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Steam Temperature and Boiler Controls

By: , Posted on: April 12, 2015

Why is Steam Temperature Control Critical to Boiler Controls?

To get better operational efficiency and also to prevent unnecessary material and thermal stress in thick walled components of boiler and turbine, it is very important to precisely control steam temperature on utility boiler. To get best possible heat rate to reduce fuel costs operators tries to maintain steam temperature at the rated value. By adjusting the amount of spray water in to the steam header after it pass through a stage of super-heater (SH) it is possible theoretically to control superheat temperature control. However, on account of non linearity, load dependent time constant (of the system response), gain, high dead time, time lag/time delay & uncertainty, it is very difficult to maintain the temperature precisely with conventional PID controllers. Process dead time changes with load, so, there will be requirement of feed forward signal requirement to take care of changes in firing rate and/or feed flow especially for once through boilers.

For in depth discussion on actual loop refer chapter VIII  from Power Plant Instrumentation and Control Handbook: A Guide to Thermal Power Plants

For full article, please visit here.

Following table enumerates the operational points.

table 1

Could PID Loop with Gain Scheduling and Feed Forward Signal Be a Solution?

Conventional PID controllers can meet basic control requirements in many control loops but not in all types. O make the loop better responsive, a number of parameters such as MS flow, Fuel flow, air flow, FR/FW ratio, and depending on applicability: Burner tilt/ Drum pressure The details regarding such modifications and interaction with reheat temperature controls have been elaborated in details with practical loop for both CE & Babacok design boilers  are available @ chapter VIII of  “ power plant Instrumentation & control handbook ( or

figure 2

Why No Direct Energy Balance?

figure 3

In large utility stations it is desirable to maintain the MS temperature near the rated value with a deviation of  +/- 5°C however in reality it is very difficult to maintain so precise. Again in larger stations a change in the tune of 10°C could drop in efficiency as high as 0.3%.  D-E-B ® Co ordinate control of steam by Metso , is another way to where co ordinate control system has been directly linked with Superheat Temperature control.

Is State Variable Controller with Observer (SCO) a Solution?

In control engineering a system can be described in terms of state space representation which is a mathematical model of physical system set by input output and state variables related by first order differential equation. Typical loop may be as follows:

figure 4

This loop is characterized by high and load dependent time constants non linearity (higher order) system.  Standard step change is used to get the knowledge about the various parameters such as Gain , system order (3,5 etc.), time constants, dead time etc.

Mathematical Model Based Approach: Predictive Adaptive and Dynamic Control

As discussed earlier, major problem associated with this loop is the time variable process dynamics, variable time delay and sudden unpredictable events (e.g. dirt deposit).  Controllers based on mathematical model could be a solution.

figure 5

The basic philosophy behind these are Anticipating process using mathematical model Adjusting model parameters  using adaptive system to make predicition error to zero.

What About a Fuzzy Control Approach?

Accurate mathematical model not only may be little costly but at times it may not be possible to accurately develop mathematical model as it is not always possible to undertstand the system correctly—especially when process operates over wide range of conditions, with disturbances. State variable approach has its limitation due to availability of all states and associated measurements. Fig 9 understanding of fuzzy available in the full paper here.

figure 10

Is it a New Approach? The Articficial Neural Network and Fuzzy Approach

In intelligent control system application use of Neural network and Fuzzy control cannot be oversetimated. In the following control application limitation of Artificial Neural Network(ANN) falling to local limit and drawback of Fuzzy controls of requirement of experienced operator can be somewhat be overcome. Neuro-fuzzy systems which use ANNs to determine their

figure 11



power plant instrumentationGrossly three factors viz. Load (MS flow) Heat transfer from Flue gas and spray quantity are mainly responsible for variations in SH temperature. However on account of various interactive loops, non linear load dependent process response coupled with time delay make superheat temperature control in a thermal power plant very difficult and challenging job.

Power Plant Instrumentation and Control Handbook: A Guide to Thermal Power Plants is available for purchase on the Elsevier Store. Use discount code “STC215” at checkout and save up to 30% on y0ur very own copy!

About the Author

Swapan Basu is Chief Executive, Systems and Controls, Engineering and Consulting Kolkata, India. He has several decades of experience in practicing instrumentation & control systems for subcritical and supercritical thermal power plants, including combined cycle projects. Since 1979, he has led teams of engineers in India, Jordan, Singapore, South Korea, Syria, and the United States. He has a number of national and international technical papers to his credit.

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