A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator

Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainable manufacturing, the development of appropriate indicators needs to be emphasized as compared to other efforts. Regrettably, the existing indicators have several drawbacks that may hamper the accuracy of sustainability performance assessment of an organization. As such, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this study suggests a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which can assist the decision making process of sustainable manufacturing by developing a new indicator mechanism. The KBFANP system comprises of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system incorporates the advantages of Knowledge-Based System Fuzzy Set Theory and Analytic Network Process into a single unified approach as a standardized indicator, which is applicable to all types of problem setting. A prototype of KBFANP system was developed, tested and analyzed on three experimental data sets and two real manufacturing settings. The system was able to provide solutions on the areas that need improvement with different levels of priority. This study also supports the notion of lean and green manufacturing as the elementary foundation of sustainable manufacturing implementation. The proposed KBFANP system can act as an advisory Decision Support System which is beneficial to both academia and industrial practitioners

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

The Bakken petroleum system (Devonian-Mississippian) in the Williston basin of North Dakota and Montana in the United States, and Saskatchewan and Manitoba in Canada is one of the largest unconventional oil plays in North America. The Bakken Formation consists of three members: upper, middle, and lower. Both upper and lower members are shale (source rocks), whereas the middle member (reservoir rock) is composed of mixed lithologies, including sandstone, dolostone, and limestone. Underlying the lower Bakken shale member, the Three Forks Formation is another target for hydrocarbon exploration.;Although the middle Bakken member along with the Three Forks Formation have been the targets for horizontal drilling and hydraulic stimulation throughout the basin, several uncertainties remain, including facies variation due to depositional and diagenetic controls on mineral composition and organic matter content in the Bakken shale members, which could play a significant role in hydrocarbon generation and production. Although the Bakken shale members may look homogeneous in the appearance, they are significantly heterogeneous and complex mixture of quartz, smectite, illite, carbonate, pyrite, and kerogen in varying proportions. Improved characterization of the Bakken shale lithofacies is important to better understand depositional environment, lithofacies distribution, and their potential influence on hydrocarbon production.;The main objective of this work is to investigate vertical and lateral heterogeneities of the Bakken shale lithofacies, based on mineralogy and organic matter richness. Secondly, if the Bakken shale members are composed of different lithofacies, can they be associated with different depositional and/or diagenetic conditions, which could influence source, transportation, and preservation of organic matter and sediment in the Williston basin.;Core data (such as X-ray diffraction, X-ray fluorescence, and Total Organic Carbon content), conventional borehole geophysical logs (such as gamma, resistivity, bulk density, neutron porosity, and photo-electric factor), and advanced petrophysical logs (such as Spectral Gamma and Pulsed Neutron Spectroscopy) are used and integrated together to classify the Bakken shale lithofacies and build models of lithofacies distribution at multiple scales. Usually there are minimal core data, scattered advanced well logs, and ubiquitous conventional well log suites in a petroliferous basin, which hinders lithofacies analysis and petrophysical modeling. Therefore, a significant effort of this work is geared towards developing and applying cost-effective mathematical algorithms (such as Support Vector Machine and Artificial Neural Network etc.) and geostatistical techniques (such as Sequential Indicator Simulation) to classify, predict, and interpolate shale lithofacies with high accuracy, using conventional well log-derived petrophysical parameters from several wells.;The results show that both upper and lower Bakken shale members are vertically and laterally heterogeneous at core, well, and regional scales. Bakken shale members can be classified as five different lithofacies, in terms of mineralogy and organic matter content. Organic-rich shale lithofacies are more dominant than organic-poor shale lithofacies. It appears several factors (such as source of minerals, paleo-redox conditions, organic matter productivity, and preservation etc.) controlled the Bakken shale lithofacies distribution pattern. Silica in the Organic Siliceous Shale (OSS) lithofacies near the basin center is hypothesized to be related to the presence of biogenic silica (e.g. radiolaria), whereas the portion of OSS lithofacies near the basin margin is believed to be associated with eolian action. High organic matter content in the Organic Mudstone (OMD) lithofacies near the basin margin could be interpreted due to the presence of algal matter. The borehole geophysical, petrophysical approaches, and the 3D lithofacies modeling techniques developed in this study can be applied to detailed studies of complex shale formations and exploration of hydrocarbon resources worldwide

Predicting fraud behaviour in online betting

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementFraud isn’t a new issue, there are discussions about this matter since the beginning of commerce. With the advance of the Internet this technique gained strain and became a billion-dollar business. There are many different types of online financial fraud: account takeover; identity theft; chargeback; credit card transactions; etc. Online betting is one of the markets where fraud is increasing every day. In Portugal, the regulation of gambling and online betting was approved in April 2015. In one hand, this legislation made possible the exploration of this business in a controlled and regulated environment, but on the other hand it encouraged customers to develop new ways of fraud. Traditional data analysis used to detect fraud involved different domains such as economics, finance and law. The complexity of these investigations soon became obsolete. Being fraud an adaptive crime, different areas such as Data Mining and Machine Learning were developed to identify and prevent fraud. The main goal of this Project is to develop a predicting model, using a data mining approach and machine learning methods, able to identify and prevent online financial fraud on the Portuguese Betting Market, a new but already strong business

Importância relativa das variáveis preditoras no processo de modelagem da produtividade florestal

Modeling forest growth and production is a major challenge for forest managers due to the large number of variables involved and the importance of the generated estimates for decision making in the forestry enterprise. Several statistical and artificial intelligence methods can be used to verify the importance of variables and their selection for the forest modeling process. This study demonstrates the use of the perturbation method in defining the relative importance of predictor variables (silvicultural, climatic and management) in predicting the productivity of eucalyptus stands at the end of the rotation. Data from 320 eucalyptus plantations located in the north of the State of Minas Gerais, aged over seven years, were used. Precipitation distributed at different ages and soil clay content were the most important variables for the prediction of volume at cutting age.A modelagem do crescimento e produção florestal é um grande desafio para os gestores florestais em função da grande quantidade de variáveis envolvidas e da importância das estimativas geradas para a tomada de decisão no empreendimento florestal. Diversos métodos estatísticos e de inteligência artificial podem ser utilizados visando a verificação da importância das variáveis e seleção das mesmas para o processo de modelagem florestal. Neste estudo é demostrado o uso do método de perturbação em modelos de Redes Neurais Artificiais na definição da importância relativa de variáveis preditoras (silviculturais, climáticas e de manejo) da produtividade de povoamentos de eucalipto ao final da rotação (produção florestal). Foram utilizados dados de 320 talhões de plantios de eucalipto localizados no norte do Estado de Minas Gerais, com idade superior a sete anos. A precipitação distribuída em diversas idades e o teor de argila do solo foram as variáveis de maior importância para a predição do volume na idade de corte

A Type-2 Fuzzy Logic Based System for Augmented Reality Visualisation of Georeferenced Data

Planning of infrastructure's provision and maintenance tasks is commonly done in a planning office using paper maps and desktop applications. However, any infrastructure plan has to be verified on location before being submitted to the responsible authorities. This task is usually accomplished by taking paper maps to the field and annotating them on site, or in the best case, using two-dimensional (2D) maps on mobile devices. Augmented reality (AR) can provide enhanced experiences of real-world situations by overlaying key information and three-dimensional (3D) visualizations when needed, thus supporting decision-making processes. AR could support land surveyors and mobile planners with a graphical overlay of the planned changes, highlighting relevant information and assets in their field of view. This paper presents an AR application, which uses interval type-2 fuzzy logic mechanisms to visualise immersive 3D georeferenced data; supporting planning and designing of infrastructure by directly modifying data to incorporate required changes, without the need of any post-processing. Immersive visual feedback is provided via a head mounted display (HMD), enhancing user's 3D spatial perception of georeferenced data

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Adaptabilidade e estabilidade de genótipos de algodão colorido utilizando lógica Fuzzy

Trabalho de Conclusão de Curso (Graduação)O algodão é uma das principais culturas do país, com grande importância para a economia. A cada ano se busca aumentar a produtividade, a fim de atender a demanda mundial pela fibra. Assim sendo, programas de melhoramento buscam por genótipos cada vez mais produtivos para recomendação. No entanto, devido a grande diversidade de ambientes em que o algodão é cultivado, torna-se necessário estudos sobre a existência de interação genótipo x ambiente e, caso exista, avaliar a adaptabilidade e estabilidade dos genótipos frente aos estímulos ambientais e sua previsibilidade em relação às oscilações do ambiente. Desta forma, este trabalho teve como objetivo verificar a presença de interação genótipo x ambiente para produtividade em genótipos de algodoeiro de fibra colorida e analisar as classificações dadas pelos métodos Centroide e Eberhart e Russel, a partir da lógica Fuzzy, para adaptabilidade e estabilidade. Os experimentos foram realizados na fazenda experimental Capim Branco, em Uberlândia-MG, na safra 2013/2014, 2014/2015, 2015/2016 e 2016/2017. Foram avaliados 12 genótipos de algodoeiro de fibra colorida, sendo 10 do Programa de Melhoramento Genético do Algodoeiro da Universidade Federal de Uberlândia: UFUJP-01, UFUJP-02, UFUJP-05, UFUJP-08, UFUJP-09, UFUJP-10, UFUJP-11, UFUJP-13, UFUJP-16, UFUJP-17 e duas testemunhas: BRS Rubi e BRS Topázio. O delineamento experimental foi de blocos completamente casualizado com três repetições. As análises realizadas foram: teste F para verificação da interação genótipo x ambiente, teste de Scott-Knott, adaptabilidade e estabilidade pelos métodos Centroide e Eberhart e Russel a partir da lógica Fuzzy. A produtividade apresentou interação GxA, que evidencia o comportamento diferencial dos genótipos frente as oscilações ambientais. Os métodos de análise de adaptabilidade e estabilidade apresentaram baixa concordância entre eles (33,3%), porém, a classificação dada utilizando a Lógica Fuzzy condiz bastante com os comportamentos de produtividade dos genótipos

A computational intelligence based prediction model for flight departure delays

Abstract : Flight departure delays are a major problem at OR Tambo International airport (ORTIA). There is a high delay for flights to depart, especially at the beginning of the month and at the end of the month. The increasing demand for flights departing at ORTIA often leads to a negative effect on business deals, individuals’ health, job opportunities and tourists. When flights are delayed departing, travellers are notified at the airport every 30 minutes about the status of the flight and the reason the flight is delayed if it is known. This study aims to construct a flight delays prediction model using machine learning algorithms. The flight departures data were obtained from ORTIAs website timetable for departing flight schedules. The flight departure data for ORTIA to any destination (i.e. Johannesburg (JNB) Airport to Cape Town (CPT)) for South African Airways (SAA) airline was used for this study. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi-Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. A cross-validation (CV) method was used for evaluating the models. The best prediction model was selected by using a confusion matrix. The results showed that the models constructed using Decision Trees (J48) achieved the best prediction for flight departure delays at 67.144%, while Multi-layered Perceptron (MLP) obtained 67.010%, Support Vector Machine (SVM) obtained 66.249% and K-Means Clustering (K-Means) obtained 61.549%. Travellers wishing to travel from ORTIA can predict flight departure delays using this tool. This tool will allow travellers to enter variables such as month, week of month, day of week and time of day. The entered variables will predict the flight departure status by examining target concepts such as On Time, Delayed and Cancelled. The travellers will only be able to predict flight departures status, although they will not have full knowledge of the flight departures volume. In that case, they will depend on the flight information display system (FIDS) board. This study can predict and empower travellers by providing them with a tool that can determine the punctuality of the flights departing from ORTIA.M.Com. (Information Technology Management

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO 2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir.;The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (\u3e2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO 2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities.;Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model\u27s strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction.;Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN\u27s input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values.;Base on the well log analysis, four reservoir subunits within a marine-dominated estuarine depositional system are defined: barrier sand, central bay shale, tidal channels and fluvial channel subunits. A 3-D geological model, which is used to estimate theoretical CO2 sequestration capacity, is constructed with the integration of core data, wireline log data and geological background knowledge. Depending on the proposed 3-D geological model, the best regions for coupled CCUS-EOR are located in southern portions of the field, and the estimated CO2 theoretical storage capacity for Jacksonburg-Stringtown oil field vary between 24 to 383 million metric tons. The estimation results of CO2 sequestration and EOR potential indicate that the Jacksonburg-Stringtown oilfield has significant potential for CO2 storage and value-added EOR