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Prof. Rogerio Peruchi
UFPB - Universidade Federal da Paraíba

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Multivariate Analysis
0 Six Sigma
0 DOE
0 SPC - Statistical Process Control
0 MSA - Measurement System Analysis

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multi - objective optimization

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Short Biography

Researcher Level 2 of the CNPq. Adjunct Professor at the Department of Industrial Engineering, Center of Technology, UFPB. Adjunct Professor at the Faculty of Engineering of the UFG at Catalão (2016). Postdoctoral in Industrial Engineering at the UNIFEI (2015). PhD in Industrial Engineering at the UNIFEI (2014) and visiting scholar in the UT at Knoxville (2013). Bachelor’s and Master’s degree in Industrial Engineering (2009 and 2011) at the UNIFEI. Experience in Industrial Engineering from the automotive sector. Research interest in Six Sigma, Quality Management, Statistical Quality Control, Statistical Process Modeling and Multiobjective Optimization

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Journal article
Published: 10 March 2021 in Energies
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Brazil is currently undergoing changes to regulations on distributed generation (DG), specifically for solar energy micro-generation. The changes proposed by the Brazilian Regulatory Agency suggest that only the cost of energy be compensated to investors. The service costs and other charges related to energy tariffs must be divided among consumers. Investors with existing installations and class entities have contested these proposals, calling them “sun-fees”. To date, no scientific papers have been published discussing these changes. The new regulations propose an end to cross subsidies, where all consumers (even those who do not have DG) pay for the transmission and distribution systems. This study compares the economic feasibility of micro-generation before and after implementing the new standards proposed by the regulatory agency. We used data on average electrical energy demand, energy price, and solar radiation in different regions. The national averages were used as a base comparison with other scenarios. The results show that projects are viable for all analyzed scenarios, however, after implementing the proposed changes, the discounted payback time is extended. This, however, does not make projects unfeasible.

ACS Style

Gabriel de Doile; Paulo Rotella Junior; Priscila Carneiro; Rogério Peruchi; Luiz Rocha; Karel Janda; Giancarlo Aquila. Economic Feasibility of Photovoltaic Micro-Installations Connected to the Brazilian Distribution Grid in Light of Proposed Changes to Regulations. Energies 2021, 14, 1529 .

AMA Style

Gabriel de Doile, Paulo Rotella Junior, Priscila Carneiro, Rogério Peruchi, Luiz Rocha, Karel Janda, Giancarlo Aquila. Economic Feasibility of Photovoltaic Micro-Installations Connected to the Brazilian Distribution Grid in Light of Proposed Changes to Regulations. Energies. 2021; 14 (6):1529.

Chicago/Turabian Style

Gabriel de Doile; Paulo Rotella Junior; Priscila Carneiro; Rogério Peruchi; Luiz Rocha; Karel Janda; Giancarlo Aquila. 2021. "Economic Feasibility of Photovoltaic Micro-Installations Connected to the Brazilian Distribution Grid in Light of Proposed Changes to Regulations." Energies 14, no. 6: 1529.

Journal article
Published: 21 February 2021 in Entropy
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The high proportion of CO2/CH4 in low aggregated value natural gas compositions can be used strategically and intelligently to produce more hydrocarbons through oxidative methane coupling (OCM). The main goal of this study was to optimize direct low-value natural gas conversion via CO2-OCM on metal oxide catalysts using robust multi-objective optimization based on an entropic measure to choose the most preferred Pareto optimal point as the problem’s final solution. The responses of CH4 conversion, C2 selectivity, and C2 yield are modeled using the response surface methodology. In this methodology, decision variables, e.g., the CO2/CH4 ratio, reactor temperature, wt.% CaO and wt.% MnO in ceria catalyst, are all employed. The Pareto optimal solution was obtained via the following combination of process parameters: CO2/CH4 ratio = 2.50, reactor temperature = 1179.5 K, wt.% CaO in ceria catalyst = 17.2%, wt.% MnO in ceria catalyst = 6.0%. By using the optimal weighting strategy w1 = 0.2602, w2 = 0.3203, w3 = 0.4295, the simultaneous optimal values for the objective functions were: CH4 conversion = 8.806%, C2 selectivity = 51.468%, C2 yield = 3.275%. Finally, an entropic measure used as a decision-making criterion was found to be useful in mapping the regions of minimal variation among the Pareto optimal responses and the results obtained, and this demonstrates that the optimization weights exert influence on the forecast variation of the obtained response.

ACS Style

Luiz Rocha; Mariana Rocha; Paulo Rotella Junior; Giancarlo Aquila; Rogério Peruchi; Karel Janda; Rômulo Azevêdo. Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion Processes. Entropy 2021, 23, 248 .

AMA Style

Luiz Rocha, Mariana Rocha, Paulo Rotella Junior, Giancarlo Aquila, Rogério Peruchi, Karel Janda, Rômulo Azevêdo. Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion Processes. Entropy. 2021; 23 (2):248.

Chicago/Turabian Style

Luiz Rocha; Mariana Rocha; Paulo Rotella Junior; Giancarlo Aquila; Rogério Peruchi; Karel Janda; Rômulo Azevêdo. 2021. "Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion Processes." Entropy 23, no. 2: 248.

Original article
Published: 30 November 2020 in Journal of Food Process Engineering
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In food industry, high variability is quite common due to seasonality of raw materials and perishable products. Statistical Process Control (SPC) is an effective methodology to reduce variability and to make predictable processes. Literature still lacks of practical approaches of SPC implementation in food operations. Thus, this paper aims to propose a SPC method for quality control of a packaging process of fruit pulp sachets. This method is based on phase I and II study of P control chart for process stability and capability assessment. In phase I, special causes of variation were found and corrected in order to prevent against recurrence. After eliminating special causes of variation, the process capability has been reported as 2 sigma quality level. In phase II, an online monitoring procedure has been implemented and there was no special causes of variation in the packaging operation, assuring process stability. Practical applications The proposed method provides to the production supervisor a very powerful and straightforward tool for quality control. Basically, if at any daily production the fraction of defectives goes below lower control limit or above upper control limit, immediately, the supervisor has to conduct the procedure for detecting special cause of variation. The proposed method was very successful for assuring process stability during online monitoring. The fraction of defectives items was remained in statistical control while the ongoing monitoring was being performed.

ACS Style

José Flávio Rique Junior; Rogério Santana Peruchi; Paulo Rotella Junior; Robson Bruno Dutra Pereira. Statistical process control of the vertical form, fill and seal packaging machine in food industry. Journal of Food Process Engineering 2020, 44, 1 .

AMA Style

José Flávio Rique Junior, Rogério Santana Peruchi, Paulo Rotella Junior, Robson Bruno Dutra Pereira. Statistical process control of the vertical form, fill and seal packaging machine in food industry. Journal of Food Process Engineering. 2020; 44 (2):1.

Chicago/Turabian Style

José Flávio Rique Junior; Rogério Santana Peruchi; Paulo Rotella Junior; Robson Bruno Dutra Pereira. 2020. "Statistical process control of the vertical form, fill and seal packaging machine in food industry." Journal of Food Process Engineering 44, no. 2: 1.

Journal article
Published: 02 November 2020 in Measurement
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This research presents an experimental study for measurement system analysis of angle of repose in fertilizers with variable granulometry (1.5 mm < d50 < 5.0 mm). In the literature, there are different procedures for determining this angle of repose, which often result on a variety of measurements for one measurand. Thus, the main objective of this work is to propose a method for measurement system validation of angle of repose. The statistical validation was based on Nested Gage Repeatability and Reproducibility (NGR&R) and the experiment was conceived through a technical apparatus built to measure the angle of repose of fertilizers. Experiments was conducted by using available resources in a typical industrial laboratory of fertilizer characterization. Among the measuring procedures, the fixed funnel has been used and an image analyzer software has been adopted to gather the data. The experiment was planned with 3 (three) operators, 10 (ten) samples with distinct granulometries and 3 (three) replicates. The result analysis has shown that the measurement error through the first measuring procedure was deemed unacceptable. After investigating the root cause of measurement error, a new standard measurement procedure was proposed. The new procedure was able to measure the angle of repose precisely, reducing measurement error from 59.93% to 4.82%.

ACS Style

I.S.B. Ferreira; R.S. Peruchi; N.J. Fernandes; P. Rotella Junior. Measurement system analysis in angle of repose of fertilizers with distinct granulometries. Measurement 2020, 170, 108681 .

AMA Style

I.S.B. Ferreira, R.S. Peruchi, N.J. Fernandes, P. Rotella Junior. Measurement system analysis in angle of repose of fertilizers with distinct granulometries. Measurement. 2020; 170 ():108681.

Chicago/Turabian Style

I.S.B. Ferreira; R.S. Peruchi; N.J. Fernandes; P. Rotella Junior. 2020. "Measurement system analysis in angle of repose of fertilizers with distinct granulometries." Measurement 170, no. : 108681.

Review paper
Published: 24 October 2020 in International Journal of Energy Research
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The growing energy demand in the world and the concern for environmentally damaging energy sources have led to an increased interest in seeking alternative renewable energy sources, such as wind energy. Furthermore, choosing effective locations for wind power plants has become a key issue in project planning. However, prior to implementation, such projects should be confirmed as economically viable. This article is a systematic review of the literature carried out with the aim to identify the main factors that impact the economic feasibility of wind energy investments. The search was performed in the ISI Web of Science (WoS) electronic database, from which 120 papers were extracted after a selection process, and were analyzed individually. As a result of the review analysis, 23 factors that have an impact on feasibility analysis were identified and organized in five categories: location (surface roughness, turbine location), economic (investment costs, operation and maintenance costs, avoided energy cost, depreciation, land rent), political (interest rates and taxes, energy sales price, inflation, financing conditions), climatic (wind speed, air density, temperature, air pressure), and technical (turbine height, installed wind power, lifetime, efficiency, rotor diameter, operation time, number of turbine blades, construction time). These factors can directly impact the cost of capital and/or energy production, affecting the economic viability of wind farms. In the last decade, there has been an exponential growth in publications about economic feasibility of wind investments. The wind investments growth has been accompanied by financial studies about this subject. This study provides insights on the main variables used in wind energy feasibility studies. The results may assist researchers and investors to identify the key parameters that are being examined in the literature, and to evaluate which ones should be considered in their study to ensure a sustainable development of power generation through the wind source.

ACS Style

Rômulo De Oliveira Azevêdo; Paulo Rotela Junior; Gianfranco Chicco; Giancarlo Aquila; Luiz Célio Souza Rocha; Rogério Santana Peruchi. Identification and analysis of impact factors on the economic feasibility of wind energy investments. International Journal of Energy Research 2020, 45, 3671 -3697.

AMA Style

Rômulo De Oliveira Azevêdo, Paulo Rotela Junior, Gianfranco Chicco, Giancarlo Aquila, Luiz Célio Souza Rocha, Rogério Santana Peruchi. Identification and analysis of impact factors on the economic feasibility of wind energy investments. International Journal of Energy Research. 2020; 45 (3):3671-3697.

Chicago/Turabian Style

Rômulo De Oliveira Azevêdo; Paulo Rotela Junior; Gianfranco Chicco; Giancarlo Aquila; Luiz Célio Souza Rocha; Rogério Santana Peruchi. 2020. "Identification and analysis of impact factors on the economic feasibility of wind energy investments." International Journal of Energy Research 45, no. 3: 3671-3697.

Review
Published: 02 September 2020 in Sustainability
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The introduction of environmental impact targets around the world has highlighted the need to adopt alternative sources of energy, which can supply the demand and mitigate the damage caused to the environment. Solar energy is one of the main sources of alternative energy, and is considered an abundant source of clean energy. However, to facilitate and encourage investors interested in the installation of photovoltaic energy systems for electricity production, it is essential to evaluate the factors that impact the economic viability of the projects. Therefore, the objective of this research is to present a systematic analytical framework, in order to identify and analyze the main factors that impact the financial feasibility of projects for the installation of photovoltaic energy plants. For this purpose, a systematic literature review was carried out, analyzing the main studies related to the topic and identifying the main factors that may financially affect investments in photovoltaic energy systems. From this review, 29 influencing factors were identified and separated into five categories, namely, location, economic, political, climatic and environmental, and technical factors. The main factors highlighted are the investment cost, power generation, operation and maintenance costs, solar radiation, lifetime, energy tariff, efficiency, electricity consumption, and interest and taxes. The results may assist policy makers, investors, researchers, and other stakeholders to identify the key factors that are being examined in the literature, and to evaluate which ones should be considered in their study to ensure the sustainable development of power generation through the solar source.

ACS Style

Rômulo De Oliveira Azevêdo; Paulo Rotela Junior; Luiz Rocha; Gianfranco Chicco; Giancarlo Aquila; Rogério Peruchi. Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments. Sustainability 2020, 12, 7173 .

AMA Style

Rômulo De Oliveira Azevêdo, Paulo Rotela Junior, Luiz Rocha, Gianfranco Chicco, Giancarlo Aquila, Rogério Peruchi. Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments. Sustainability. 2020; 12 (17):7173.

Chicago/Turabian Style

Rômulo De Oliveira Azevêdo; Paulo Rotela Junior; Luiz Rocha; Gianfranco Chicco; Giancarlo Aquila; Rogério Peruchi. 2020. "Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments." Sustainability 12, no. 17: 7173.

Journal article
Published: 08 June 2020 in IEEE Access
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Hard turning processes have several advantages against traditional turning. Improved surface integrity and short process time are some examples. Surface integrity is one of the most important issues in modeling of machining processes. Multiple roughness parameters are observed in relation to several controllable and uncontrollable input parameters. Since these multiple roughness parameters are correlated, multivariate methods are the most suitable approach for process control. This research aims to propose a method for assessing stability and performance of multivariate processes in the presence of noise variables. A hybrid method based on design of experiment, statistical process control and principal component analysis was applied to AISI 52100 hardened steel turning. The process performance index was obtained within the range of 0.18 to 1.11. The best process performance was achieved taking cutting speed of 170m/min and lubricating fluid flow of 3 L/min.

ACS Style

George Evangelista; Rogerio Santana Peruchi; Tarcisio Goncalves Brito; Paulo Rotela Junior; Luiz Celio Souza Rocha. A Multivariate Statistical Quality Control of AISI 52100 Hardened Steel Turning. IEEE Access 2020, 8, 109092 -109104.

AMA Style

George Evangelista, Rogerio Santana Peruchi, Tarcisio Goncalves Brito, Paulo Rotela Junior, Luiz Celio Souza Rocha. A Multivariate Statistical Quality Control of AISI 52100 Hardened Steel Turning. IEEE Access. 2020; 8 ():109092-109104.

Chicago/Turabian Style

George Evangelista; Rogerio Santana Peruchi; Tarcisio Goncalves Brito; Paulo Rotela Junior; Luiz Celio Souza Rocha. 2020. "A Multivariate Statistical Quality Control of AISI 52100 Hardened Steel Turning." IEEE Access 8, no. : 109092-109104.

Journal article
Published: 17 April 2020 in IEEE Access
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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.

ACS Style

Liviam Soares Lacerda; Paulo Rotella Junior; Rogerio Santana Peruchi; Gianfranco Chicco; Luiz Celio Souza Rocha; Giancarlo Aquila; Luiz Moreira Coelho Junior. Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility. IEEE Access 2020, 8, 73931 -73946.

AMA Style

Liviam Soares Lacerda, Paulo Rotella Junior, Rogerio Santana Peruchi, Gianfranco Chicco, Luiz Celio Souza Rocha, Giancarlo Aquila, Luiz Moreira Coelho Junior. Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility. IEEE Access. 2020; 8 (99):73931-73946.

Chicago/Turabian Style

Liviam Soares Lacerda; Paulo Rotella Junior; Rogerio Santana Peruchi; Gianfranco Chicco; Luiz Celio Souza Rocha; Giancarlo Aquila; Luiz Moreira Coelho Junior. 2020. "Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility." IEEE Access 8, no. 99: 73931-73946.

Journal article
Published: 28 February 2020 in Acta Scientiarum. Technology
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The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study.

ACS Style

Leandro Duarte Pereira; Pedro Paulo Balestrassi; Vinicius De Carvalho Paes; Anderson Paulo De Paiva; Rogério Santana Peruchi; Ronã Rinston Amauri Mendes. Short-term forecasting models for automated data backup system: segmented regression analysis. Acta Scientiarum. Technology 2020, 42, e46073 -e46073.

AMA Style

Leandro Duarte Pereira, Pedro Paulo Balestrassi, Vinicius De Carvalho Paes, Anderson Paulo De Paiva, Rogério Santana Peruchi, Ronã Rinston Amauri Mendes. Short-term forecasting models for automated data backup system: segmented regression analysis. Acta Scientiarum. Technology. 2020; 42 ():e46073-e46073.

Chicago/Turabian Style

Leandro Duarte Pereira; Pedro Paulo Balestrassi; Vinicius De Carvalho Paes; Anderson Paulo De Paiva; Rogério Santana Peruchi; Ronã Rinston Amauri Mendes. 2020. "Short-term forecasting models for automated data backup system: segmented regression analysis." Acta Scientiarum. Technology 42, no. : e46073-e46073.

Journal article
Published: 11 February 2020 in IEEE Access
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DMAIC (define, measure, analyze, improve and control) is one of the most utilized methods for guiding practitioners in the decision-making process of quality improvement projects. Industrial processes commonly deal with multiple critical-to-quality (CTQ) characteristics. When these characteristics are correlated, multivariate statistical techniques should be applied. This paper aims to propose a domainspecific Six Sigma method, the MDMAIC (multivariate DMAIC). The new stepwise procedure helps practitioners not only to reduce problem dimension but also to take account of the correlation structure among CTQs during the decision-making process. Principal component analysis has been applied for assessing the measurement system, analyzing process stability and capability, as well as modeling and optimizing multivariate manufacturing processes. A hardened steel turning case has been presented for proposal validation. The result analysis has shown that the MDMAIC was very successful in leading the practitioner during the steps and phases of the quality improvement project. The multivariate capability index of the enhanced process emphasized the substantial economic improvement.

ACS Style

Rogerio Santana Peruchi; Paulo Rotela Junior; Tarcisio G. Brito; Anderson P. Paiva; Pedro P. Balestrassi; Lavinia M. Mendes Araujo. Integrating Multivariate Statistical Analysis Into Six Sigma DMAIC Projects: A Case Study on AISI 52100 Hardened Steel Turning. IEEE Access 2020, 8, 34246 -34255.

AMA Style

Rogerio Santana Peruchi, Paulo Rotela Junior, Tarcisio G. Brito, Anderson P. Paiva, Pedro P. Balestrassi, Lavinia M. Mendes Araujo. Integrating Multivariate Statistical Analysis Into Six Sigma DMAIC Projects: A Case Study on AISI 52100 Hardened Steel Turning. IEEE Access. 2020; 8 (99):34246-34255.

Chicago/Turabian Style

Rogerio Santana Peruchi; Paulo Rotela Junior; Tarcisio G. Brito; Anderson P. Paiva; Pedro P. Balestrassi; Lavinia M. Mendes Araujo. 2020. "Integrating Multivariate Statistical Analysis Into Six Sigma DMAIC Projects: A Case Study on AISI 52100 Hardened Steel Turning." IEEE Access 8, no. 99: 34246-34255.

Journal article
Published: 14 June 2019 in Energies
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Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives may allow decreasing in the investment amount for wind power. Based on this, incentives programs for the production of clean energy include cheaper purchase of wind turbines, lower taxing and financing rates, can make wind power more profitable and attractive.

ACS Style

Paulo Rotela Junior; Eugenio Fischetti; Victor G. Araújo; Rogério S. Peruchi; Giancarlo Aquila; Luiz Célio S. Rocha; Liviam S. Lacerda. Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis. Energies 2019, 12, 2281 .

AMA Style

Paulo Rotela Junior, Eugenio Fischetti, Victor G. Araújo, Rogério S. Peruchi, Giancarlo Aquila, Luiz Célio S. Rocha, Liviam S. Lacerda. Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis. Energies. 2019; 12 (12):2281.

Chicago/Turabian Style

Paulo Rotela Junior; Eugenio Fischetti; Victor G. Araújo; Rogério S. Peruchi; Giancarlo Aquila; Luiz Célio S. Rocha; Liviam S. Lacerda. 2019. "Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis." Energies 12, no. 12: 2281.

Journal article
Published: 10 April 2019 in Measurement
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Measurement system analysis has a pivotal role in assessing the measuring error when conducting empirical studies. Gage Repeatability and Reproducibility (GR&R) studies are classic methods applied to evaluate measurement system adequacy for a particular application. However, previous published studies are limited to determine the source of measuring error related to not only reproducibility but also repeatability. Hence, the objective of this research is to propose new indicators for measurement error detection due to both repeatability and reproducibility variation in GR&R studies. These indicators were calculated using standardized scores from the analysis of variance. The proposed procedure has been applied not only to literature data but also to a stainless steel cladding process on surfaces of carbon steel. While literature method has detected only reproducibility error, the proposed procedure were able to detect both reproducibility and repeatability sources of measuring error.

ACS Style

Lavínia Maria Mendes Araújo; Rafael Gomes Nobrega Paiva; Rogério Santana Peruchi; Paulo Rotela Junior; José Henrique De Freitas Gomes. New indicators for measurement error detection in GR&R studies. Measurement 2019, 140, 557 -564.

AMA Style

Lavínia Maria Mendes Araújo, Rafael Gomes Nobrega Paiva, Rogério Santana Peruchi, Paulo Rotela Junior, José Henrique De Freitas Gomes. New indicators for measurement error detection in GR&R studies. Measurement. 2019; 140 ():557-564.

Chicago/Turabian Style

Lavínia Maria Mendes Araújo; Rafael Gomes Nobrega Paiva; Rogério Santana Peruchi; Paulo Rotela Junior; José Henrique De Freitas Gomes. 2019. "New indicators for measurement error detection in GR&R studies." Measurement 140, no. : 557-564.

Journal article
Published: 01 February 2018 in Measurement
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ACS Style

Giancarlo Aquila; Rogério Santana Peruchi; Paulo Rotella Junior; Luiz Rocha; Anderson Rodrigo de Queiroz; Edson De Oliveira Pamplona; Pedro Paulo Balestrassi. Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system. Measurement 2018, 115, 217 -222.

AMA Style

Giancarlo Aquila, Rogério Santana Peruchi, Paulo Rotella Junior, Luiz Rocha, Anderson Rodrigo de Queiroz, Edson De Oliveira Pamplona, Pedro Paulo Balestrassi. Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system. Measurement. 2018; 115 ():217-222.

Chicago/Turabian Style

Giancarlo Aquila; Rogério Santana Peruchi; Paulo Rotella Junior; Luiz Rocha; Anderson Rodrigo de Queiroz; Edson De Oliveira Pamplona; Pedro Paulo Balestrassi. 2018. "Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system." Measurement 115, no. : 217-222.

Original article
Published: 13 December 2017 in The International Journal of Advanced Manufacturing Technology
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Hard turning operations have been extensively investigated owing to their ability to reduce process cycle time, increase process flexibility, ensure high-dimensional accuracy, and enable machining without a cutting fluid. These processes are rather common for dealing with multiple quality characteristics. To evaluate the process ability and meet customer needs, multivariate statistical techniques are recommended for estimating the capability indices. Principal component analysis can be applied to reducing the problem dimension and estimate process capability indices. The aim of this study was to assess the capability of AISI 52100 hardened steel turning operations and achieve process specifications. Multivariate process capability indices were calculated to assess five roughness parameters of surface finishing. By using a weighted approach of principal component analysis, a new method is proposed for estimating the process capability indices. The results highlight not only the relevance of conducting a multivariate capability analysis in the case of actual machining but also how successfully the proposed method was performed.

ACS Style

R. S. Peruchi; Paulo Rotella Junior; T. G. Brito; J. J. J. Largo; P. P. Balestrassi. Multivariate process capability analysis applied to AISI 52100 hardened steel turning. The International Journal of Advanced Manufacturing Technology 2017, 95, 3513 -3522.

AMA Style

R. S. Peruchi, Paulo Rotella Junior, T. G. Brito, J. J. J. Largo, P. P. Balestrassi. Multivariate process capability analysis applied to AISI 52100 hardened steel turning. The International Journal of Advanced Manufacturing Technology. 2017; 95 (9-12):3513-3522.

Chicago/Turabian Style

R. S. Peruchi; Paulo Rotella Junior; T. G. Brito; J. J. J. Largo; P. P. Balestrassi. 2017. "Multivariate process capability analysis applied to AISI 52100 hardened steel turning." The International Journal of Advanced Manufacturing Technology 95, no. 9-12: 3513-3522.

Journal article
Published: 15 September 2017 in Entropy
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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.

ACS Style

Paulo Rotela Junior; Luiz Célio Souza Rocha; Giancarlo Aquila; Pedro Paulo Balestrassi; Rogério Santana Peruchi; Liviam Soares Lacerda. Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization. Entropy 2017, 19, 352 .

AMA Style

Paulo Rotela Junior, Luiz Célio Souza Rocha, Giancarlo Aquila, Pedro Paulo Balestrassi, Rogério Santana Peruchi, Liviam Soares Lacerda. Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization. Entropy. 2017; 19 (9):352.

Chicago/Turabian Style

Paulo Rotela Junior; Luiz Célio Souza Rocha; Giancarlo Aquila; Pedro Paulo Balestrassi; Rogério Santana Peruchi; Liviam Soares Lacerda. 2017. "Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization." Entropy 19, no. 9: 352.

Journal article
Published: 26 August 2016 in Acta Scientiarum. Technology
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Resumo This paper aimed to compare the performance of multivariate GR&R (gage repeatability and reproducibility) studies based on PCA (principal component analysis) and Manova (multivariate analysis of variance) methods. To estimate the multivariate gauge index, geometric and arithmetic means have been implemented with and without weighting strategies. Bootstrap confidence interval based on BCa (bias-corrected and accelerated) method has been adopted to determine multivariate gauge index adequacy. This confidence interval was calculated for the mean of univariate gauge indices estimated from each quality characteristic. The result analyses have shown that weighted approaches provided the best estimates of gauge index in multivariate GR&R studies.

ACS Style

Rogério Santana Peruchi; Nilson José Fernandes; Pedro Paulo Balestrassi; Anderson Paulo Paiva; Helio Maciel Junior. Comparisons of multivariate GR&R methods using bootstrap confidence interval. Acta Scientiarum. Technology 2016, 38, 489 .

AMA Style

Rogério Santana Peruchi, Nilson José Fernandes, Pedro Paulo Balestrassi, Anderson Paulo Paiva, Helio Maciel Junior. Comparisons of multivariate GR&R methods using bootstrap confidence interval. Acta Scientiarum. Technology. 2016; 38 (4):489.

Chicago/Turabian Style

Rogério Santana Peruchi; Nilson José Fernandes; Pedro Paulo Balestrassi; Anderson Paulo Paiva; Helio Maciel Junior. 2016. "Comparisons of multivariate GR&R methods using bootstrap confidence interval." Acta Scientiarum. Technology 38, no. 4: 489.

Journal article
Published: 01 March 2016 in Measurement
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Measurement error is an unavoidable source of variation in any decision-making process based on experimental research. Components of variation due to measurement system and manufacturing process must be estimated and special causes of variation should be reduced whenever possible. GR&R (gage repeatability and reproducibility) studies quantify these sources of variation by using analysis of variance. The main contribution of this paper is to conjoin GR&R and the multiple comparisons method of Scott-Knott in order to help practitioners identifying special causes of variation in empirical studies. Stainless steel cladding process has been evaluated to validate the proposed procedure. The experimental findings have shown that the well-structured method based on Scott-Knott test was effective in indicating the source of error due to reproducibility.

ACS Style

Robson Bruno Dutra Pereira; Rogerio Peruchi; Anderson Paulo de Paiva; Sebastião Carlos Da Costa; João Ferreira. Combining Scott-Knott and GR&R methods to identify special causes of variation. Measurement 2016, 82, 135 -144.

AMA Style

Robson Bruno Dutra Pereira, Rogerio Peruchi, Anderson Paulo de Paiva, Sebastião Carlos Da Costa, João Ferreira. Combining Scott-Knott and GR&R methods to identify special causes of variation. Measurement. 2016; 82 ():135-144.

Chicago/Turabian Style

Robson Bruno Dutra Pereira; Rogerio Peruchi; Anderson Paulo de Paiva; Sebastião Carlos Da Costa; João Ferreira. 2016. "Combining Scott-Knott and GR&R methods to identify special causes of variation." Measurement 82, no. : 135-144.

Journal article
Published: 01 March 2016 in Computers & Industrial Engineering
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Normal Boundary Intersection (NBI) is traditionally used to generate equally spaced and uniformly spread Pareto Frontiers for multi-objective optimization programming (MOP). This method tends to fail, however, when correlated objective functions must be optimized using Robust Parameter Designs (RPD). In such multi-objective optimization programming, there can be reached impractical optima and non-convex frontiers. To reverse this shortcoming, it is common to apply Principal Component Analysis (PCA), which provides uncorrelated objective functions. The aim of this paper is to combine the Robust Parameter Designs, Principal Component Analysis, and Normal Boundary Intersection approaches into a novel method called RPD-MNBI. This approach finds equally spaced Pareto optimal frontiers that are capable of minimizing noise variables’ effects. To validate this proposal, this study investigates an end milling process. The most important empirical finding is that the original correlation structure is preserved. On the other hand, the Weighted Sums and Normal Boundary Intersection-Mean Square Error methods, modify the process behavior, resulting in unreal optima. Finally, confirmation runs using an L9 Taguchi design were performed for 10%, 50%, and 90% weights. The proposed method provides process robustness according to confidence intervals for both mean and standard deviation.

ACS Style

Luiz Gustavo Dias Lopes; Tarcísio Gonçalves Brito; Anderson Paulo Paiva; Rogério Santana Peruchi; Pedro Paulo Balestrassi. Robust parameter optimization based on multivariate normal boundary intersection. Computers & Industrial Engineering 2016, 93, 55 -66.

AMA Style

Luiz Gustavo Dias Lopes, Tarcísio Gonçalves Brito, Anderson Paulo Paiva, Rogério Santana Peruchi, Pedro Paulo Balestrassi. Robust parameter optimization based on multivariate normal boundary intersection. Computers & Industrial Engineering. 2016; 93 ():55-66.

Chicago/Turabian Style

Luiz Gustavo Dias Lopes; Tarcísio Gonçalves Brito; Anderson Paulo Paiva; Rogério Santana Peruchi; Pedro Paulo Balestrassi. 2016. "Robust parameter optimization based on multivariate normal boundary intersection." Computers & Industrial Engineering 93, no. : 55-66.

Data article
Published: 15 January 2016 in Data in Brief
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In this Data in Brief paper, a central composite experimental design was planned to collect the surface roughness of an end milling operation of AISI 1045 steel. The surface roughness values are supposed to suffer some kind of variation due to the action of several factors. The main objective here was to present a multivariate experimental design and data collection including control factors, noise factors, and two correlated responses, capable of achieving a reduced surface roughness with minimal variance. Lopes et al. (2016) [1], for example, explores the influence of noise factors on the process performance.

ACS Style

Luiz Gustavo Dias Lopes; Tarcísio Gonçalves De Brito; Anderson Paulo De Paiva; Rogério Santana Peruchi; Pedro Paulo Balestrassi. Experimental Design and Data collection of a finishing end milling operation of AISI 1045 steel. Data in Brief 2016, 6, 609 -613.

AMA Style

Luiz Gustavo Dias Lopes, Tarcísio Gonçalves De Brito, Anderson Paulo De Paiva, Rogério Santana Peruchi, Pedro Paulo Balestrassi. Experimental Design and Data collection of a finishing end milling operation of AISI 1045 steel. Data in Brief. 2016; 6 ():609-613.

Chicago/Turabian Style

Luiz Gustavo Dias Lopes; Tarcísio Gonçalves De Brito; Anderson Paulo De Paiva; Rogério Santana Peruchi; Pedro Paulo Balestrassi. 2016. "Experimental Design and Data collection of a finishing end milling operation of AISI 1045 steel." Data in Brief 6, no. : 609-613.

Journal article
Published: 12 May 2015 in Computers & Operations Research
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The modern portfolio theory has been trying to determine how an investor might allocate assets among the possible investments options. Since the seminal contribution provided by Harry Markowitz’s theory of portfolio selection, several other tools and procedures have been proposed to deal with return-risk trade-off. Furthermore, diversification across sources of returns and risks based on entropy indexes is another pivotal aspect in portfolio management. An efficient approach to model these portfolio properties with the proportion of each asset can be obtained according to mixture design of experiments. Desirability method can be applied to optimize this nonlinear multiobjective problem. Nevertheless, a tuning procedure is required, since preference articulation parameters in desirability algorithm are unknown a priori. As a result, a computer-aided desirability tuning method is proposed to find an optimal portfolio with time series of returns and risks modeled by ARMA–GARCH models. To assess the proposal feasibility, the method is tested with a heteroskedastic dataset formed by weekly world crude oil spot prices and returns. Computer-aided desirability tuning was able to enhance the global desirability by 79% in relation to the result with no tuning procedure.

ACS Style

R.R.A. Mendes; A.P. Paiva; R.S. Peruchi; P.P. Balestrassi; R.C. Leme; Messias Borges Silva. Multiobjective portfolio optimization of ARMA–GARCH time series based on experimental designs. Computers & Operations Research 2015, 66, 434 -444.

AMA Style

R.R.A. Mendes, A.P. Paiva, R.S. Peruchi, P.P. Balestrassi, R.C. Leme, Messias Borges Silva. Multiobjective portfolio optimization of ARMA–GARCH time series based on experimental designs. Computers & Operations Research. 2015; 66 ():434-444.

Chicago/Turabian Style

R.R.A. Mendes; A.P. Paiva; R.S. Peruchi; P.P. Balestrassi; R.C. Leme; Messias Borges Silva. 2015. "Multiobjective portfolio optimization of ARMA–GARCH time series based on experimental designs." Computers & Operations Research 66, no. : 434-444.