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Soft Sensor (soft + sensor)

Distribution by Scientific Domains
Chemistry63%
Life Sciences18%

Selected Abstracts

Development of a new soft sensor method using independent component analysis and partial least squares

AICHE JOURNAL, Issue 1 2009
Hiromasa Kaneko
Abstract Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model. © 2008 American Institute of Chemical Engineers AIChE J, 2009 [source]

Soft Mechanical Sensors Through Reverse Actuation in Polypyrrole,

ADVANCED FUNCTIONAL MATERIALS, Issue 16 2007
Y. Wu
Abstract The phenomenon of voltage generated from a soft sensor using polypyrrole in response to mechanical deformation is described and investigated. The sensor consists of two polypyrrole layers in contact with an electrolyte and operates in bending mode in air. The magnitude and sign of the induced voltage was found to depend on the type of dopant counter-ions and the nature of the surrounding electrolyte. The mechanical sensor response is shown to be a "reverse actuation", generating millivolt signals for millimeter sized deflections or ,,1000,C,m,3 charge for 1,% strain in the polypyrrole layer. A model based on ,Deformation Induced Ion Flux' has been proposed whereby the strain induced volume change in the polymer produces a shift in the Donnan equilibrium between mobile dopant ions inside the polymer and in the external electrolyte. A simple thermodynamic model provides reasonable estimates of the size of the voltage and charge produced. [source]

Development of a new soft sensor method using independent component analysis and partial least squares

AICHE JOURNAL, Issue 1 2009
Hiromasa Kaneko
Abstract Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model. © 2008 American Institute of Chemical Engineers AIChE J, 2009 [source]

Application of support vector regression for developing soft sensors for nonlinear processes,

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2010
Saneej B. Chitralekha
Abstract The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input,output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high-dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady-state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR-based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least-square-based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear-ARX model-based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input,output data. Le domaine du développement des capteurs logiciels a récemment gagné en importance avec la création d'outils de calcul efficaces et facilement utilisables à cette fin. L'idée de base est de convertir l'information obtenue dans les données d'entrée et de sortie recueillies à partir du processus dans un modèle mathématique. Un tel modèle mathématique peut servir de solution de rechange économique pour les capteurs matériels. L'outil de régression par machine à vecteur de support (RMVS) constitue un outil de calcul qui a récemment été l'objet de beaucoup d'attention dans la littérature des systèmes d'identification, surtout en raison de ses succès dans la création de modèles de boîte noire non linéaires. Dans ce travail, nous démontrons l'application de la RMVS comme outil efficace et facile à utiliser pour la création de capteurs logiciels pour les procédés non linéaires. Dans une étude de cas industrielle, nous illustrons le développement d'un capteur logiciel à indice de fluidité à état permanent pour un processus d'extrusion du polymère d'acétate de vinyle-éthylène à l'échelle industrielle en utilisant la RMVS. Le capteur logiciel fondé sur la RMVS, valide sur une vaste gamme d'indices de fluidité, a surclassé le capteur logiciel fondé sur les moindres carrés non linéaires existant en matière d'erreurs de prédiction plus faibles. Dans les deux autres études de cas, nous démontrons l'application de la RMVS pour la création de capteurs logiciels sous la forme de modèles dynamiques pour deux procédés non linéaires: un processus de neutralisation du pH simulé et un processus d'extrusion de polymère à deux vis à l'échelle laboratoire. Une procédure heuristique est proposée pour la création d'un capteur logiciel fondé sur un modèle ARX non linéaire dynamique en utilisant la RMVS, dans lequel on atteint automatiquement le délai optimal et les ordres en utilisant les données d'entrée et de sortie. [source]

Fluorescence-based soft-sensor for monitoring ,-lactoglobulin and ,-lactalbumin solubility during thermal aggregation

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2008
Rand Elshereef
Abstract A soft-sensor for monitoring solubility of native-like ,-lactalbumin (,-LA) and ,-lactoglobulin (,-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of ,-LA and ,-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of ,-LG and ,-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of ,-LA and ,-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-wavelength fluorescence spectra collected under a variety of process conditions and provide a fairly accurate quantification of respective native-like proteins despite the significant overlapping between their emission profiles. It was also demonstrated that a PLS model can be used as a black-box prediction tool for estimating protein aggregation when combined with simple mass balances. Bioeng. 2008;99: 567,577. © 2007 Wiley Periodicals, Inc. [source]

Modeling and Parameter Identification of the Simultaneous Saccharification-Fermentation Process for Ethanol Production

BIOTECHNOLOGY PROGRESS, Issue 6 2007
Silvia Ochoa
Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil-based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and "easy" to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification-fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the modelapos;s parameters, employing experimental data reported in the literature. [source]

Development of a new soft sensor method using independent component analysis and partial least squares

AICHE JOURNAL, Issue 1 2009
Hiromasa Kaneko
Abstract Soft sensors are used widely to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. To cope with this problem, a regression model can be updated. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method to the soft sensor to increase fault detection ability. Then, we have tried to increase the predictive accuracy. By using the ICA-based fault detection and classification model, the objective variable can be predicted, updating the PLS model appropriately. We analyzed real industrial data as the application of the proposed method. The proposed method achieved higher predictive accuracy than the traditional one. Furthermore, the nonsteady state could be detected as abnormal correctly by the ICA model. © 2008 American Institute of Chemical Engineers AIChE J, 2009 [source]

Application of support vector regression for developing soft sensors for nonlinear processes,

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2010
Saneej B. Chitralekha
Abstract The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input,output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high-dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady-state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR-based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least-square-based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear-ARX model-based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input,output data. Le domaine du développement des capteurs logiciels a récemment gagné en importance avec la création d'outils de calcul efficaces et facilement utilisables à cette fin. L'idée de base est de convertir l'information obtenue dans les données d'entrée et de sortie recueillies à partir du processus dans un modèle mathématique. Un tel modèle mathématique peut servir de solution de rechange économique pour les capteurs matériels. L'outil de régression par machine à vecteur de support (RMVS) constitue un outil de calcul qui a récemment été l'objet de beaucoup d'attention dans la littérature des systèmes d'identification, surtout en raison de ses succès dans la création de modèles de boîte noire non linéaires. Dans ce travail, nous démontrons l'application de la RMVS comme outil efficace et facile à utiliser pour la création de capteurs logiciels pour les procédés non linéaires. Dans une étude de cas industrielle, nous illustrons le développement d'un capteur logiciel à indice de fluidité à état permanent pour un processus d'extrusion du polymère d'acétate de vinyle-éthylène à l'échelle industrielle en utilisant la RMVS. Le capteur logiciel fondé sur la RMVS, valide sur une vaste gamme d'indices de fluidité, a surclassé le capteur logiciel fondé sur les moindres carrés non linéaires existant en matière d'erreurs de prédiction plus faibles. Dans les deux autres études de cas, nous démontrons l'application de la RMVS pour la création de capteurs logiciels sous la forme de modèles dynamiques pour deux procédés non linéaires: un processus de neutralisation du pH simulé et un processus d'extrusion de polymère à deux vis à l'échelle laboratoire. Une procédure heuristique est proposée pour la création d'un capteur logiciel fondé sur un modèle ARX non linéaire dynamique en utilisant la RMVS, dans lequel on atteint automatiquement le délai optimal et les ordres en utilisant les données d'entrée et de sortie. [source]

PLS: A versatile tool for industrial process improvement and optimization

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 6 2008
Alberto Ferrer
Abstract Modern industrial processes are characterized by acquiring massive amounts of highly collinear data. In this context, partial least-squares (PLS) regression, if wisely used, can become a strategic tool for process improvement and optimization. In this paper we illustrate the versatility of this technique through several real case studies that basically differ in the structure of the X matrix (process variables) and Y matrix (response parameters). By using the PLS approach, the results show that it is possible to build predictive models (soft sensors) for monitoring the performance of a wastewater treatment plant, to help in the diagnosis of a complex batch polymerization process, to develop an automatic classifier based on image data, or to assist in the empirical model building of a continuous polymerization process. Copyright © 2008 John Wiley & Sons, Ltd. [source]