Chemical Process Performance Evaluation

Çınar, Ali; Palazoğlu, Ahmet; Kayihan, Ferhan

doi:10.1201/9781420020106

Cited by 38 publications

(31 citation statements)

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“…Partial least squares regression (PLS) model is used to decompose the original data matrix X into two orthogonal matrices, the loadings (L) and scores (T) of 12.2 Background Literature 319 a number of latent indicators, and a residual matrix, E as shown in Eqn (12.18) (Cinar et al, 2004).…”

Section: Partial Least Square Variable Importance In Projectionmentioning

confidence: 99%

Selection of Sustainable Processes Using Sustainability Footprint Method

Mukherjee

Sengupta

Sikdar

2015

Computer Aided Chemical Engineering

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Section: Partial Least Square Variable Importance In Projectionmentioning

confidence: 99%

Selection of Sustainable Processes Using Sustainability Footprint Method

Mukherjee

Sengupta

Sikdar

2015

Computer Aided Chemical Engineering

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“…En nuestro caso de estudio discriminamos las muestras que se encuentran fuera de control por encima y fuera de control por debajo de los límites establecidos, creando los vectores ARLO MAS (muestras fuera de control que violan el límite superior) y ARLO MENOS (muestras que violan el límite inferior), transformando nuestro vector de datos poblacionales en una estructura binaria de las muestras fuera de control como se aprecia en las figuras 19(a) y 19(b). (Kayihan et al, 2007). La aplicación de componentes principales radica en la potencial de esta técnica para realizar compresión de datos, correlacionar información y detectar patrones o tendencias.…”

Section: Los Límites Deunclassified

“…El número de componentes principales es por lo general menor que el número de variables medidas, o la mayor parte de la variación del sistema o proceso, puede describirse con menos variables que el número de variables originales totales. Los componentes principales PC i también se pueden calcular por la descomposición espectral (Kayihan et al, 2007), calculando los valores y vectores propios, de la matriz de covarianza S de la matriz de datos X. Donde P es una matriz unitaria cuyas columnas son los autovectores normalizados de S y es una matriz diagonal que contiene los valores propios de S. La proyección o scores T se calcula mediante el uso de la transformación T = X.P.…”

Section: Los Límites Deunclassified

“…Si las observaciones individuales X (k) son independientes de X y S, entonces T 2 sigue una distribución F con m y n-m (m variables medidas, n tamaño de la muestra) grados de libertad (Kayihan et al, 2007):…”

Section: Los Límites Deunclassified

“…Si el vector de observación X (k) no es independiente de los estimadores y S, pero se incluye en su cálculo, entonces T 2 sigue una distribución Beta con m/2 y (n-m-1)/2 grados de libertad (Kayihan et al, 2007).…”

Section: Los Límites Deunclassified

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Integracion De Tecnicas Y Las Secuencias De Alarmas Para La Deteccion Y El Diagnostico De Fallos

Aguilar¹,

Fernando²

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Diagnosis of process faults in chemical systems using a local partial least squares approach

Krüger¹,

Dimitriadis

2008

AIChE Journal

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This article discusses the application of partial least squares (PLS) for monitoring complex chemical systems. In relation to existing work, this article proposes the integration of the statistical local approach into the PLS framework to monitor changes in the underlying model rather than analyzing the recorded input/output data directly. As discussed in the literature, monitoring changes in model parameters addresses the problems of nonstationary behavior and presents an analogy to model-based approaches. The benefits of the proposed technique are that (i) a detailed mechanistic plant model is not required, (ii) nonstationary process behavior does not produce false alarms, (iii) parameter changes can be non-Gaussian, (iv) Gaussian monitoring statistics can be established to simplify the monitoring task, and (v) fault magnitude and signatures can be estimated. This is demonstrated by a simulation example and the analysis of recorded data from two chemical processes. 2008 American Institute of Chemical Engineers AIChE J, 54: 2581-2596, 2008 Keywords: condition monitoring, fault detection, fault diagnosis, partial least squares, local approach IntroductionThe condition monitoring of industrial processes is standard practice in many industries, particularly the chemical industry. Based on the large body of work in the area of fault detection and diagnosis (FDD), the goals of condition monitoring are threefold: (i) to detect anomalous behavior in a process from the measured process variables, (ii) to locate and identify the source of such behavior, and (iii) to identify the magnitude of the fault. An ideal condition monitoring procedure can prevent a major malfunction in processing units by identifying incipient faults before they actually cause significant damage. Detailed discussions of condition monitoring and fault detection aspects can be found in many survey papers, for example, Refs. 1-4, or research texts. 5-7Techniques for process monitoring and FDD are based on a variety of paradigms including signal-based techniques [8][9][10][11] that are mainly applied to mechanical systems, model-based techniques 4,12-14 which address a wider spectrum of applications, rule-based techniques, [15][16][17][18] With advances in instrumentation and computer technology, the number of recorded process variables has increased to the point that some or many of the measured variables can be highly correlated. 27 In such cases, the creation of condition monitoring models is hindered by multicollinearity and suitably derived methods must be used. The most popular of these methods are statistical-based techniques, such as principal component analysis (PCA), partial least squares (PLS), and their extensions.In a previous paper, 28 a statistical theory was noted which can be readily used for the early detection of incipient faults, known as the local approach. The theory can be used to transform the problem of detecting a fault in a stochastic process to that of monitoring the mean of a Gaussian vector that is constructed from the...

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Chemical Process Performance Evaluation

Cited by 38 publications

References 0 publications

Selection of Sustainable Processes Using Sustainability Footprint Method

Selection of Sustainable Processes Using Sustainability Footprint Method

Integracion De Tecnicas Y Las Secuencias De Alarmas Para La Deteccion Y El Diagnostico De Fallos

Diagnosis of process faults in chemical systems using a local partial least squares approach

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