São Caetano do Sul, Brazil
São Caetano do Sul, Brazil

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Kunigk L.,Instituto Maua Of Tecnologia | Jurkiewicz C.H.,Instituto Maua Of Tecnologia
Brazilian Journal of Microbiology | Year: 2010

This study aimed to evaluate the effectiveness of natural casing treatment with nisin and phosphoric acid on control of spoilage microorganisms in vacuum packaged sausages. Ovine casings were dipped in the following baths: 1) 0.1% food grade phosphoric acid; 2) 5.0 mg/L nisin; 3) 0.1% phosphoric acid and 5.0 mg/L nisin; and 4) sterile water (control). The sausages were produced in a pilot plant, stuffed into the pretreated natural casings, vacuum packaged and stored at 4 and 10 °C for 56 days. The experiments were performed according to a full factorial design 23, totalizing 8 treatments that were repeated in 3 blocks. Aerobic plate counts and lactic acid bacteria analysis were conducted at 1, 14, 28, 42 and 56 days of storage. Treatment of casings with phosphoric acid 0.1% alone did not inhibit the growth of lactic acid bacteria and reduced the aerobic plate count by 1 log. The activity of nisin against lactic acid bacteria was enhanced by the addition of phosphoric acid, demonstrating a synergistic effect. Furthermore nisin activity was more evident at lower storage temperature (4 oC). Therefore treatment of the natural casings with nisin and phosphoric acid, combined with low storage temperature, are obstacles that present a potential for controlling the growth of lactic acid bacteria in vacuum packaged sausage.


Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo
Proceedings of the IEEE Conference on Decision and Control | Year: 2014

Identification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. A previous contribution approaches the first using matchable-observable models originally developed in the adaptive control literature. This paper uses direct or derivative-free optimization to design filters. The accuracy, robustness and moderate computational demands of the methods is demonstrated via simulations with randomly generated models. The results obtained are comparable or superior to the best results obtained using standard implementations of the algorithms described in the literature. © 2014 IEEE.


Potts A.S.,University of Sao Paulo | Romano R.A.,Instituto Maua Of Tecnologia | Garcia C.,University of Sao Paulo
Control Engineering Practice | Year: 2014

Model Predictive Control (MPC) Relevant Identification (MRI) methods are a good option for identification, if there is model structure mismatch. Herein a new MRI method, named Enhanced Multistep Prediction Error Method (EMPEM), is proposed. EMPEM combines the best characteristics of others MRI methods in a single algorithm. It was developed to identify either closed-loop or open-loop systems; its convergence and stability make it perform better than the other presented methods. To show the advantages of EMPEM, a comparison is made against two other methods (one MRI and one PEM). The statistical analysis indicates that in the cases studied, the performance and the robustness of the new method is equal or better than the other ones. © 2013 Elsevier Ltd.


Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo | Garcia C.,University of Sao Paulo
IEEE International Conference on Control and Automation, ICCA | Year: 2011

The challenge of identifying multivariable models from input/output data is a subject of great interest, either in scientific works or in industrial plants. The parameterization of multi-output models is considered to be the most crucial task in a MIMO system identification procedure. In this work, a pioneering multivariable identification method is proposed, implemented and evaluated using a linear simulated plant. It is compared to other traditional MIMO identification methods and its results outperformed the other analyzed methods. It was also tested the situation of over-dimensionality of the estimated models, through the use of Hankel singular values and again the proposed method surpassed the other ones in estimating the correct model order. © 2011 IEEE.


Potts A.S.,University of Sao Paulo | Romano R.A.,Instituto Maua Of Tecnologia | Garcia C.,University of Sao Paulo
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2012

Two representative approaches for MRI methods are reported in the literature. The first one is based on the solution of an optimal problem, while the second is based on the prefiltering of the system input and output signals. Each method has advantages and disadvantages according to the process to identify, the length of the prediction horizon or its mathematical implementation. Herein a new MRI method is proposed (C-EMPEM), based on the advantages of both algorithms and on some improvements. The new method was developed to identify either closed-loop or open-loop systems. A comparison is performed among some MRI and PEM methods and the new one proposed, considering a closed-loop system. The results indicate that in the studied case, the performance of the new method is better. © 2012 IFAC.


Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo
Proceedings of the IEEE Conference on Decision and Control | Year: 2013

The selection of a suitable parameterization for the plant model, a crucial step in the identification of multivariable systems, has direct impact on the numerical properties of the parameter estimation algorithm.We employ a parameterization, particularly suitable for system identification, which has the following properties: observability, match-point controllability, and matchability. Using it, the number of model parameters is kept to a minimum, no undesired pole-zero cancellations can appear, and the use of nonlinear estimation is not necessary. We relate this parameterization to classical autoregressive model structures, and propose an algorithm for parameter estimation. By means of Monte Carlo simulations it is found that the algorithm is promising: fewer data points and lower signal-to-noise ratio are required to obtain results that are similar or better than those obtained by traditional methods. © 2013 IEEE.


Romano R.A.,Instituto Maua Of Tecnologia | Dos Santos P.L.,University of Porto | Pait F.,University of Sao Paulo | Perdicoulis T.-P.,UTAD | Ramos J.A.,Nova Southeastern University
Proceedings of the American Control Conference | Year: 2016

In this paper an identification method for state-space LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure. © 2016 American Automatic Control Council (AACC).


Romano R.A.,Instituto Maua Of Tecnologia | Pait F.,University of Sao Paulo | Ferrao R.C.,Instituto Maua Of Tecnologia
Proceedings of the IEEE Conference on Decision and Control | Year: 2016

Identification of linear time-invariant multivariable systems can best be understood as comprising three separate problems: selection of system model structure, filter design, and parameter estimation itself. In previous contributions we approached the first using matchable-observable models originally developed in the adaptive control literature, and used direct or derivative-free optimization to design filters. In this paper we show a simple and effective structure-selection method and demonstrate its accuracy, robustness and moderate computational demands using data from an industrial evaporator and experimental results with a twin rotor. © 2015 IEEE.


Mello L.C.,Instituto Maua Of Tecnologia | De Castro E.R.,Instituto Maua Of Tecnologia | Jermolovicius L.A.,Instituto Maua Of Tecnologia
Quimica Nova | Year: 2016

Although the gas-liquid reactions are employed in many industrial processes, this issue in the most of time is not included in the curriculum of Chemistry and Chemical Engineering courses. This work aims to propose the study of gas-liquid reaction for the degree course, considering a widespread system, the reaction of CO2-NaOH. A reaction mechanism was proposed considering all the intermediate reactions. For each point collected in the outlet solution all the products formed were quantified included residual absorbing solutions. The pH of the outlet solution received a special attention, because with this information was possible to study the controversy that there is in the state of art between some authors in consider the product of the reaction bicarbonate when under conditions of high temperature of or long exposure time of the liquid to gas. For this study was used a continuous stirred-tank reactor in which the gas phase simulate a bubble with a defined size in contact with the liquid phase. The experimental were made by trails under atmospheric pressure, using a feed mixture containing 76% to 69% of CO2 and 24% to 31% of synthetic air and as liquid solution sodium hydroxide 0.015 mol L-1, 0.08 mol L-1, 0.56 mol L-1 and 2.0 mol L-1. The experimental results obtained and the behavior of CO2-NaOH reaction were compared with the data reported in the literature and show a good performance.


The objective of this study was to expand the "Diagnosis of the Packing System" tool, created to evaluate and properly manage the Packing System, adding procedures that allow the evaluation of the environmental impacts, trigged in its operations. The three current indicators are systemic cost, innovation and competences. A fourth indicator was added and comprehends the treatment of solid waste, carbon dioxide emissions, water and energy consumption. Thus, the methodology that was applied in this research was exploratory and qualitative, and it was carried out through a case study, aiming to verify if the company has a systemic perspective in the phases of the development of the product, as well as to improve the tool "Diagnosis of the Packing System" in the process that is related to the chosen product. The results of the research show that it is possible to design a basic scenario, and, through it, to plan some strategies that enable the improvement of the studied system. Through its conception, the tool can be adapted to many different products of the company. The inclusion of the indicator helps the correct strategic attitude towards the world environmental policies that have been created.

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