Data Reduction, Compression, and Recovery for Online Performance Monitoring

2019 ◽  
Author(s):  
Salvador DeCelles ◽  
Matthew Stamm ◽  
Nagarajan Kandasamy
Keyword(s):  
Data Reduction ◽  
Performance Monitoring ◽  
Online Performance Monitoring

Lightweight Online Performance Monitoring and Tuning with Embedded Gossip

2009 ◽  
Vol 20 (7) ◽  
pp. 1038-1049 ◽  
Author(s):  
J.W. Zhu ◽  
P.G. Bridges ◽  
A. Maccabe
Keyword(s):  
Performance Monitoring ◽  
Online Performance Monitoring

A Case Study of Optimizing Combined Cycle Performance at Kalaeloa

2009 ◽  
Author(s):  
Helmer Andersen
Keyword(s):  
Power Plants ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Plant Performance ◽  
Operational Performance ◽  
Cycle Performance ◽  
Monitoring Tools ◽  
Online Performance Monitoring ◽  
On Line

Fuel is by far the largest expenditure for energy production for most power plants. New tools for on-line performance monitoring have been developed for reducing fuel consumption while at the same time optimizing operational performance. This paper highlights a case study where an online performance-monitoring tool was employed to continually evaluate plant performance at the Kalaeloa Combined Cycle Power Plant. Justification for investment in performance monitoring tools is presented. Additionally the influence of various loss parameters on the cycle performance is analyzed with examples. Thus, demonstrating the potential savings achieved by identifying and correcting the losses typically occurring from deficiencies in high impact component performance.

Online Performance Monitoring and Quality Prediction for Batch Processes

2004 ◽  
Vol 37 (1) ◽  
pp. 299-304
Author(s):  
Cenk Ündey ◽  
Ali Cinar
Keyword(s):  
Performance Monitoring ◽  
Batch Processes ◽  
Quality Prediction ◽  
Online Performance Monitoring

Online performance monitoring and diagnosis of multivariate systems

2020 ◽  
pp. 095965182095365
Author(s):  
Neshat Moghbeli ◽  
Javad Poshtan
Keyword(s):  
Performance Monitoring ◽  
Closed Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Multivariate System ◽  
Online Performance Monitoring ◽  
Tank System ◽  
Performance Deterioration ◽  
System Output ◽  
Multivariate Systems

Online performance monitoring can be used to improve the performance of control systems in industry. The purpose of this article is to detect a performance deterioration and determine its cause in a system. In this article, two indices are used for online performance monitoring of a nonlinear multivariate system with optimally tuned proportional integral controllers. The first index is defined based on a squared distance measurement between the closed-loop system outputs and chosen set-points. The second index is a statistical index that uses all the information in the covariance matrices of the closed-loop system output data. Both indices are used and compared for performance monitoring of a quadruple-tank system. Moreover, hypothesis testing method has been used to determine the cause of the performance deterioration, so that appropriate solutions according to the cause can be applied to the system to improve the performance.

Online Performance Monitoring of Industrial Compressors Using Meanline Modelling

2014 ◽  
Author(s):  
Matteo Cicciotti ◽  
Dionysios P. Xenos ◽  
Ala E. F. Bouaswaig ◽  
Nina F. Thornhill ◽  
Ricardo F. Martinez-Botas
Keyword(s):  
Performance Monitoring ◽  
Guide Vane ◽  
Pressure Ratio ◽  
Inlet Guide Vane ◽  
Mechanical Degradation ◽  
Adaptive Parameters ◽  
Online Performance Monitoring ◽  
Constrained Minimization Problem ◽  
Inlet Conditions ◽  
Process Measurements

This paper proposes a framework for detecting mechanical degradation online and assessing its effect on the performance of industrial compressors. It consists of a model of the machine in undegraded condition and of a degradation adaptive model. The proposed methodology for online degradation detection differentiates itself from those found in the literature as the undegraded model is not linearized and ambient/inlet conditions are explicitly taken into account. The degradation is modelled through adaptive parameters which are estimated and updated online through the solution of a constrained minimization problem within a moving window. It uses available process measurements of flow, pressures, temperatures and composition. The update of the parameters guarantees the model accuracy and it permits the estimation of the effects of mechanical degradation away from the compressor running line. The performance monitoring framework has been successfully applied on an industrial air centrifugal compressor. It was found that after 3250 hours of operation from the previous maintenance the efficiency and the pressure ratio had dropped approximately 5.5% and 2.5% of their respective undegraded values. Furthermore, it was found that the performance deviations from the baseline depend from the position of the operative point in the performance map. In fact, the pressure ratio drop was lower (2%) and efficiency drop was higher (6%) for lower inlet guide vanes opening whereas pressure ratio drop was higher (3%) and efficiency drop was lower (1.6%) for higher inlet guide vane opening.

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