Showing papers by "Salvatore Graziani published in 2007"

A nonlinear model for ionic polymer metal composites as actuators

Abstract: This paper introduces a comprehensive nonlinear dynamic model of motion actuators based on ionic polymer metal composites (IPMCs) working in air. Significant quantities ruling the acting properties of IPMC-based actuators are taken into account. The model is organized as follows. As a first step, the dependence of the IPMC absorbed current on the voltage applied across its thickness is taken into account; a nonlinear circuit model is proposed to describe this relationship. In a second step the transduction of the absorbed current into the IPMC mechanical reaction is modelled. The model resulting from the cascade of both the electrical and the electromechanical stages represents a novel contribution in the field of IPMCs, capable of describing the electromechanical behaviour of these materials and predicting relevant quantities in a large range of applied signals. The effect of actuator scaling is also investigated, giving interesting support to the activities involved in the design of actuating devices based on these novel materials. Evidence of the excellent agreement between the estimations obtained by using the proposed model and experimental signals is given.

Virtual Instruments Based on Stacked Neural Networks to Improve Product Quality Monitoring in a Refinery

Abstract: In this paper, a virtual instrument for the estimation of octane number in the gasoline produced by refineries is introduced. The instrument was designed with the aim of replacing measuring hardware during maintenance operations. The virtual instrument is based on a nonlinear moving average model, implemented by using multilayer perceptron neural networks. Stacking approaches are adopted to improve the estimation performance of the instrument. Classical linear algorithms of model aggregation are compared in the paper with a nonlinear strategy, based on the neural combination of a set of first-level neural estimators. The validity of the proposed approach is verified by comparison with the performance of both linear and nonlinear modeling techniques. The designed virtual instrument has been implemented by a large refinery in Sicily, which supplied the data used during the design phase

A Comparative Analysis of the Influence of Methods for Outliers Detection on the Performance of Data Driven Models

Abstract: In this paper we describe, test, and compare the performance of a number of techniques used for outlier detection to improve modeling capabilities of soft sensors on the basis of the quality of available data. We analyze methods based on standard deviation of population, on residuals of a linear input-output regression, on the structure correlation of the data, on principal components and partial least squares (both linear and nonlinear) in multi dimensional space (2D, 3D, 4D), on Q and T2 statistics, on the distance of each observation from the mean of the data, and on the Mahalanobis distance. We apply techniques for outlier detection both on a fictitious model data and on real data acquired from a sulfur recovery unit of a refinery. We show that outlier removal almost always improves modeling capabilities of considered techniques.

Development of a Soft Sensor for a Thermal Cracking Unit using a small experimental data set

Abstract: In this paper we compare a number of strategies to cope with the problem of small data sets in the identification of a nonlinear process. Four methods are analyzed: expansion of the training set by adding zero-mean fixed-variance Gaussian noise, expansion of the training set by adding zero-mean gaussian noise variance variable according with signal amplitude, integration between bootstrap method and stacked neural networks, and a new method based on the integration of bootstrap method, of the noise injection method, and of stacked neural networks. Such methods have been applied to develop a soft sensor for a thermal cracking unit working in a refinery in Sicily, Italy.

Selection of regressors using correlation analysis to design a Virtual Instrument for an SRU of a refinery

Abstract: In this paper the problem of regressors selection in Virtual Instruments (VI) design is addressed The VI is designed to replace the on line analyzer of a Sulfur Recovery Unit (SRU) of a large refinery located in Sicily during maintenance operations. It is designed by using nonlinear MA models implemented by a MLP neural network. The use of a set of cross-correlation functions, proposed by Billings and Voon to evaluate the performance of nonlinear models is used to select the regressors of the discrete-time NMA model by implementing an automatic regressor selection algorithm. The designed Soft Sensor has been implemented at the refinery to be tested on line.

Soft Sensor design for a Sulfur Recovery Unit using Genetic Algorithms

Abstract: In the paper the Soft Sensor design strategy for an industrial process, via neural NMA model, is described. In details, the hydrogen sulphide (H2S percentage) in the tail stream of a Sulfur Recovery Unit (SRU) of a refinery located in Sicily, Italy, is estimated by a Soft Sensor, that was designed to replace the online analyzer during maintenance operations. A general design strategy, based on the automatic selection of regressors of a NMA model is proposed. It is based on the minimization of the Lipschitz numbers by a Genetic Algorithms (GA) approach. A comparative analysis with an empirical model, developed on the basis of suggestions given by plant experts, is included to show the validity of the proposed procedure.

Education Enhancement on Three-Phase System Measurements

Abstract: Training on measurements to determine the characteristics of three-phase electrical systems is very useful for a deep knowledge of the theoretical aspects. Laboratory sessions for measurement by using advanced technologies must be performed in the training activity. Two implemented tools are presented. In the first one a virtual instrument is implemented, it can be used in distance learning sections: the system includes electromechanical instruments for a better understanding of the system operation. The last tool provides different features from the previous one; moreover, it can be realized as an electronic power meter, active and reactive, in a wide range of values.