Optimization of life-cycle preventative maintenance strategies using genetic algorithm and Bayesian Updating

2010 ◽  
pp. 318-318 ◽  
Author(s):  
E Tantele ◽  
T Onoufriou
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Bayesian Updating ◽  
Preventative Maintenance ◽  
Maintenance Strategies

Multi-objective optimization of safety instrumented systems maintenance strategy: a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hafed Touahar ◽  
Nouara Ouazraoui ◽  
Nor El Houda Khanfri ◽  
Mourad Korichi ◽  
Bilal Bachi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Environmental Risks ◽  
Life Cycle Costs ◽  
Maintenance Strategy ◽  
Maintenance Strategies ◽  
Content Type ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

PurposeThe main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be activated prematurely, these activations are characterized in terms of frequency by a Spurious Trip Rate (STR) and their occurrence leads to significant technical, economic and even environmental losses. This work aims to propose an approach to optimize the performances of the SIS by a multi-objective genetic algorithm. The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).Design/methodology/approachThe optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).FindingsA case study concerning a safety instrumented system implemented in the RGTE facility has shown the great applicability of the proposed approach and the results are encouraging. The results show that the selection of a good maintenance strategy allows a very significant minimization of the PFDavg, the frequency of spurious trips and Life Cycle Costs of SIS.Originality/valueThe maintenance strategy defined by the system designer can be modified and improved during the operational phase, in particular safety systems. It constitutes one of the least expensive investment strategies for improving SIS performances. It has allowed a considerable minimization of the SIS life cycle costs; PFDavg and the frequency of spurious trips.

scholarly journals Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

2020 ◽  
Vol 13 (1) ◽  
pp. 290
Author(s):  
Seyed Hashem Mousavi-Avval ◽  
Shahin Rafiee ◽  
Ali Mohammadi
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Fuzzy Inference ◽  
Cost Ratio ◽  
Inference System ◽  
Benefit Cost Ratio ◽  
Multi Objective Genetic Algorithm ◽  
Neuro Fuzzy ◽  
Environmental Emissions ◽  
Benefit Cost

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

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