Real-Time On-Line Monitoring of Pulverized Coal at Power Stations

2003 ◽  
Author(s):  
Xiaoshu Cai ◽  
Junfeng Li ◽  
Zhijun Zhao ◽  
Xin Ouyang ◽  
Chunyang Huang
Keyword(s):  
Particle Size ◽  
Real Time ◽  
Pulverized Coal ◽  
Utility Boiler ◽  
Power Stations ◽  
Utility Boilers ◽  
On Line ◽  
Measure Particle Size ◽  
One Year ◽  
Light Fluctuation

The principle of light fluctuation is presented. Based on this method, a novel system for on-line real time measuring pulverized coal was developed by authors. This system may be used in both the direct firing pulverizing system and the indirect firing pulverizing system. The results of over-one-year monitoring in a 300 MW utility boiler and many measurements in utility boilers with the capacities from 300MW to 600 MW show that the system can reliably measure particle size, concentration and velocity of pulverized coal.

An Ada-Based On-Line Computer System for AGR Power Stations

1993 ◽  
Vol 26 (3) ◽  
pp. 76-79
Author(s):  
R Clarke ◽  
F Humphries
Keyword(s):  
Real Time ◽  
Computer System ◽  
Relational Database ◽  
High Performance ◽  
Computer Systems ◽  
Case Tools ◽  
Power Stations ◽  
On Line ◽  
Replacement System ◽  
And Performance

Nuclear Electric have commissioned Ferranti International to develop a replacement system for their on-line computer systems in earlier AGR power stations. This system represents a step forward both in software and performance and takes advantage of investment made for military use in the Ada Language, CASE tools and a high-performance real-time relational database.

scholarly journals Mass flow and particle size monitoring of pulverised fuel vertical spindle mills

2016 ◽  
Vol 37 (2) ◽  
pp. 175-197 ◽  
Author(s):  
Hamresin Archary ◽  
Walter Schmitz ◽  
Louis Jestin
Keyword(s):  
Particle Size ◽  
Mass Flow ◽  
Good Precision ◽  
Monitoring Tool ◽  
Performance Factors ◽  
Flow Monitoring ◽  
Power Stations ◽  
Plant Environment ◽  
On Line ◽  
Set Up

Abstract The first step towards condition based maintenance of the milling plant is the implementation of online condition monitoring of the mill. The following paper presents and analyses methods of monitoring the key performance factors of a vertical spindle mill that is suited for implementation on older power stations, i.e. the quantity (mass flow rate) and quality (particle fineness) of the pulverised fuel produced by the mill. It is shown herein that the mill throughput can be monitored on-line using a simple mill energy balance that successfully predicts the coal throughput within 2.33% as compared to a calibrated coal feeder. A sensitivity analysis reveals that the coal moisture is a critical measurement for this method to be adopted as an on-line mass flow monitoring tool. A laser based particle size analyser tool was tested for use in the power plant environment as an online monitoring solution to measure pulverised fuel fineness. It was revealed that several factors around the set-up and operation of the instrument have an influence on the perceived results. Although the instrument showed good precision and repeatability of results, these factors must be taken into account in order to improve the accuracy of the reported results before the instrument can be commissioned as an on-line monitoring solution.

Monitoring pH and ORP in a SHARON reactor

2011 ◽  
Vol 63 (11) ◽  
pp. 2505-2512 ◽  
Author(s):  
J. Claros ◽  
J. Serralta ◽  
A. Seco ◽  
J. Ferrer ◽  
D. Aguado
Keyword(s):  
Real Time ◽  
Reduction Potential ◽  
Ammonia Removal ◽  
Real Time Control ◽  
Time Control ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
High Ammonia ◽  
On Line ◽  
One Year

This paper analyses the valuable information provided by the on-line measurements of pH and oxidation reduction potential (ORP) in a continuous single high ammonia removal over nitrite (SHARON) reactor. A laboratory-scale SHARON reactor equipped with pH, ORP, electric conductivity and dissolved oxygen (DO) probes has been operated for more than one year. Nitrogen removal over nitrite has been achieved by adding methanol at the beginning of anoxic stages. Time evolution of pH and ORP along each cycle allows identifying the decrease in nitritation rate when ammonia is consumed during the aerobic phase and the end of the denitrification process during the anoxic phase. Therefore, monitoring pH and ORP can be used to develop a real-time control system aimed at optimizing the length of both aerobic and anoxic stages. Real-time control of methanol addition can be carried out by using the information provided by these probes: excessive methanol addition in the anoxic stage is clearly detected in the ORP profile of the following aerobic phase, while a deficit of methanol is detected in both pH and ORP profiles of that anoxic phase. Moreover, other valuable information such as the amount of ammonia nitrified, failures in DO measurements, excessive stirring during the anoxic stage and methanol dosage in the aerobic phase was also provided by the pH and ORP profiles.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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