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APPLICATION OF computational intelligence method IN PETROLEUM GEOMECHANICS CHARACTERIZATION

INTERNATIONAL JOURNAL OF computational intelligence AND APPLICATIONS 2014年第4期13卷

作者： Zhang, Shike Yuan, Yuan Xiao, Jianqing Yin, Shunde Anyang Normal Univ Sch Civil Engn & Architecture 436 Xiange Ave Anyang 455000 Henan Peoples R China Univ Wyoming Dept Chem & Petr Engn Dept 3295 1000 E Univ Ave Laramie WY 82071 USA

Petroleum geomechanics characterization refers to the process of quantitatively assigning geomechanical parameters using all available field data. In this paper, an attempt is made to develop a computational intelligence method that integrates genetic algorithm (GA) and artificial neural network (ANN). Through this method, these geomechanical parameters such as horizontal in situ stresses, fracture aperture and joint spacing are determined based on the borehole displacements during drilling well. In the hybrid ANN-GA model, GA can automatically identify geomechanical parameters as neural inputs from monitored borehole displacements, and the ANN is trained to learn the complex relationship between geomechanical parameters and target borehole displacements. Data from numerical experiments on petroleum wells are used in verification of the proposed computational intelligence method for geomechanics characterization. The study of numerical experiment illustrates that the proposed computational intelligence method has the ability to generate reliable results.

关键词： computational intelligence method artificial neural network genetic algorithm petroleum geomechanics characterization back analysis

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computational intelligence methods for Underwater Magnetic-based Protection Systems

Computational Intelligence Methods for Underwater Magnetic-b...

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International Joint Conference on Neural Networks (IJCNN)

作者： Decherchi, Sergio Leoncini, Davide Gastaldo, Paolo Zunino, Rodolfo Faggioni, Osvaldo Soldani, Maurizio Italian Inst Technol Dept Drug Discovery & Dev Genoa Italy Univ Genoa Dept Physiol & Elect Engn Genoa Italy National Inst Geophys Volcanol Portovener Italy

ISBN: (纸本)9781424496365

Magnetic-based detection technologies for undersea protection systems are very effective in monitoring critical areas where weak signal sources are difficult to identify (e.g. diver intrusion in proximity of the seafloor). The complexity of the involved geomagnetic phenomena and the nature of the target detection strategy require the use of adaptive methods for signal processing. The paper shows that computational intelligence (CI) models can be integrated with those magnetic-based technologies, and presents an effective, reliable system for adaptive undersea protection. Two different CI paradigms are successfully tested for the specific application task: Circular BackPropagation (CBP) and Support Vector Machines (SVMs). Experimental results on real data prove the advantage of the integrated approach over existing conventional methods. Individual CI components and the overall detection system have been verified in real experiments.

关键词： Adaptation models Magnetic sensors Magnetic separation Magnetoacoustic effects Magnetometers SVM Training adaptive signal processing adaptive signal processing adaptive undersea protection backpropagation circular backpropagation computational intelligence method geomagnetic phenomena geomagnetism geophysical signal processing magnetic sensors magnetic-based detection technology national security object detection support vector machines support vector machines target detection strategy underwater magnetic-based protection systems weak signal sources

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Enhancing anomaly detection in electrical consumption profiles through computational intelligence

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ENERGY REPORTS 2024年 11卷 951-962页

作者： Luna-Romero, Santiago Felipe Serrano-Guerrero, Xavier de Souza, Mauren Abreu Escriva-Escriva, Guillermo Pontificia Univ Catolica Parana Grad Program Hlth Technol Curitiba BR-80215901 Brazil Univ Politecn Salesiana Energy Transit Res Grp Cuenca 010103 Ecuador Univ Politecn Valencia Inst Energy Engn Camino Vera S-NEdificio 8E2a Planta Valencia 46022 Spain

The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The "zero energy buildings" idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its "Z" value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.

关键词： Building energy efficiency Anomaly detection computational intelligence method Zero energy buildings Energy consumption patterns Electrical energy consumption

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Water Quantity Prediction Based on Particle Swarm Optimization and Evolutionary Algorithm Using Recurrent Neural Networks

Water Quantity Prediction Based on Particle Swarm Optimizati...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Nian Lai, Shuhua Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA

ISBN: (纸本)9781424496365

Stormwater pollution is one of most important issues that the District of Columbia faces. Urban stormwater pollution can be a large contributor to the water quality problems of many receiving waters, as runoff transports a wide spectrum of pollutants to local receiving waters and their cumulative magnitude is large. Therefore, evaluations of stormwater runoff quantity are necessary to enhance the performance of an assessment operation and develop better water resources management and plan. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on water quantity prediction. Only a limited number of neural networks models were applied to the water quantity monitoring. Therefore, we proposed an Elman style based recurrent neural network on the water quantity prediction. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the stormwater runoff discharge. The USGS real-time water data at Four Mile Run station at Alexandria, VA were used as time series input. The excellent experimental results demonstrated that the proposed method provides a suitable prediction tool for the stormwater runoff monitoring.

关键词： Elman style based recurrent neural network Neural networks Particle swarm optimization Prediction algorithms Rivers Time series analysis Water pollution Water resources computational intelligence method cumulative magnitude environmental science computing evolutionary algorithm evolutionary computation global optimization algorithm hybrid learning algorithm hydrology learning (artificial intelligence) particle swarm optimisation particle swarm optimization recurrent neural nets stormwater pollution stormwater runoff monitoring stormwater runoff quantity time series time series prediction water pollution water quality water quality water quantity monitoring water quantity prediction water resources water resources management

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Orthogonal PSO Algorithm for Solving Ramp Rate Constraints and Prohibited Operating Zones in Smart Grid Applications

Orthogonal PSO Algorithm for Solving Ramp Rate Constraints a...

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International Joint Conference on Neural Networks (IJCNN)

作者： Al Bahrani, Loau Tawfak Patra, Jagdish C. Swinburne Univ Technol Fac Sci Engn & Technol Melbourne Vic Australia

ISBN: (纸本)9781479919598

Recently Particle Swarm Optimization (PSO) algorithms have been used to solve several engineering optimization problems. In this paper, we propose a novel Orthogonal PSO Learning Algorithm (OPSOLA) to solve Economic Dispatch (ED) problems in a Smart Electric Power Grid (SEPG) application. Thermal power "turbine-generator" has several nonlinear characteristics, e.g., the ramp-rate limits, prohibited operating zones and non-smooth cost functions. The proposed OPSOLA has ability to solve such complex problems in SEPG. The OPSOLA utilizes a combined method by adding orthogonality to PSO algorithm. In this combined learning strategy, the particles move and construct a new exemplar to guide the particles to fly more steadily toward the optimum solution. This is accomplished by determining the promising movements of the candidate particle in subsequent iterations based on orthogonality. The OPSOLA is evaluated and tested through a IEEE 6-unit thermal power plant and by comparing the results with several other optimization methods. We found that OPSOLA provides better performance in solving the ED problems in terms of convergence characteristics, quality of solution and execution time.

关键词： computational intelligence method nonlinear optimization economic dispatch orthogonality particle swarm optimization

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Identifying Prestress Loss of Long Span Bridge based on Genetic Algorithm Combined with Neural Network

Identifying Prestress Loss of Long Span Bridge based on Gene...

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International Conference on Artificial intelligence and computational intelligence

作者： Wang Ying Liu Jianxin Zhang Lewen Zhao Renda Shanghai Normal Univ Architecture Engn Coll Shanghai Peoples R China Shandong Univ Geotech & Struct Engn Ctr Jinan Peoples R China Southwest JiaoTong Univ Sch Civil Engn Chengdu Peoples R China

ISBN: (纸本)9780769538167

In recent years, computational intelligence methods are widely used to solve problems in engineering structural field by more and more researchers. In this paper, a method based on combining artificial neural networks (ANN) and genetic algorithm (GA) in identifying the prestress loss of the long span bridge was proposed. A model with some parameters was constructed to access the prestress loss. Based on the differences of the bridge deck elevation of the long span bridge in service between damaged structure and intact structure, the model parameters were identified by the genetic algorithm, and the prestress loss values of different time were obtained. At the same time, the differences of responses were constructed within BP neural networks. In fact, the trained BP neural networks were used as a subroutine in the process of identification using GA. It is demonstrated that the method combining BP neural networks with GA of identification for prestress loss is efficient and the identified results fit well with the actual value.

关键词： Genetic algorithm Artificial neural networks computational intelligence method Identification Bridge structure Prestress loss

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BP Neural Network with Genetic Algorithm Optimization for Prediction of Geo-Stress State from Wellbore Pressures

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INTERNATIONAL JOURNAL OF computational intelligence AND APPLICATIONS 2016年第3期15卷

作者： Zhang, Shike Lv, Jincheng Yuan, Xinsheng Yin, Shunde Anyang Normal Univ Sch Civil Engn & Architecture 436 Xiange Ave Anyang 455000 Henan Peoples R China Univ Wyoming Dept Petr Engn Dept 3295 1000 E Univ Ave Laramie WY 82071 USA

Although lots of ways can be used to estimate geo-stress state, estimation of geo-stress state without knowing geomechanical parameters such as pore pressure, tensile strength and Poisson's ratio, etc., still remains one of the most challenging tasks in geotechnical engineering. The main contribution of this paper is to present a back-propagation neural network (BPNN) with genetic algorithm (GA) optimization to predict the geo-stresses based on wellbore pressures of hydraulic fracturing tests during drilling. In the suggested hybrid model, the BPNN is used establish a mapping between the recording pressures and the geo-stress state. Also the GA is used to carry out the optimization of the weights and thresholds of BPNN model for improving accuracy of prediction. Finally, based on the record pressures in hydraulic fracturing (HF) tests, the BPNN model with genetic algorithm optimization successfully predicts the geo-stresses at the corresponding formation in the event that these parameters such as pore pressure, tensile strength and Poisson's ratio are unavailable. In the meantime, the geo-stress state has been calculated using the theoretical formula by assuming pore pressure and tensile strength of rock mass are known. Then results from theoretical equation, BPNN and BPNN with GA optimization are compared, which shows that the degree accuracy of geo-stresses predicted by using GA-BPNN model is more obviously improvement than the predicted results by the basic BPNN model.

关键词： computational intelligence method artificial neural network genetic algorithm geo-stress prediction hydraulic fracturing test

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Orthogonal PSO Algorithm for Solving Ramp Rate Constraints and Prohibited Operating Zones in Smart Grid Applications

Orthogonal PSO Algorithm for Solving Ramp Rate Constraints a...

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International Joint Conference on Neural Networks

作者： Loau Tawfak Al Bahrani Jagdish C. Patra Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9781479919611

关键词： computational intelligence method economic dispatch nonlinear optimization orthogonality particle swarm optimization orthogonality Particle swarm optimization parametric subharmonic oscillator HEAT RATE Prohibited operating zones Smart grids Operating zones rate limitation

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