Journal of Scientific & Industrial ResearchVol. 65, August 2006, pp 639-645

Neural network for short-term predictions of ambient particulate matteraround thermal power plant

G Sriram':", N Krishna Mohan2 and V Gopalasarny'ISCSVMV, Deemed University, Enathur, Kanchipuram 631561

2Department of Mechanical Engineering, JDepartment of Civil Engineering, Annamalai University, Annmalainagar 608002

Received 28 November 2005; accepted 24 April 2006

A three-layer neural network model has been developed to predict suspended particulate matter (SPM) in and around athermal power plant at Neyveli. The feed forward supervised neural network-the back propagation was used to train thenetwork. Utilizing the meteorological data along with SPM data for all the six monitoring stations surrounding the thermalpower plant, the neural gave better predictions. The aim of the present research work is to construct a forecasting model thatwould be suitable for the use of future planning and developments.

Keywords: Air pollution, Neural networks, Suspended particulate matter, Thermal power plant

IPC Code: BO 1D49/00, G06N3/02

IntroductionGaseous and particulate air pollutants, emitted into

the ambient air by coal-fired electric power generatingstations, are transported and diffused by wind. Boxmodel and Gaussian plume models were commonlyused for computing pollutant concentration profiles.Gaussian model has some limitations 1-3. It producesunreasonable results when applied to diffusion in lowwind casesl". Since the advent of continuousmonitoring networks, time-series analyses have beenused to detect annual, seasonal, etc., data trends"; asdata gathering has reached high quality, morecomplex cycles within the series have been studied",The time - series approach has produced three typesof stochastic models: 1) ModelsB based on the timeseries of a single pollutant (AR, ARMA, ARIMAmodels); 2) Multivariable time series wheremeteorological explanatory variables have been addedon ARX, ARMAX models"; and 3) Forecast modelbased on a combination of classical and non-parametric methods 10. Artificial neural network(ANN) has become useful and efficient tool forestablishing forecasting models for air pollution. Aneural network (NN) is a special structure consistingof basic block called neurons, organized and

*Author for correspondenceTel: 04144-221584E-mail: sriram_gopalasamyv@yahoo.com

interconnected in one or more layers II. It inti mates thefunctioning of the human brain. If one is, for instance,familiar with the thermal inversion conditions atparticular station, one can predict high ambientconcentrations under such conditions withoutknowing anything about the physical back ground.The NN-based prediction model works in the sameway. As input to the model, a historical set ofsignificant meteorological and ecological data is usedand the outputs are ambient concentrations predictedby the model. First, the network is trained with thehistorical data. By the proper choice of training sets,the network is, after the learning process has beencompleted, capable of predicting the ambientconcentrations as an output according to themeteorological situation given as an input, and theinternal structure of the network, established duringthe learning period'". An overview of forecasting airpollutant using ANN has been discussed':',

Study Area

Neyveli Lignite Corporation (NLC), a public sectorenterprise, established in 1956 and situated at Neyveliin Tamil Nadu, about 200 km south of Chennai, isinvolved in the production of lignite, urea, and thegeneration of power. Neyveli thermal power station-I(TPS-l) has 6 units of 50 MW each and 3 units of 100MW each. Thermal Power Station-Il (TPS-Il) consists7 units of 210 each. Taking 6 air quality monitoring

640 J SCI IND RES VOL 65 AUGUST 2006

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stations operating in and around the Neyveli ThermalPower Plant along with wind speed and winddirection, NN forecasting model (back propagation)has been developed to forecast the suspendedparticulate matter (SPM) for February and September1995 for assessing compliance of emissions with airquality guidelines, criteria and standards. .

Materials and MethodsAmbient Air Quality MonitoringSix sampling stations Blocks 6, 8 and 29,

Vadakkuthu, Umangalam and Mudhanai have beenselected in conformity with local site suitability and tocover major population pockets in the vicinity. The airquality was monitored simultaneously at theselocations on every alternate day for 24 h. HighVolume Samplers (HVS) were used for themeasurement of SPM using gravimetric method. InTPS -I, 4 stacks were operating, and in TPS-II, 7stacks were operating during power generation.

Forecast Model DevelopmentIn the present work, NeuroIntelligence 2.1 neural

network software was used to construct NN models.Three layers NN model with hidden layer was used topredict SPM concentration. For example, parametersand structure of NN for station- 6 for February1995(Fig. 1) are as follows: 1) Neurons (Inputs, 7; Hidden,

3; Output, 1; Learning rate, O. 25; Momentum, 0.9;Time interval, for training patterns - February 95 &Historical data; Iterations, 501; and Trainingalgorithms, Back propagation); 2) Input Features(Wind speed, Wind direction, SPM concentration -station 1, SPM concentration - station 2, SPMconcentration - station 3, SPM concentration - station4 and SPM concentration - station 5); 3) Target (SPMconcentration, Station 6); and 4) Output Features(SPM concentration, station 6 at next intervals). Themoni tori ng data pertai ns to Feburay 1995 period wasused to meet the requirements of training and testingthe NN. NeuroIntelligence divides each dataset ontothree sets as follows:

Training SetIt is a part of input dataset used for NN training, i.e.

for adjustment of network weights.

Validation SetIt is a'part of data used to tune network topology or

network parameters other than weights. For example,it is used to define the number of hidden units todetect the moment when NN performance started todeteriorate. NeuroIntelligence uses Validation Set tocalculate generalization loss and retain the bestnetwork (the network with the lowest error onValidation Set).

Test SetIt is a part of input data set used only to test how

well NN will perform on new data. The Test set isused after the network is ready (trained), to test whaterrors will occur during future network application.This set is not used during training and thus can beconsidered as consisting of new data entered by theuser for NN application.

Scaling Numeric ColumnsBy default, numeric values are scaled using the

following formula:SF = (SRmax-SRmin)/(Xmax-Xmin)Xp = SRmin + (X-Xmin)* SFwhere, X=actual value of a numeric column,Xr-iinerninimum actual value of the column,Xmaxernaximurn actual value of the column,SRmin=lower scaling range limit, Skrnaxeupperscaling range limit, SF=scaling factor, andXp=preprocessed value. For input columns, scalingrange is [-1...1], which is carried out by determiningmaximum and minimum values of each variable overwhole data period.

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SRIRAM et al.: NEURAL NETWORK PREDICTION OF PARTICULATE MATTER AT THERMAL POWER PLANT 641

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Fig. 2-SPM concentration in sampling station -6 (Training set of pattern) February 1995

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Fig. 3-SPM concentration in sampling station -2 (Testing set-of pattern) for February 1995

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J set IND RES VOL 65 AUGUST 2006

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Fig, 4-SPM concentration in sampling station -6 (Validation set of pattern) February 1995

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1- Target - Outeut o Selected target 0 Selected output JFig, 5-SPM concentration in sampling station -4 (Training set of pattern) for September 1995

SRIRAM et al.: NEURAL NETWORK PREDICTION OF PARTICULATE MATTER AT THERMAL POWER PLANT 643

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Testing Results VisualizationActual vs output graph displays a line graph of the

actual and network output values for records.Horizontal axis displays the row number of the inputdataset and vertical axis displays the range of theoutput values. All graphs are plotted for the datadisplayed in the form of table as actual vs output forTraining, Test and Validation sets. .

644 J SCI IND RES VOL 65 AUGUST 2006

Table 1- Performance of neural network for SPM forecasting - February 1995

Station number Parameters Training Testing Validation

Correlation 0.972476 0.937442 0.961946R2 0.941808 0.868636 0.923422

Correlation 0.935368 0.931903 0.927702

2 R2 0.851786 0.832738 0.816149

Correlation 0.906605 0.892829 0.944954

3 R2 0.77915 0.748167 0.843585

Correlation 0.937368 0.926862 0.915831

4 R2 0.845085 0.824409 0.770704

Correlation 0.921808 0.935269 0.930751

5 R2 0.80242 0.85889 0.847241

Correlation 0.969853 0.954472 0.960937

6 R2 0.927905 0.906626 0.89231

Table 2-Performance of neural network for SPM forecasting - September 1995

Station number Parameters Training Testing Validation

Correlation 0.952197 0.935072 0.966292R2 0.888932 0.86483 0.919174

Correlation 0.960471 0.936991 0.946522

2 R2 0.915136 0.837557 0.884463

Correlation 0.974291 0.873511 0.992068

3 R2 0.942035 0.615504 0.983571

Correlation 0.981805 0.945293 0.962882

4 R2 0.960682 0.888004 0.922276

Correlation 0.973816 0.926812 0.978071

5 R2 0.931954 0.845211 0.942147

Correlation 0.956837 0.84887 0.938162

6 R2 0.905697 0.646128 0.855225

Activation FunctionsLogistic function has a sigmoid curve and is

calculated using the following formula:F(x) = 1/ (l+e-x). Its output range is [0... 1]This function is used most often and is set by default.Training Algorithms

Back propagation algorithm is the most popularalgorithm for training of multilayer perceptrons and isoften used by researchers and practitioners. Aftertraining, the display is: 1) Absolute error for trainingand validation sets; 2) Network error improvementduring the last iteration; 3) Number of currentiteration; 4) Training speed in number of iterationsper see; 5) Number of units in each network's layerincluding input and output layers; and 6) Trainingalgorithm used to train the network.

Results and DiscussionThe three layers NN model with hidden layer is

used to predict SPM concentration and the predictedvalues were compared with the measuredconcentrations at six sampling stations in NeyveliThermal Power Plant. Utilizing the commonmeteorological data along with SPM data for all the 6monitoring stations surrounding the TPP, the NN is

SRIRAM et al.: NEURAL NETWORK PREDICTION OF PARTICULATE MAlTER AT THERMAL POWER PLANT 645

constructed. For example, time series ofmeteorological and SPM data (213) of February 1995is used to construct NN forecasting model for thestation 6. In the similar line, NN forecast alsoprepared to forecast for September 1995.Separate network for other 5 stations are also

developed and trained. Figs 2-4 show the result ofSPM concentration in sampling stations-6 (Testingset, Training &Validation of pattern) for February1995. Figs 5-7 show the result of SPM concentrationin sampling stations-4 (Testing set, Training &Validation of pattern) for September 1995. Theperformance of NN model of all six sampling stationsfor February and September 1995 are tabulated(Tables 1 & 2).

ConclusionsAl\TNs are well suited to modeling complex,

nonlinear phenomena such as the air pollution andtornado formations. The performance of the models iscloser to the ideal values and hence they can be usedfor forecasting the pollutants concentration. Theexecution time of the model can be reduced byoptimal selection of input parameters, which alsocould improve the efficiency of the models. Theresults of study indicate that the neural network helpsin the air quality management programme inimplementing the control strategies by forecasting thepollutants concentration well in advance.

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