objective decision making approach for nuclear power plant installation

2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan
Keyword(s):  
Linear Programming ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Energy Supply ◽  
Energy Demand ◽  
Nuclear Power ◽  
Power Plants ◽  
Total Duration ◽  
Objective Functions ◽  
Multi Objective

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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