On-line performance monitoring using OMIS

1998 ◽  
pp. 3-10
Author(s):  
Marian Bubak ◽  
Włodzimierz Funika ◽  
Kamil Iskra ◽  
Radosław Maruszewski
Keyword(s):  
Performance Monitoring ◽  
On Line

Autonomous Performance Monitoring of an On-Line Real-Time Decision Support System

2011 ◽  
Vol 44 (1) ◽  
pp. 13080-13085 ◽  
Author(s):  
Michael Lees ◽  
Rob Evans ◽  
Iven Mareels
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Performance Monitoring ◽  
On Line

Implementation of on-line performance monitoring system at Seoincheon and Sinincheon combined cycle power plant

Energy ◽  
2005 ◽  
Vol 30 (13) ◽  
pp. 2383-2401 ◽  
Author(s):  
Si-Moon Kim ◽  
Yong-Jin Joo
Keyword(s):  
Power Plant ◽  
Monitoring System ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Combined Cycle Power Plant ◽  
On Line

On-line model-based performance monitoring of a shell-and-tube type heat exchanger using steam and water

2008 ◽  
Vol 49 (10) ◽  
pp. 2669-2677 ◽  
Author(s):  
Tetsuya Wakui ◽  
Ryohei Yokoyama
Keyword(s):  
Heat Exchanger ◽  
Performance Monitoring ◽  
Line Model ◽  
Model Based ◽  
On Line ◽  
Tube Type ◽  
Shell And Tube

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.

Development of On-Line Performance Monitoring and Fault Diagnosis System for Diesel Generator System

2011 ◽  
Vol 460-461 ◽  
pp. 461-466
Author(s):  
Yan Ping Cai ◽  
Ai Hua Li ◽  
Yan Ping He ◽  
Tao Wang
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Performance Monitoring ◽  
Vibration Signal ◽  
Diesel Generator ◽  
Generator System ◽  
Diagnosis System ◽  
Data Acquisition Card ◽  
On Line ◽  
Vibration Signal Analysis

Aiming at the need of on-line performance monitoring and fault diagnosis for diesel generator system, an on-line data acquisition, analysis and diagnosis system based on portable industrial computer, different function sensors and PCL-818HD data acquisition card was constructed. Its design of hardware, software and its test and diagnosis method were given in this paper. Through the vibration signal analysis and on-line fault diagnosis example of generator system, it shows that the diagnosis system can realize on-line performance monitoring and fault diagnosis for diesel generator system.

On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filtering Techniques

2003 ◽  
Author(s):  
Pierre Dewallef ◽  
Olivier Le´onard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Stochastic Methods ◽  
Inlet Temperature ◽  
State Variables ◽  
Present Contribution ◽  
Regression Problem ◽  
On Line

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, … Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.

scholarly journals Advancing the Practice of Performance Monitoring & Evaluation in Ocean Planning

2018 ◽  
Author(s):  
SeaPlan
Keyword(s):  
Adaptive Management ◽  
Performance Monitoring ◽  
Planning Process ◽  
Monitoring And Evaluation ◽  
Critical Component ◽  
Practical Application ◽  
Substantial Body ◽  
On Line ◽  
Rigorous Treatment ◽  
Management Cycle

A substantial body of literature from the broader planning discipline identifies performance monitoring and evaluation (PM&E) as the engine of the adaptive management cycle. In ocean planning, ideally PM&E is integrated throughout the cycle, enabling a plan to identify and respond to changing conditions and, ultimately, to evolve iteratively toward its goals. However, planning authorities face a variety of challenges on the ground, which leads to PM&E seldom being thoroughly considered early in the planning process, instead typically relegated to less than rigorous treatment in later implementation phases.This paper acknowledges the barriers to effective PM&E integration and explores strategies for advancing its practical application in ocean planning. The intent is to promote discussion among ocean planning practitioners and stakeholders about this critical component as new ocean plans come on line and existing plans are updated.

Digital Compressor Analytics

2018 ◽  
Author(s):  
Martin Bakken ◽  
Erling Lunde ◽  
Lars E. Bakken
Keyword(s):  
Dynamic Model ◽  
Performance Monitoring ◽  
Rotating Stall ◽  
Soft Sensors ◽  
Laboratory Setup ◽  
Transient Disturbances ◽  
On Line ◽  
Plant Data ◽  
And Performance ◽  
Production Losses

Norwegian gas export is a high value business, where small and transient disturbances may cause substantial production losses. Process experience has shown that the compressor system may suffer considerably owing to surge and rotating stall in situations where the compressor is forced to trip. One of the main challenges concerns analysis of the actual trip trajectory to validate whether the compressor has entered the unstable area of the performance characteristics. This type of analysis is paramount with regard to compressor operation and tuning of the compressor safety system. Recent advances in data analytics and digitalization capabilities give promise of new ways to handle and analyse such challenges. The current work presents data from a real compressor trip. The investigation reveals that plant data alone may not be sufficient for analysis of the trip trajectory. Hence, the trip scenario was analysed in light of experimental data, fan law principles and utilization of a detailed dynamic model. The results reveal that utilization of a dynamic model gives fruitful insight into the compressor system dynamics during a trip. These findings form a basis for future digitalization of the plant. This idea will be developed into the specification of a concept called a Digital Compressor. The digital compressor may run in off-line or on-line mode with the aim of providing: high resolution estimates (soft sensors) for non-measured or inaccurate process variables; or identification of process parameters and characteristics, such as gas density. Use cases include: off-line “what happened” analysis; identifying the minimal viable instrumentation; on-line advanced condition and performance monitoring. A digital compressor laboratory setup will be introduced, containing both a dynamic simulation system as well as a complete gas compressor rig — with all necessary computational and communication infrastructure.

scholarly journals Close loop optimisation of large CHP based on approximation model

2019 ◽  
Vol 137 ◽  
pp. 01051
Author(s):  
Łukasz Śladewski ◽  
Rafał Wereszczyński ◽  
Jerzy Majchrzak ◽  
Krzysztof Możejko ◽  
Wojciech Bujalski ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Advanced Control ◽  
Efficiency Increase ◽  
On Line ◽  
Environmental Requirements ◽  
Measurement Validation ◽  
Complex Optimization ◽  
Combustion Optimization ◽  
Refinery Plant ◽  
Correction System

Complex optimization of CHP plants becomes a very important issue in research and implementation, particularly in the context of increasing environmental requirements. The process in industrial CHP plants could be decomposed into several subprocesses, which could be optimized individually using dedicated solutions. The article presents the results of work of complex, multi-modular optimization project of one CHP plant located in petrochemical and refinery plant in Poland. The scope of the project is economical load dispatch optimizer aimed to increase economical profit of CHP operation, combustion optimization for boiler efficiency increase and NOX emission reduction, steam temperature advanced control for improved control quality, sootblowing optimization for reduction of steam demand for sootblowing process. The solution includes also measurement validation and correction system, which is based on data reconciliation algorithm and on-line performance monitoring system.

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