## 5 Model Approach  
This research is based on the boiler consumption at the Yermarus Thermal Power Station (YTPS) located in India. The Yermarus Thermal Power Station is a coal-fired thermal power station in the Raichur district of Karnataka. The Karnataka Power Corporation owns the power facility. Bharat Heavy Electricals is the EPC contractor for this power project, which is India's first 800 MW supercritical thermal power plant. The plant has two units in it. The installed capacity of the power plant is 1600 MW (2 \(\times\) 800 MW). Figure 2: PCA–ELM Integration Approach. The equipment of a thermal power plant has different operating parameters to generate the maximum capacity. For a boiler, the operating parameters are the inlet steam pressure, inlet steam temperature, outlet steam pressure, outlet steam temperature, at re-heater, super-heater, de-super-heater, air pre heater, forced draft and load, total coal flow, total primary air flow, total secondary air flow, separated overfire air, heavy fuel oil pump current, seal air fan current, Air Pre-Heater (APH) main motor current, and APH standby motor current, and the levels of various gases are the parameters fed as inputs to perform PCA. From the widely deployed measuring devices, sensors, and recorders, the boiler log is summarized. Further, data from this boiler log for January 2022 from the Yermarus thermal power station were given as the input to the network model. It consists of nine coal mills. The data consist of 232 parameters and 96 samples each. The PCA calculation was performed on SPSS 19.0 software. Upon performing the eigenvalue decomposition technique for the input data covariance matrix, 232 parameters were reduced to 16 principal components based upon the covariance factor. The variance of each parameter can be explained using Table 1.  
For all the 232 variables, there will be 232 components, but the characteristics of the original variables will be reduced to 16 components. Key information about the 232 variables is mined from these components. The percentage of the variance of the first principal component (PC1) is 32.433. It signifies that the first principal component has 32.433% of the characteristics of the original data. The second principal component (PC2) has the second highest variance percentage of 23.347%. These 16 principal components form the inputs to the input layer of the modeled ELM network. The modeling of the ELM includes the selection of the number of hidden neurons and activation function and the division of data for training and testing. The modeled ELM consists of one hidden layer, and a variety of activation functions are considered. All the data are divided into two parts--one part is for training, and the other for testing. The ELM model is built for predicting the output. First, the ELM has been trained on some samples by giving both inputs and outputs. After that, it has been tested on some inputs, and the outputs obtained are compared with the original outputs in the data. The PC1 vs PC2 results are depicted in Figure 3. The type of activation function and number of hidden neurons affect the prediction capability and speed of the ELM model. If the accuracy and speed of the model is good, then the network model can be used for further greater analysis. By using SPSS, the principal components are randomly assigned as training samples and testing samples for different ranges of hidden nodes