Identification and analysis of impact factors on the economic feasibility of wind energy investments

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Multivariate GR&R through factor analysis

Several measurement tasks present multivariate nature. In the cases with quality characteristics highly correlated within groups, but with a relatively small correlation between groups, the available multivariate GR&R methods are not suitable to provide a correct interpretation of the results. The present work presents a new multivariate GR&R approach through factor analysis. Factor analysis is a multivariate statistical method which focuses on the explanation of the covariance structure of the data. Through orthogonal rotation of the factors a suitable structure can be achieved with loadings easy to relate the variables to the factors. The proposed multivariate GR&R method through factor analysis is described and applied in the quality evaluation of holes obtained through helical milling process of AISI H13 hardened steel. The method succeeded in achieving a simple structure, with one factor related to the roughness outcomes and other related to the roundness ones, simplifying the gage capability evaluation.publishe

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses

Método dos componentes principais ponderados aplicado em avaliação de sistemas de medição com grandezas correlacionadas

Esta dissertação explora a análise do sistema de medição de características correlacionadas através do estudo de repetitividade e reprodutividade. Quando as correlações entre as características não são significativas, o uso de métodos univariados pode ser satisfatório. Por outro lado, quando as correlações são significativas e estas grandezas são mensuradas pelo mesmo dispositivo de medição, caracteriza-se a necessidade de usar uma abordagem multivariada para avaliação do sistema de medição. A principal contribuição desta pesquisa é a proposta de um método para análise multivariada do sistema de medição baseado em análise de componentes principais. O método denominado Componentes Principais Ponderados (WPC) utiliza como resposta do modelo os escores das componentes principais, ponderados por seus respectivos autovalores. Para comprovar a eficiência deste novo método serão utilizados dados da literatura, simulados e obtidos em laboratório. No geral, o método WPC foi mais robusto que os outros métodos univariado (ANOVA) e multivariados (MANOVA e PCA) para avaliação de sistemas de medição com características correlacionadas. A ponderação das componentes principais por seus respectivos autovalores permitiu que nenhuma informação deixasse de ser incluída no estudo. Mesmos em casos que houve correlações não significativas, o método apresentou índices estimados bem próximo do esperado. Em situações que as características apresentaram correlações altas, as estimativas dos índices convergiram para os valores médios calculados através do método univariado

MDMAIC: um roadmap seis sigma multivariado.

Esta tese explora a aplicação de projetos Seis Sigma para solução de problemas multivariados em processos de manufatura. O principal objetivo desta pesquisa consiste em propor o roadmap MDMAIC (multivariado – definir, medir, analisar, melhorar (improve), controlar), baseado em análise de componentes principais para definir, medir, analisar, melhorar e controlar processos com múltiplas respostas correlacionadas. As principais contribuições deste trabalho não se resumem apenas ao roadmap, mas também em novos métodos para análise de sistema de medição, análise de capabilidade de processo, modelagem e otimização de múltiplas respostas e projeto econômico de cartas de controle. A abordagem que viabilizou a integração das técnicas e ferramentas multivariadas ao MDMAIC consiste em analisar escores ponderados de componentes principais para grupos de variáveis, as quais devem ser separadas de acordo com seus objetivos de otimização. O roadmap proposto e os métodos específicos de cada etapa foram testados e validados através de dados simulados, dados da literatura e dados obtidos em laboratório para o processo de soldagem com arame tubular para o revestimento de chapas de aço carbono ABNT 1020 com aço inoxidável ABNT 316L

Central composite disigns for optimization of the energy factor in 3D printing

This study proposes an optimization strategy to analyze the trade-off between the conflicting objectives of minimizing energy use in 3D printing by fused deposition modeling. The motivation for this work is the need to optimize natural resources, finite in nature, in a more competitive industrial reality and increasingly focused on sustainability, another important point is that energy savings generate improvement in consumption raising organizational profit. The methodologies used were a brief review of the literature and response surface methodology in a CCD experiment. The modeling of the specimen took place through the CAD Fusion 360 software, its development began with the creation of a rectangular 2D sketch, obeying the parameters of 80 mm in its length and 10 mm in width, an Ender 3 printer, yellow PLA, was used following the guidelines set out in ISO 178. Objective of the research is to optimize the manufacturing process using fused deposition modeling, reducing energy consumption (kwh). A complete factorial design was used , as factors: the printing speed (X1), the printing density (X2), layer height (X3) and the layer width (X4), as a response of the experiment were adopted for the manufacturing process, energy (Y). The residue normality tests were performed, with a p-value of 0.170 > 0.05, showing that the data are normal, the VIF below 10 and R-sq (adj) is above 87.16%, the equation has the validated model

Lean, six sigma and sustainability case studies on supply chain management: a systematic literature review

Lean and six sigma approaches contribute to the development of processes with less waste and variability. The sustainability of processes the introduction of routines aimed at environmental well-being. Lean production methods, along with a sustainable supply chain, ensure cooperation, trust, reduced waste and process variation. Evidence suggests that organizations find the integration between these themes challenging. Thus, the objective of the research is to analyze the academic literature on the practical application of lean six sigma in the context of the supply chain. The method used was the critical review of the literature, to build the research gap and subsequently build the proposed methodological framework. As a result, the article presents characteristics of the literature for the application of Green Lean Six Sigma in the supply chain and identifies the tools that are used in the case studies that made up the research database

Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization

Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in association with a model resulting from the insertion of the Entropy function directly into the optimization procedure. First, the DEA model was applied to perform a pre-selection of the assets. Then, assets given as efficient were submitted to the proposed model, resulting from the insertion of the Entropy function into the simplified Sharpe’s portfolio optimization model. As a result, an improved asset participation was provided in the portfolio. In the DEA model, several variables were evaluated and a low value of beta was achieved, guaranteeing greater robustness to the portfolio. Entropy function has provided not only greater diversity but also more feasible asset allocation. Additionally, the proposed method has obtained a better portfolio performance, measured by the Sharpe Ratio, in relation to the comparative methods