Using artifical neutral networks and the genetic algorithm to optimize well-field design: Phase I

Reservoir simulation is a well-established component of reservoir management throughout much of the petroleum industry. Black oil simulators and more complex compositional, thermal, and chemical models are used as forecasting tools in both the day-to-day operational management of production facilities and longer-term field development planning. As yet, however, little use has been made of reservoir simulation coupled with systematic optimization techniques. The main advantage of applying these mathematical tools to decision- making problems is that they are less restricted by human imagination than conventional case-by- case comparisons. As the number of competing engineering, economic, and environmental planning objectives and constraints increases, it becomes difficult for human planners to track complex interactions and select a manageable set of promising scenarios for examination. Using optimization techniques, the search can range over all possible combinations of variables, locating strategies whose effectiveness is not always obvious to planners. Optimization also generates large sets of promising scenarios from which planners can choose

The generalization ability of artificial neural networks in forecasting TCP/IP network traffic trends

Artificial Neural Networks (ANNs) have been used in many fields for a variety of applications, and proved to be reliable. They have proved to be one of the most powerful tools in the domain of forecasting and analysis of various time series. The forecasting of TCP/IP network traffic is an important issue receiving growing attention from the computer networks. By improving upon this task, efficient network traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the ability of the ANN to generalize, i.e. to have the outputs of the ANN approximate target values given inputs that are not in the training set. This has an impact on the quality of forecasts produced by the ANN. Although there are some discussions in the literature regarding the issues that affect network generalization ability, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. This research examined the impact a selection of key design features has on the generalization ability of ANNs. We examined how the size and composition of the network architecture, the size of the training samples, the choice of learning algorithm, the training schedule and the size of the learning rate both individually and collectively affect the ability of an ANN to learn the training data and to generalize well to novel data. To investigate this matter, we empirically conducted several experiments in forecasting a real world TCP/IP network traffic time series and the network performance validated using an independent test set. MATLAB version 7.4.0.287’s Neural Network toolbox version 5.0.2 (R2007a) was used for our experiments. The results are found to be promising in terms of ease of design and use of ANNs. Our results indicate that in contrast to Occam’s razor principle for a single hidden layer an increase in number of hidden neurons produces a corresponding increase in generalization ability of ANNs, however larger networks do not always improve the generalization ability of ANNs even though an increase in number of hidden neurons results in a concomitant rise in network generalization. Also, contradicting commonly accepted guidelines, networks trained with a larger representation of the data, exhibit better generalization than networks trained on smaller representations, even though the larger networks have a significantly greater capacity. Furthermore, the results obtained indicate that the learning rate, momentum, training schedule and choice of learning algorithm have as much a significant effect on ANN generalization ability. A number of conclusions were drawn from the results and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of ANNs in TCP/IP network traffic forecasting. The main contribution of this research lies in the identification of optimal strategies for the use of ANNs in forecasting TCP/IP network traffic trends. Although the information obtained from the tests carried out in this research is specific to the problem considered, it provides users of back-propagation networks with a valuable guide on the behaviour of networks under a wide range of operating conditions. It is important to note that the guidelines accrued from this research are of an assistive and not necessarily restrictive nature to potential ANN modellers

An artificial intelligence platform for design optimization and data analysis: application for fire and ventilation problems

This thesis focuses on the development of novel multi-objective software platforms to assist engineering design and investigation, especially for simulation-based indoor environment problems, which always involve multiple evaluation criteria. In addition, this thesis aims to develop new methods to reduce the computational cost associated with the design process. In modern building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, the majority of these optimization algorithms are focused on single objective optimization procedures and require a large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near-optimal solutions were however sensitive to the chosen value of the weighting factor. In another hand, the computational cost is very heavy in the computer-aided investigation process of reverse engineering problems. Computational Fluid Dynamics (CFD) aided fire investigation is one of the reverse engineering. With the significant growth of the world population, our cities are becoming more and more crowding. In this situation, any fire occurring would cause severe consequences, including property damage and human injuries or even deaths. In assessing the fire cause, the fire origin determination is a crucial step identifying the origin of fire outbreak and the sequential fire and smoke propagation. Traditionally, fire investigators relied upon the visible fire damages at the fire scene to determine the location of fire originated based on their own professional experience. The fire investigation process is however subject to the expert interpretation inherently embedded in the qualitative analyses. In addition, we are living in an era of big data, where lots amount of data are generating every day, especially in engineering field. Traditional analysis methods are not suitable to handle large amount of data quickly and accurately. In contrast, new techniques such as machine learning are able to deal with big data and extract data features. The main body of this thesis is composed of seven chapters, and the details of each chapter are as the followings: The research background and a comprehensive literature review are described in the first two chapters where the research gaps found in the existing literatures are discussed. From Chapter 3 to Chapter 6, the main contributions of this research are demonstrated. In Chapter 3, a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room was developed. In addition, an adaptive sampling procedure was also introduced to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Chapter 4 presents a Multi-fidelity Kriging algorithm to quantitatively determine the fire origin based on the soot deposition patterns predicted by the numerical simulations, which provides an unbiased and fast methodology to assist the fire investigation. A comprehensive multi-objective optimization platform of the ventilation system inside a typical high-speed train (HST) cabin is discussed in Chapter 5, where the NSPSO and the Multi-fidelity Kriging were combined together to reduce computational cost. Chapter 6 demonstrates a successful application of convolutional neural networks (CNN) in vegetation feature analysis to help cut powerline wildfire risk caused by vegetation conduction ignition. Finally, all the contributions in this research are summarised in Chapter 7

Redes neurais em processos siderurgicos : analisador virtual de propriedades metalurgicas do sinter e modelo de predição de qualidade do aço

Orientador: Ana Maria Frattini FiletiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: O baixo acerto simultâneo de temperatura e de teor de carbono ao final do refino é um típico problema das siderúrgicas e é determinado, principalmente, por oscilações de qualidade das matérias primas e pelas limitações de atuação dos modelos matemáticos.O consumo de coque e a permeabilidade dos altos-fomos são parcialmente inferidos por informações das propriedades metalúrgicas das matérias primas: o RDI (Índice de Degradação sob Redução) indica a geração de finos dentro dos altos-fomos e o RI (Índice de Redutibilidade) infere a quantidade de coque necessária para a redução dos óxidos de ferro neste reator. Entretanto, estas propriedades são pouco exploradas pelas empresas pois os ensaios são caros e, particularmente o RI, leva um longo tempo de obtenção da resposta. Estas limitações atribuem oscilações de qualidade ao gusa e, portanto, influem no refino.No processo de refino, o modelo estático define a mistura de matérias primas e a razão de oxigênio necessária ao início do sopro no conversor LD e, após as medidas efetuadas pela sublança, o modelo dinâmico busca acertar a temperatura e a composição do aço objetivadas, através de correções com refrigerantes e oxigênio. Estes modelos são similares em muitas indústrias, mas têm comportamentos diferentes devido as diferenças em níveis de controle.Para resolver estas duas limitações, não existência de análises de propriedades metalúrgicas e restrições em modelos matemáticos (para agir na aciaria), modelos baseados em redes neurais artificiais foram desenvolvidos para prever RDI e RI de sínter (a partir de análises químicas e mineralógicas) e para prever teor de carbono e de temperatura do aço (a partir de informações do processo de refino). No caso da sinterização, foram estudadas, no Centro de Pesquisa da CSN, as influências das características mineralógicas nas propriedades metalúrgicas do sínter. Em seguida, construiu-se o banco de dados com amostras industriais de sínter analisadas em laboratórios.Planejamento e Otimização de Experimentos (POE) foi necessário para planejar as sinterizações piloto, para selecionar amostras industriais (para simplificar as análises químicas, mineralógicas, de RDI e de RI) e para ajustar os parâmetros dos modelos neurais. Para o processo de aciaria, foi utilizado o histórico do processo e o POE para ajustar as redes neurais.A modelagem com diferentes bancos de dados de diferentes trabalhos, possibilitaram, com a organização do POE, obter-se novos resultados no contexto de ajuste dos parâmetros e de arquitetura de rede neural artificial.O analisador virtual atingiu bons resultados de previsão de RDI (precisão de 4,5% contra o permissível de 2 a 4% da norma ISO 4696-1) e de RI (precisão de 4,5% contra 7 a 11 % de tolerância da norma ISO 7215). No caso da aciaria, os modelos neurais foram superiores aos modelos convencionais da CSN. Entretanto, o resultado significativo foi obtido pelo modelo dinâmico via redes neurais, com um acerto simultâneo de 73,3%, contra 44% do modelo dinâmico convencional. Este excelente resultado motivou testes industriais. Para tanto, combinou-se os modelos estático convencional e dinâmico via redes neurais. Após 30 corridas, o acerto simultâneo foi de 83,3%Abstract: The low hitting rate of temperature and carbon content in blow-off is a typical problem in steel companies. There are two main reasons for that: oscillation in raw materials quality and limitation in mathematical models to hit the target.Coke consumption and permeability in the blast furnace can be partially infered from information on metallurgical properties of raw materials: the RDI (Reduction Degradation Index) indicates the quantity of fines produced inside blast furnace and RI (Reducibility Index) infers the quantity of coke necessary to reduce iron oxides in this reactor. However, these properties are not assessed in many industries because the analyses are expensive. Besides, in the case of RI analysis, the results take a long time to be conc1uded. These restrictions make difficult avoiding oscillation in hot metal quality and affect steelmaking process.In the refining process, the static model defines the raw mix materials and the oxygen flow rate necessary to start blowing in the LD converter and, after in-blow measuring by sublance, the dynamic model search to hit the aimed temperature and composition of steel in the blow-off, through corrections with coolants and final volume of oxygen . These models are similar in many industries, but they have different behavior where there are differences in levels of control. Artificial neural network were developed to predict RDI an RI of sinter (from chemical and mineralogical analyses) and to predict carbon content and temperature of steel (from Lot solve these two limitations, no analyses of metallurgical properties and restrictions in mathematical models (to act in steelmaking process), models based on information of the refining process). In the case of sintering, first of all was studied, in the Research Center of CSN, how mineralogical characteristics define the metallurgical properties in sinter. After that, it was necessary to build the database with industrial samples of sinter analyzed in laboratories.Design and Optimization Experiment (DOE) was necessary to plan pilot sintering, to select industries samples of sinter (to simplify the chemical, mineralogical, RDI and RI analyses) and to set the parameters of neural networks models. For the steelmaking process, it was used historical database and DOE techniques only to set neural models.The modeling of artificial neural networks with different database and different size, through DOE, brought new information on optimization of parameters and architecture of these models.The virtual analyzer reached good results predicting RDI (the accuracy was 4,5% versus 2 to 4% of ISO 4696-1 tolerance) and RI (the accuracy was 4,5% versus 7 to 11 % of ISO 7215 tolerance).Artificial neural networks models performed better than CSN' s conventional models in the case of the steelmaking process. But the best result was reached with the neural model acting as dynamic model, with hitting rate equal to 73,3% versus 44% for conventional dynamic model. This excellent result motivated industrial tests. Then, it was combined conventional static and artificial neural network dynamic models. After 30 heats, the hitting rate reached 83,3%DoutoradoSistemas de Processos Quimicos e InformaticaDoutor em Engenharia Químic