The Experts below are selected from a list of 206715 Experts worldwide ranked by ideXlab platform
Carlos Magno Couto Jacinto - One of the best experts on this subject based on the ideXlab platform.
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nsga ii trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
Enrico Zio - One of the best experts on this subject based on the ideXlab platform.
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nsga ii trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
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Availability based simulation and optimization modeling framework for open pit mine truck allocation under dynamic constraints
International journal of mining science and technology, 2013Co-Authors: Rodrigo Mena, Enrico Zio, Fredy Kristjanpoller, Adolfo ArataAbstract:Abstract We present a novel system productivity simulation and optimization modeling framework in which equipment Availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck–shovel system of an open-pit mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator–optimizer software tool. We make an application on a real open-pit mine case study taking into account the stochasticity of the equipment behavior and environment. The total system Production values obtained with and without considering the equipment reliability, Availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total Production of the system and obtain specific information on the Production Availability and productivity of its components.
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Practical Applications of Monte Carlo Simulation for System Reliability Analysis
Springer Series in Reliability Engineering, 2012Co-Authors: Enrico ZioAbstract:In this chapter, we shall illustrate some applications of MCS applied to system reliability analysis. First, the power of MCS for realistic system modeling is shown with regard to a problem of estimating the Production Availability of an offshore plant, accounting for its operative rules and maintenance procedures [1]. Then, the application of MCS for sensitivity and importance analysis [1, 2] is illustrated.
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application of monte carlo simulation for the estimation of Production Availability in offshore installations
2010Co-Authors: Kwang Pil Chang, Daejun Chang, Enrico ZioAbstract:The purpose of this chapter is to show the practical application of the Monte Carlo simulation method in the evaluation of the Production Availability of offshore facilities, accounting for realistic aspects of system behavior. A Monte Carlo simulation model is developed for a case study to demonstrate the effect of maintenance strategies on the Production Availability, e.g., by comparing the system performance under different preventive maintenance tasks.
Valeria Vitelli - One of the best experts on this subject based on the ideXlab platform.
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nsga ii trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
Enrique López Droguett - One of the best experts on this subject based on the ideXlab platform.
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nsga ii trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil gas equipment
Expert Systems With Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
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NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment
Expert Systems with Applications, 2013Co-Authors: Valeria Vitelli, Enrico Zio, Enrique López Droguett, Carlos Magno Couto JacintoAbstract:Scale deposition can damage equipment in the oil & gas Production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for Production Availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem using an empirical modeling approach, based on experimental data. Specifically, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to train a neural network (NN) (i.e. to find its parameters, that is its weights and biases) to provide the prediction intervals (PIs) of the scale deposition rate. The PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). We perform k-fold cross-validation to guide the choice of the NN structure (i.e. the number of hidden neurons). We use hypervolume indicator metric to evaluate the Pareto fronts in the validation step. A case study is considered, with regards to a set of experimental observations: the NSGA-II-trained neural network is shown capable of providing PIs with both high coverage and small width.
Antoine Rauzy - One of the best experts on this subject based on the ideXlab platform.
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Production Availability analysis of floating Production storage and offloading fpso systems
Applied Ocean Research, 2018Co-Authors: Huixing Meng, Leïla Kloul, Antoine RauzyAbstract:Abstract Floating Production Storage and Offloading (FPSO) systems are currently the popular scheme in offshore oil and gas industry. The profitability of these systems is extremely dependent on their Production availabilities. In this article, we report the lessons learned from the assessment of the Production Availability of a FPSO system. Regarding this study, we used stochastic simulation as the assessment tool because it is naturally suitable for performance evaluation of the Production systems. To obtain relevant results, it requires a strong modeling discipline as well as rigorous experimental protocols. By adopting modeling patterns in the Production Availability analysis, we can model the target systems in a modular way. We propose here to build models by assembling modeling patterns dedicated to Production Availability studies. We discuss the performed experiments with a special focus on sensitivity analyses. The results by changing the failure rates are validated with those altering the repair rates.
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IMBSA - Safety Analysis of a Data Center’s Electrical System Using Production Trees
Model-Based Safety and Assessment, 2017Co-Authors: Walid Mokhtar Bennaceur, Leïla Kloul, Antoine RauzyAbstract:In this paper, we investigate the Production Availability of a data center’s power system using Production Trees, a new modeling methodology for Availability analysis of Production systems. Production Trees (PT) allow modeling the relationship between the units of a Production system with a particular attention to the Production levels of the units located upstream and downstream a Production line. For that new modeling operators have been introduced allowing to gather or to split the flows upstream or downstream a PT. Our results include the reliability level of the power system configuration in terms of load interruption, load loss probability and related frequency indices, and the importance factor of components to identify the critical parts of the system.
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Safety Analysis of a Data Center’s Electrical System Using Production Trees
Model-Based Safety and Assessment, 2017Co-Authors: Walid Mokhtar Bennaceur, Leïla Kloul, Antoine RauzyAbstract:In this paper, we investigate the Production Availability of a data center’s power system using Production Trees, a new modeling methodology for Availability analysis of Production systems. Production Trees (PT) allow modeling the relationship between the units of a Production system with a particular attention to the Production levels of the units located upstream and downstream a Production line. For that new modeling operators have been introduced allowing to gather or to split the flows upstream or downstream a PT. Our results include the reliability level of the power system configuration in terms of load interruption, load loss probability and related frequency indices, and the importance factor of components to identify the critical parts of the system.
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Production trees: A new modeling methodology for Production Availability analyses
Reliability Engineering & System Safety, 2017Co-Authors: Leïla Kloul, Antoine RauzyAbstract:Abstract In this article, we propose a new modeling methodology for Production Availability analyses. These analyses are typically carried out by means of flow network models and Monte-Carlo simulations. The design of flow network models is often delicate because the Production of a unit may depend on the states of other units located downstream and upstream in the Production line. We show here how to handle this problem by means of operators working on three flows: a capacity flow moving forward from source to target units, a demand flow moving backward from target units to source units, and finally a Production flow moving forward from source to target units. The Production depends on the demand which itself depends on the capacity. Models designed according to this scheme caneventually be seen either as flow networks or as an extension of (Dynamic) Fault Trees to Production Availability analyses. We present the AltaRica 3.0 library of modeling patterns we designed to represent the different operators. We report results of experiments we performed on models designed using this library.
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Production performance of an offshore system by applying AltaRica 3.0
Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, 2016Co-Authors: Huixing Meng, Leïla Kloul, Benjamin Aupetit, Antoine RauzyAbstract:Dans cet article, nous presentons une etude destinee a evaluer la disponibilite d'une unite flottante de Production, de stockage et de dechargement (FPSO: Floating Production Storage and Offloading unit) realisee avec le langage de modelisation AltaRica 3.0. L'objectif principal de notre travail etait d'identifier les parametres sensibles et les composants critiques a l'egard de la disponibilite de la Production. Prendre en compte les parametres sensibles et s’assurer du bon fonctionnement des composants critiques peut permettre de garantir une disponibilite et une Production elevees du systeme de Production offshore In this article, we show the application of the AltaRica 3.0 modeling language for assessing the Production Availability of a Floating Production Storage and Offloading system (FPSO). The main objective of our study is to identify the sensitive parameters and crucial components with respect to the Production Availability. Taking care of the sensitive parameters and ensuring the well working status of crucial components can guarantee a relative high Production Availability of the offshore Production system.
