Pac 2013 Online Fuel Analysis Kalkitech

7/28/2019 Pac 2013 Online Fuel Analysis Kalkitech

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2013

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PAC 2013International Conference on Emerging Automation and Information

Technologies for Power Industry Growth

Bangalore, 19-20 April, 2013

Prabhu P

Asst.Vice President

Kalki Communication Tech. Pvt Ltd

Bangalore - 102

Tel: +91 80 4052 7900

E-mai: [email protected]

Power Plant Performance

Optimization by Online Fuel

Analysis & Emission Monitoring


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Academic

- B.Tech in Mechanical Engineer

- M.Tech in Energy Conservation and Management

Worked in:

- Bhagyanagar Solvent Extractions Pvt Ltd (2002-2004)

- Ercom Consulting Engineers Pvt Ltd (2001-2002)

Currently:

- Asst. Vice President, Kalki Communication Technologies Pvt Ltd, 2004 onwards

Certification:

- Certified Energy Auditor and Certified Energy Manager, National Productivity

Council, Ministry of Power

Prabhu.P


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Academic

- B.Tech in Electrical Engineering, WBUT, 2009

- M.Tech in Power Systems, Indian Institute of Technology Delhi, 2011

Currently:

- Tech Lead (Optimization), Kalki Communication Technologies Pvt Ltd, 2011

onwards

Sohom Datta


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Power Plant Performance Optimization by Online Fuel Analysis

& Emission Monitoring

Plant PerformanceKPI

Boiler Efficiency

Heat Rate

Terminal Temperature Difference & Drain Cooler

Approach

Heat Exchanger Effectiveness


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Need for fuel analysis

Track Operational Losses

Better Operational Control

Study impact in process based on fuel properties


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Trends in fuel analysis

1. Proximate Analysis as received basis

2. Ultimate Analysis in Laboratory by collecting sample

3. Periodic Analysis of fuel on receipt of new fuel to the

Plant


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Challenges in present methodology for actual Operating

conditions

1. Time consuming

2. Missed opportunity based on delayed availability of fuel

properties

3. Lack of information to fine tune operational performance

4. Difficult to predict actual performance of the equipment

5. Difficult to maintain emission parameters within limit6. Difficult to predict fuel flow to meet unit generation

7. Inaccurate operator guidance set points from the online

optimization solutions


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Proposed Solution Architecture

ANN Training

Process Parameters

Fuel Properties

Equipment Status & Environmental

Constraints

Trained ANN Fuel ModelData Validation Model

Online Process

Parameters

Thermodynamic Model

Predicted Fuel PropertiesOnline Process Parameters

(Load & Controllable

Parameter)

Estimate Error

Predicted Process Parameter

Error within

Limits

NoYes

Combustion Optimization

Soot Blower Optimization

Heat Rate Optimization

Heater Level Optimization

Fuel Properties

Optimizer

Predicted Fuel Properties

Online Performance

Optimization Module


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Proposed methodology -Fuel modeling and optimization

1.Computerized Online Intelligent Fuel Analysis System.

2. Historical fuel properties and correlated historical process

parameters are used for the analysis

3. Fuel properties are predicted by Artificial Neural Network.

4. Online process parameters obtained from Data acquisition

System to fine tune and reconcile the predicted fuel

properties by using first principle mathematical models

5. Online process parameters and predicted fuel properties

are used in performance optimization modules


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Data Collection

Fuel Inputs Process Inputs Remarks

Proximate Analysis

Moisture

Ash

Volatile Matter

Fixed Carbon

Ultimate Analysis Carbon

Hydrogen

Oxygen

Nitrogen

Sulphur

Ash

Fuel Calorific value

Process Parameters

Ambient Temperature

Emission Parameters from CEMS

Generator output

Flows

Air, Fuel, Feed Water, Mainsteam &

Spray water

Temperatures

Flue gas

Furnace Exit, Economizer inlet &

Outlet, Airheater inlet & outlet

Feedwater

Economizer inlet and outlet

Air

Airheater inlet and outlet

Steam

Mainsteam and Reheat steam,

Pressures

Furnace Pressure

Feedwater Inlet

Mainsteam and Reheat Steam

Etc

1. Equipment

degradation and running

status of equiment is

considered


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Fuel Modeling

1. Historical Process and Fuel Properties samples

has to be collected for at least three month period.

2. Before training the data with Neural Network, it has

to be properly validated and smoothened by using

first principle models.

3. Predict degradation of equipments based on pastdata.

4. Train the data with Artificial Neural Network.


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Summary

1. Coal properties varies on a regular basis

2. Operators are constrained due to inadequate fuel data

3. Fuel properties for current operating condition available after one or two days

4. Process parameter related to fuel properties are identified and an relationship

is established

5. Estimated fuel properties (online) acts as base for entire performance

optimization modules

6. Dynamic training of ANN fuel model

7. Predicted fuel properties are used to

estimate the exact combustion air requirement

prediction of temperature distribution in combustion chamber for soot

blower optimization, and

recommend the controllable parameter set points for heat rate

improvement


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Conclusion

Accuracy and on-time estimation of fuel properties

improves the accuracy of Plant Performance Analysis and

Optimization Set point

Key elements of proposed methodology are Trained ANN

Fuel Model, Thermodynamic model, Data validation model

and Fuel properties optimizer

Potential benefits are fuel savings and extended plant life


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Documents

Pac 2013 Online Fuel Analysis Kalkitech