Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Performance Comparisons of EWMA Control Chart Schemes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A hierarchy of adaptive (X)over-bar control charts

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

A multiagent architecture for supervisory and control system

Supervising and controlling the many processes involved in petroleum production is both dangerous and complex. Herein, we propose a multiagent supervisory and control system for handle continuous processes like those in chemical and petroleum industries In its architeture, there are agents responsible for managing data production and analysis, and also the production equipments. Fuzzy controllers were used as control agents. The application of a fuzzy control system to managing an off-shore installation for petroleum production onto a submarine separation process is described. © 2008 IEEE.

Optimal clone identifier based on dynamic rules and process algebra

The rule creation to clone selection in different projects is a hard task to perform by using traditional implementations to control all the processes of the system. The use of an algebraic language is an alternative approach to manage all of system flow in a flexible way. In order to increase the power of versatility and consistency in defining the rules for optimal clone selection, this paper presents the software OCI 2 in which uses process algebra in the flow behavior of the system. OCI 2, controlled by an algebraic approach was applied in the rules elaboration for clone selection containing unique genes in the partial genome of the bacterium Bradyrhizobium elkanii Semia 587 and in the whole genome of the bacterium Xanthomonas axonopodis pv. citri. Copyright© (2009) by the International Society for Research in Science and Technology.

Preliminary results concerning the VSS X- chart with unknow in-control parameters

The VSS X- chart is known to perform better than the traditional X- control chart in detecting small to moderate mean shifts in the process. Many researchers have used this chart in order to detect a process mean shift under the assumption of known parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS X- control chart when the process parameters are estimated and we compare them in the case where the process parameters are assumed known. We draw the conclusion that these performances are quite different when the shift and the number of samples used during the phase I are small. ©2010 IEEE.

Monitoramento da comunidade de plantas daninhas na cana-de-açúcar e da eficiência de controle químico por meio de técnicas de análise multivariada de dados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modelagem e otimização por metodologia de superfícies de resposta: um estudo em arames de aço SAE 9254 para molas automobilísticas

Pós-graduação em Engenharia Mecânica - FEG

Joint economic design of X̄ and R control charts for processes subject to two independent assignable causes

A model for the joint economic design of X̄ and R control charts is developed. This model assumes that the process is subject to two assignable causes. One assignable cause shifts the process mean; the other shifts the process variance. The occurrence of the assignable cause of one kind does not block the occurrence of the assignable cause of another kind. Consequently, a second process parameter can go out-of-control after the first process parameter has gone out-of-control. A numerical study of the cost surface to the model considered has revealed that it is convex, at least in the interest region.

Gráficos de T² para o monitoramento de processos multivariados autocorrelacionados

In which refers to statistical process control, the analysis of univariate cases is not enough for many types of company, being necessary to resort to multivariate cases. Besides, it is usually supposed that the observations are independent. However, the violation of this hypothesis indicates the existence of autocorrelation in the process. In this work, by a basic quantitative approach for an exploratory and experimental research, the study target are the multivariate autocorrelated control charts, using Hotteling T². The ARL values were collected by simulations of a computational program on FORTRAN language, with objective of studying the charts properties, in addition to compare with the

Gráficos de T² para o monitoramento de processos multivariados autocorrelacionados

In which refers to statistical process control, the analysis of univariate cases is not enough for many types of company, being necessary to resort to multivariate cases. Besides, it is usually supposed that the observations are independent. However, the violation of this hypothesis indicates the existence of autocorrelation in the process. In this work, by a basic quantitative approach for an exploratory and experimental research, the study target are the multivariate autocorrelated control charts, using Hotteling T². The ARL values were collected by simulations of a computational program on FORTRAN language, with objective of studying the charts properties, in addition to compare with the

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

CRITICAL POINTS OF THE PRODUCTION PROCESS OF SOLID WOOD FLOORING

The quality concepts represent one of the important factors for the success of organizations and among these concepts the stabilization of the production process contributes to the improvement, waste reduction and increased competitiveness. Thus, this study aimed to evaluate the production process of solid wood flooring on its predictability and capacity, based on its critical points. Therefore, the research was divided into three stages. The first one was the process mapping of the company and the elaboration of flowcharts for the activities. The second one was the identification and the evaluation of the critical points using FMEA (Failure Mode and Effect Analysis) adapted methodology. The third one was the evaluation of the critical points applying the statistical process control and the determination of the process capability for the C-pk index. The results showed the existence of six processes, two of them are critical. In those two ones, fifteen points were considered critical and two of them, related with the dimension of the pieces and defects caused by sandpaper, were selected for evaluation. The productive process of the company is unstable and not capable to produce wood flooring according to the specifications and, therefore these specifications should be reevaluated.