A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction- and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contribution- and reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation. Copyright © 2003 John Wiley & Sons, Ltd.

Statistical process monitoring: basics and beyond

Global warming resulting from the emission of greenhouse gases, especially CO2, has become a widespread concern in the recent years. Though various CO2 capture technologies have been proposed, chemical absorption and adsorption are currently believed to be the most suitable ones for post-combustion power plants. The operation of the chemical absorption process is reviewed in this work, together with the use of absorbents, such as the ionic liquid, alkanolamines and their blended aqueous solutions. The major concerns for this technology, including CO2 capture efficiency, absorption rate, energy required in regeneration, and volume of absorber, are addressed. For adsorption, in addition to physical adsorbents, various mesoporous solid adsorbents impregnated with polyamines and grafted with aminosilanes are reviewed in this work. The major concerns for selection of adsorbent, including cost, adsorption rate, CO2 adsorption capacity, and thermal stability, are compared and discussed. More effective and less energy-consuming regeneration techniques for CO2-loaded adsorbents are also proposed. Future works for both absorption and adsorption are suggested.

/pdf/a-review-of-co2-capture-by-absorption-and-adsorption-5eczh2835p.pdf

A Review of CO2 Capture by Absorption and Adsorption

Techniques to detect and isolate failures in complex technological systems, such as sensor biases, actuator malfunctions, leaks, and equipment deterioration are surveyed. The methods are based on analytical redundancy afforded by a mathematical model of the system. The main components of such techniques are residual generation using the model, signature generation by statistical testing, and signature analysis. Model-structural conditions for failure isolation are introduced together with transformation methods to implement them. Sensitivity and robustness considerations are presented, and a design framework based on model redundancy is proposed. >

Survey of model-based failure detection and isolation in complex plants

A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS creates the components by modeling the relationship between input and output variables while maintaining most of the information in the input variables. PLS is useful in situations where the number of explanatory variables exceeds the number of observations and/or a high level of multicollinearity among those variables is assumed. Motivated by this fact we will provide a kernel PLS algorithm for construction of nonlinear regression models in possibly high-dimensional feature spaces.We give the theoretical description of the kernel PLS algorithm and we experimentally compare the algorithm with the existing kernel PCR and kernel ridge regression techniques. We will demonstrate that on the data sets employed kernel PLS achieves the same results as kernel PCR but uses significantly fewer, qualitatively different components.

/pdf/kernel-partial-least-squares-regression-in-reproducing-x9kytjbqg7.pdf

Kernel partial least squares regression in reproducing kernel hilbert space

This paper shows how information inherent in the process constraints and measurement statistics can be used to enhance flow and inventory data. Two important graph-theoretic results are derived and used to simplify the reconciliation of conflicting data and the estimation of unmeasured process streams. The scheme was implemented and evaluated on a CDC-6400 computer. For a 32-node 61-stream problem, the results indicate a 42 to 60 % reduction in total absolute errors, for the three cases in which the number of measured streams were 36, 50, and 61 respectively. A gross error detection criterion based on nodal imbalances is proposed. This criterion can be evaluated prior to any reconciliation calculations and appeared to be effective for errors of 20 % or more for the simulation cases studied. A logically consistent scheme for identifying the error sources was developed using this criterion. Such a scheme could be used as a diagnostic aid in process analysis.

https://gregstanleyandassociates.com/ReconciliationRectificationProcessData-1976.pdf

Reconcillation and Rectification of Process Flow and Inventory Data

Distillation with secondary reflux and vaporization: A comparative evaluation

Detection of gross errors in process data

Une nouvelle methode statistique est utilisee pour detecter, identifier et estimer les grosses erreurs (erreurs systematiques et fuites) qui peuvent se presenter dans les procedes chimiques en regime permanent. Developpement d'un modele mathematique pour decrire les fuites et les erreurs systematiques. Une nouvelle strategie de detection de grosses erreurs multiples basee sur la compensation serielle des grosses erreurs est aussi proposee

Generalized likelihood ratio method for gross error identification

http://automatica.dei.unipd.it/public/Schenato/PSC/2010_2011/gruppo4-Building_termo_identification/IdentificazioneTermodinamica20072008/Biblio/art11.pdf

Richard S.H. Mah

Papers

Reconcillation and Rectification of Process Flow and Inventory Data

Distillation with secondary reflux and vaporization: A comparative evaluation

Detection of gross errors in process data

Generalized likelihood ratio method for gross error identification

Nonlinear partial least squares