Prediction of NOx Emissions in Boilers Using Neural Networks and Excel™

In an attempt to demystify artificial neural networks (ANNs) and to set the tone of this paper, it is best to start by saying that ANNs are mathematical functions of the form y = f(x, w), where y is the output or response that is determined from a given input x by a series of calculations through a network of connected processing elements or nodes. This article demonstrates the feasibility of utilizing a Microsoft® Excel™ workbook for defining, training, and subsequently using ANNs as a prediction tool. The example given here involves the prediction of nitrogen oxide (NOx) emissions from boilers. It is very important to be able to predict the emissions of pollutants such as CO and NOx in boilers in order to determine the effectiveness of the combustion process and to evaluate the environmental contribution of gas emissions. This requires measured data such as effluent temperature and gas flow rate.

The nodes or neurons are arranged in layers and are connected by unidirectional communication arcs, which carry numerical data. The input nodes represent a set of variables, x, and the output nodes correspond to the predicted output set of y variables. These two sets of nodes form the input and output layers, respectively. The information between layers is processed through various intermediate layers, the so-called hidden layers. The parameters that modify the information transferred from one layer to the next, w, represent the strength of the connection between the nodes, and whose numeric values give the network its predictive power.

Figure 1 - Schematics of a three-layer neural network model.

The neural network in Figure 1 contains three layers. The input layer has two nodes, I1 and I2, one for T (temperature) and one for F (flow), respectively. The hidden layer has three nodes, H1, H2, and H3; the output layer has only one node, O1, which corresponds to the calculated concentration of NOx.

Calculations in the ANN start when values are applied to the input nodes. These inputs are then multiplied by the weights wk,i connected to all nodes in the hidden layer. The products are summed up for each node and a bias is subtracted (sum = Σxkwk,i – bk). xThis sum is passed along to an activation function to produce the output of the node; the sigmoidal function is the most commonly used activation function, f(sum) = 1/(1+exp(-sum)). The resulting values are then used as the inputs to the output layer. Each neuron in the output layer receives weighted inputs minus bias from each neuron in the hidden layer to determine the activation function of the output layer, which corresponds to the neural network output. The activation function serves to model nonlinear behaviors. The input nodes act as distribution nodes, with no calculations associated; the inputs are just transferred to the nodes in the hidden layer.

For example, in the neural network model of Figure 1, operations are performed in the hidden nodes that receive the two inputs (input #1 has a value of 0.4 and input #2 a value of 0.6). Each input has a corresponding weight that connects to the hidden nodes (weight #1 connects input #1 and has a value of –1.26, etc.). Three values of f(sum) will be calculated, one for each node in the hidden layer 0.341, 0.979, and 0.947, respectively. Then, the weighted sum of inputs and calculation of the activation function for the output node are applied, and a value of 0.609 is obtained in the network output.

This particular method of performing the calculations in the ANN is associated with a structural design named feed forward. If the actual value of the target is equal to 0.62, the error between the target value and the output would be equal to –0.011 (i.e., 0.62 – 0.609 = –0.011). All errors for all the observations in the dataset are calculated for these particular sets of weights and bias values.

When a neural network is trained, the weights and bias terms of neurons are adjusted individually. Training a neural network is an iterative process; it uses a nonlinear optimization algorithm to obtain the optimal values of the weights. The data must be divided into two subsets: a training dataset and a test dataset. An error back-propagation is the most widely used algorithm, and it is known as a learning algorithm. A full mathematical explanation of this algorithm can be found elsewhere.1–3 Initially, the connection weights and bias are set to random values. The weights are then adjusted until a training error criterion is minimized, that is, until learning is successful. The most common convergence criterion is the mean squared error (MSE), which is the squared difference between the actual output and the predicted output, divided by the number of observations (patterns) of the training dataset.

Neural networks were studied as early as the 1940s by McCulloh and Pitts.4 However, they did not become popular until around 1985, when the method of back-propagation for training neural networks was introduced by Rumelhart et al.5 Neural models are now enjoying a resurgence, and there is a substantial amount of research in the area of neural networks, because of their ability to represent nonlinear relationships, which is useful in making function approximation, forecasting, and recognizing patterns.6,7

Use of Excel to train a neural network

The objective of this study was to develop a simple, easily understood methodology for training neural networks using an Excel workbook. Data from a boiler stack were used to build and train a neural network that predicts the concentration of NOx emissions. A combustion analyzer was used to measure NOx in the flue gas.

The Excel workbook that predicts the NOx emissions consists of three modules (DATA, TRAINER, and QUERY). The DATA worksheet is an interface for users to keyboard the data and normalize them. The TRAINER worksheet is an interactive interface and work area. The user can interact with the spreadsheet by setting and adjusting parameters needed for training the neural network. The QUERY worksheet is designed for query and computing results.

Configuring the DATA worksheet

The data must be preprocessed in order for the ANN to effectively learn from them. All values that appear to be scattered far away from the majority of values are considered outliers and must be excluded from the dataset. The numeric data of all the variables are linearly scaled between 0 and 1, considering the minimum and maximum values. This scaling of variable values is made in order to avoid using data spanning different orders of magnitude. Following the common practice, the dataset to train the network was made by randomly selecting about 70% of the database. The remaining 30% of data were then used to check the generalization capability of the model.

Microsoft Excel makes it possible to access more than one worksheet. Each worksheet is represented in the bottom section of the interface with a tab. The left-most tab is marked Sheet1. The second from the left is marked Sheet2. The last is marked Sheet3. To rename a worksheet, the user double-clicks its sheet tab and then types a new name. Once this is done, the user changes the tabs Sheet1, Sheet2, and Sheet3 to the new names DATA, TRAINER, and QUERY, respectively. To move between the different worksheets, the corresponding tabs are clicked.

Figure 2 - DATA worksheet.

The steps below are followed:

  1. Enter the number for each record into column A, type 1 into cell A4, and the equation =A4+1 into cell A5. Then highlight cell A5; the cursor box will enclose the cell, and a small black square will be seen at the lower right corner called the fill handle. Point to the square (the pointer should change to a thin cross), press and hold the left mouse button, drag the pointer downward to cell A33, and release. This yields a column of serial numbers by copying the equation from cell A5 down to and including A33. See Figure 2.
  2. Enter the headings for columns B, C, and D into rows 2 and 3.
  3. Enter the values for the flow, temperature, and NOx into columns B, C, and D, respectively. These will correspond with the actual values for input nodes 1 and 2 and the output node of the network.
  4. Enter the function to determine the largest number for each input and output node, starting in cell B36 to cell D36, using the MAX function. To do so, position on cell B36 and click on the Insert Function button to the left of the formula bar. Several functions are available; select MAX and the functions arguments dialog box will pop up. With the mouse, select range B4:B33, click the OK button, then into cell C36 enter MAX(C4:C33); into cell D36 enter MAX(D4:D33).
  5. Enter the minimum values for data values for each input and output node as in step 4, using the MIN formula, into cells B37 to D37.
  6. The next step is to normalize the data by subtracting the minimum value and dividing by the difference of the maximum minus the minimum. The normalized values for input #1 of column B will be in column E. Into cell E4 type (B4-$B$37)/($B$36-$B$37). Copy this formula into the rest of column E; use the fill handle to copy values into neighbor cells from rows 4 to 33, as in step 1. When this is done, cell references that do not include $ signs are updated (cell address B4 in the formula changes to B5, B6, etc.), whereas cell references with $ signs (cells with absolute address) are not; a simple way to do this is to hit the function key F4 when typing the cell address. This is important because $B$36 and $B$37 are fixed parameters. Do the same for input #2 values on column F and the output values on column G using the proper address for max. and min. Save the workbook.

This ends the easy part of laying out the DATA worksheet. The TRAINER worksheet is prepared as shown below.

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