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Thermal energy is generated in the boiler section which consists of furnace, economizer, drum, superheater and reheater. Correct proportion of the coal and air are given to the furnace for the complete combustion of the coal to produce flue gas. The obtained hot flue gas from this unit transfers heat to all the other units of boiler as shown in Fig. Meanwhile, feedwater enters the economizer unit for the preheating purpose. The preheated liquid enters drum unit for the conversion of liquid to steam. Drum unit in turn comprises of downcomer, riser and a drum. Downcomer transfers hot water from economizer and riser, where the conversion of water to unsaturated steam takes place.

The steam and the droplets of water from the unsaturated steam are separated in the upper and lower part of the drum unit respectively. The steam is later transferred to superheater and the water is circulated in the drum for the conversion. The unsaturated steam is converted to saturated steam in the superheater unit. There are three stages in the superheater, between which attemporator is placed for the temperature regulation. The data necessary for the validation of the developed model are collected from MW coal fired thermal power plant. The data collected from the thermal power plant are: feedwater flow rate; pressure at the exit of economizer unit, drum unit and superheater unit; temperature at the exit of the same three units; main steam flow rate; attemperator flow rate, turbine unit pressure, and the power.

The data were collected for the duration of min with the sampling interval of three minutes. A statistical model is capable of explaining the correlations between input and output variables. Boiler unit of the subcritical thermal power plant involves several interacting loops. The objective of the statistical modeling technique is to understand the interactions in the unit and between the units.

Also, this modeling strategy can overcome the difference between the model and the plant as the model is built from the plant data. Regression analysis is the basic form of statistical analysis and is classified as linear regression, nonlinear regression and multivariate regression. Linear regression is used if the system output depends on one independent variable; nonlinear regression is used if the system is nonlinear and also used in the lack of pre-defined input-output correlation; multivariate regression is used if the system depends on two or more independent variables.

The coefficients of the regression equation need to be improved to find ideal response. There are many optimization techniques for SISO single input single output systems that are affected by only one variable. When output is affected by many variables, output can be optimized using RSM. Statistical optimizations using design of experiments DOE finds the main factors of the multi-dimensional systems to evaluate optimal operating conditions of the integrated units.

There is not much literature on the usage of RSM in optimization of thermal power plants. The proposal of applying multi-factorial design for finding variables and its optimal value of the integrated unit of the coal-fired boiler process have been reported for the first time. Collected data from the plant are used in the response surface methodology RSM program written in MATLAB a to find the optimum input variables of the regression equation of integrated pressure output and temperature output.

The multiple linear regression equation involving two or more input variables can be expressed as,. In the above equation, Y is the dependent variable; X1, X2, Alternative form of interactive multiple regression equation is expressed in Eq. This interactive multiple regression equation is used in this work for deriving the prediction equation of the economizer unit, drum unit and superheater unit. In economizer unit, model of the temperature of the preheated water at the exit of the economizer is formulated. Temperature loop of the economizer has one dependent variable and four independent variables.

Hence in the concept of the multivariate regression equation there are eighteen parameters to be evaluated from the analysis of the regression Eq. Similar to the economizer unit, the regression equation for the temperature of the drum unit is given as in the Eq. Hence the drum equation has one dependent variable and three independent variables. Similar to other units, plant data are collected from the superheater unit of the plant.

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Flow rate of supersaturated steam is a function of its pressure. Regression model of the temperature has one dependent variable and three independent variables. The regression equation of the main steam temperature from the superheater unit is written as,. From the above equation, Qsh is the specific heat transfer rate from the flue gas to the superheater unit and Watm is the water droplets flow rate from the attemporator unit. The regression equation of the pressure and temperature of the main steam of the integrated model are also developed and analyzed in the next section. This identification is done with the help of OriginPro software.

The fitness of the model is found using the value obtained by coefficient of determination. At the same time, analysis of variance ANOVA is also carried out to analyze the effectiveness of the model. Coefficient of determination R 2 is the essential test for the regression analysis. It determines the degree of which the model output replicated the plant data.

It is found as the proportion of variance in the dependent variable that can be estimated from the independent variable. The R 2 value lies from 0 to 1. The value near to zero indicates that the developed model cannot be used for representing the plant data and on the other side; the value near to 1 says that the developed model can represent the plant output very efficiently.

The regression analysis also provides the significant test results along with the coefficient of determination. It is the test which analyzes the degree of reliability of output variable on the input variables. The significance value p-value of 0. The p-value of the regression analysis determines whether the coefficients of the framed model significantly have some effect on the dependent variable or not.

If p-value is less than the chosen significance value, then there exists statistical significance between that particular independent variable on the dependent variable. ANOVA is another analysis to study on the variance of the data collected from the plant. For this purpose, it primarily determines the variability of the collected data between the groups and the variability within the groups. ANOVA test provides informative of degree of freedom between the data set and within the data , sum of squares of the data, mean square of the data ratio of sum of squares to degree of freedom , F-value mean square of the model to the mean square of the error and lastly the significance value.

Also if p-value is less than 0. Thus, the independent variables for the control of the temperature of the preheated water from the economizer unit are specific heat transfer from the flue gas to the unit, feedwater flow rate and the pressure of steam from the drum. The independent variables for the temperature control of the drum unit are the specific heat transfer rate from the flue gas to the walls of drum unit, feed water flow rate and the pressure of the preheated water from the economizer unit.

Similarly the independent variable for the temperature control of the main steam from the superheater are specific heat transfer rate from the flue gas to the unit walls, flowrate of the attemporator and the pressure of the outlet steam from the drum. Since the manipulation of pressure of the any unit is not possible, using Clausius-Clapeyron equation, the pressure can be changed to temperature and therefore the temperature of the boiler unit can be varied.

Inputs and outputs from boiler units are collected and the analysis of regression equation is performed. The analysis gives the predicted values for the coefficients of the model along with the standard error values of each variable, the t-value and the p-value to analyze the significance of the manipulated variable on the respective process variable. Findings and the conclusion drawn from the analysis are discussed in this section.

Flowrate of liquid from economizer unit is stated in terms of differential pressure. The equation with the predicted coefficients obtained from the regression analysis is given in the Eq.


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The value of the coefficient of the regression, R 2, for this model is 1 which indicates the perfect fit of the model and the real time data. By analyzing the p-value of the regression analysis, the variable X 1 2 , X 2 2 and X 3 2 will affect the temperature.

The deviation of the model response from the plant output is very less which is evident from Fig. Response of predicted temperature model and the plant temperature output of economizer unit. The temperature model of the drum with the predicted parameters is shown in Eq. Response of predicted temperature model and the plant temperature output of drum unit.

The manipulated variables of the temperature model of the steam of the superheater are Q sh X 1 , W atm X 2 and P d X 3. The regression equation of the temperature of the superheater obtained from the regression analysis is in Eq. The manipulated variables considered for the integrated unit are Q , W fw and h e and the outputs are P int and T int. Response of predicted temperature model and the plant temperature output of superheater unit. The approachability of the model is provided by the analysis of variance.

The F-values of Anova Table suggests that the prediction equation are in good agreement with the plant data. The value of the coefficient of determination suggests that the model is suitable to analyse the importance of individual inputs, cumulative inputs, and interactive effects of selected inputs on the main steam of the integrated unit. The proposed method is compared with the main steam temperature model by Valsalam [18]. The MSE value for the proposed method is 1.

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Flue gas is drawn from the bulk flow going to the chimney, using a slip stream fan and passes over the gas to water heat exchanger GWH to heat the water. These will help in reducing the overhaul duration and will provide more reliable data on the health of the components. Thermal power plants are generally provided with Rotary Air Pre-heaters of Ljungstrom design where considerable fan power is lost due to seal leakage.

Depending on the learning, the experimental facility shall be suitably scaled up. Colloidal silica is difficult to be analysed and causes severe problems in boilers and turbines. NETRA is developing an online method for detection of colloidal silica in DM water so that preventive measures can be taken. NETRA has developed the artificial intelligence based real time plant advisory software system, which provides real-time online operator advisory to run the plant in the most efficient manner and thereby leads to on the spot improvement in unit heat rate.

The system has been developed based on data collected from one of the power stations. This software based system will take the real time plant data and provide the online advisory for optimisation of the operational activities. The system is a self learning type and can adapt to newer plant conditions. The system has real time data validation and reconciliation for detecting and reproducing plausible value for any corrupted value due to sensor random error or gross error.