There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

https://www.itk.ntnu.no/fag/fordypning/TK16-filer/Samling1_MorariLee.pdf

Model predictive control: past, present and future

In this review, the technologies and working principles of different materials used in supercapacitors are explained. The most important supercapacitor active materials are discussed from both research and application perspectives, together with brief explanations of their properties, such as specific surface area and capacitance values. A review of different supercapacitor electrolytes is given and their positive and negative features are discussed. Finally, cell configurations are considered, pointing out the advantages and drawbacks of each configuration.

Review on supercapacitors: Technologies and materials

Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal ex- planation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and pre- diction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the phi- losophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory ver- sus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the model- ing process.

/pdf/to-explain-or-to-predict-1wxih4kis9.pdf

To Explain or to Predict

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

Roberts (1959) first introduced the exponentially weighted moving average (EWMA) control scheme. Using simulation to evaluate its properties, he showed that the EWMA is useful for detecting small shifts in the mean of a process. The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done. In this article, we evaluate the properties of an EWMA control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value. A design procedure for EWMA control schemes is given. Parameter values not commonly used in the literature are shown to be useful for detecting small shifts in a process. In addition, several enhancements to EWMA control schemes are considered. These include a fast initial response feature that makes the EWMA control scheme more sensitive to start-up problems, a combined Shewhart EWMA that provides protection against both larg...

https://www.jstor.org/stable/pdfplus/1269835.pdf

Exponentially weighted moving average control schemes: properties and enhancements

For processes described by linear transfer functions with additive disturbances, the best possible control in the mean square sense is realized when a minimum variance controller is implemented. It is shown that an estimate of the best possible control can be obtained by fitting a univariate time series to process data collected under routine control. No ‘identifiabuity’ constraints need be imposed. The use of this technique is demonstrated with pilot plant and production data.



Pour les procedes decrits par des fonctions de transfert lineaires avec des perturbations additives, le meilleur controle possible au sens des moindres carres s'obtient avec un regulateur a variance minimum. On montre qu'on peut obtenir une estimation du meilleur controle possible en adaptant une serie chronologique univariee pour traiter des donnees recueillies lors d'un retrocontrole de routine. Il n'est pas necessaire d'imposer des contraintes d'«identificabilite». L'utilisation de cette technique est decrite par des donnees d'une usine pilote et des donnees de production.

Assessment of control loop performance

A parametric model predicting the performance of a solid polymer electrolyte, proton exchange membrane (PEM) fuel cell has been developed using a combination of mechanistic and empirical modeling techniques. This paper details the mechanistic model development. Mass transport properties are considered in the mechanistic development via Stefan‐Maxwell equations. Thermodynamic equilibrium potentials are defined using the Nernst equation. Activation overvoltages are defined via a Tafel equation, and internal resistances are defined via the Nernst‐Planck equation, leading to a definition of ohmic overvoltage via an Ohm's law equation. The mechanistic model cannot adequately model fuel cell performance, since several simplifying approximations have been used in order to facilitate model development. Additionally, certain properties likely to be observed in operational fuel cells, such as thermal gradients, have not been considered. Nonetheless, the insights gained from the mechanistic assessment of fuel cell processes were found to give the resulting empirical model a firmer theoretical basis than many of the models presently available in the literature. Correlation of the empirical model to actual experimental data was very good.

https://iopscience.iop.org/article/10.1149/1.2043959/pdf

Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell. II: Empirical model development

A review of performance monitoring and assessment techniques for univariate and multivariate control systems

Measurements from industrial processes are often serially correlated. The impact of this correlation on the performance of the cumulative sum and exponentially weighted moving average charting techniques is investigated in this paper. It is shown that serious errors concerning the “state of statistical process control” may result if the correlation structure of the observations is not taken into account. The use of time series methods for coping with serially correlated observations is outlined. Paper basis weight measurements are used to illustrate the time series methodology.

Thomas J. Harris

Papers

Exponentially weighted moving average control schemes: properties and enhancements

Assessment of control loop performance

Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell. II: Empirical model development

A review of performance monitoring and assessment techniques for univariate and multivariate control systems

Statistical process control procedures for correlated observations