SIMULATION-BASED PREDICTIVE EMISSION MONITORING SYSTEM

Transcription

1 SIMULTION-BSED PREDICTIVE EMISSION MONITORING SYSTEM Mncho Hadjsk, Kosta Boshnakov and Nkolnka Chrstova Department of utomaton n Industry Unversty of Chemcal Technology and Metallurgy Blvd. Klment Ohrdsk, No. 8, 1756 Sofa, Bulgara E-mals: hadjsk@uctm.edu, kb@uctm.edu, nchrst@uctm.edu KEYWORDS Hybrd modelng, nferental measurement, mathematcal model, predctve emsson montorng system, thermal power staton BSTRCT development and nvestgaton of mathematcal models for Predctve Emsson Montorng Systems (PEMS) n order to reconstruct the mssng data on the base of low frequent drect measurements are dscussed. comparatve analyss s provded among varety of ndvdual and hybrd models usng real epermental data. Practcal recommendatons are derved for desgn of PEMS orented to thermal power plants. INTRODUCTION The man functons of the emsson montorng systems n thermal power statons are the measurements and the regstraton of the harmful emssons and the preparaton of the reports for the nspectons for envronmental protecton. The Regulatng agences n US and n the countres of the EC have regulated two ways of reportng data (readngs) formaton n the emsson montorng systems (Eberhard 1995; Joseph and Macak 1996; Ekhus and Black; Gmonez et al.; EP Handbook 1997; US Envronmental Protecton gency 000): - By usng drect measurements n the Contnuous Emsson Montorng Systems (CEMS), whch s the tradtonal approach; - By usng mathematcal models based on the ndrect measurements for realzaton of the ecologcal goals, known as Predctve Emsson Montorng Systems (PEMS). In both of the reportng data sources the requrements of the natonal legslatons (Bulgaran Mnstry of Envronmental Protecton) and the statutory procedures and the standards n US and EC should be satsfed (EP Handbook 1997; lberta Envronmental Protecton 1998; US Envronmental Protecton gency 000; Code of Federal Regulatons, Part 60; Code of Federal Regulatons, Part 75). The Predctve Emsson Montorng has strengthened ts poston n US and t s provded for wth the respectve normatve documents (Eberhard 1995; Ekhus and Black; Topcal Report ; Ghen et al. 003). PEMS have been ntroduced n power statons n US and Europe wth total capacty of MW. The emsson montorng on the base of ndrect measurements s consdered as an alternatve of the tradtonal approach (CEMS). Besdes the carryng out of the basc functons, the predctve montorng systems enable realzaton of optmal control of the combuston processes under the ecologcal restrctons, realzaton of some dagnostc functons as well as usage for generaton of recommendatons for operatonal and control actvtes, whch wll mprove the total effectveness of the equpment works on lower nvestment epenses. The man goals of the current study are constructon and nvestgaton of mathematcal models for PEMS appled to thermal power plant and development of procedures for mssng data reconstructon on the base of low frequent drect measurements when CEMS and PEMS work together. The crtera for dscrmnaton of the partcular solutons are: accuracy, valdty for all the technologcal regmes, operatonal relablty and mantenance epenses. RECONSTRUCTION OF THE MISSING DT IN THE EMISSION MONITORING SYSTEMS The am of ths study s development of Predctve Emsson Montorng Systems (PEMS) n thermal power staton on the base of epert knowledge and by usng smulaton approach. The system for ecologcal montorng of the consdered project s realzed as multple system for consecutve analyss of the pollutants for each gas outlet. The used scheme allows multfunctonal carryng out of the Contnuous Emsson Montorng Systems (CEMS) for realzaton of optmal control of the combuston processes ecept for performance of ts man purpose. The tme perod of multpleng of each gas outlet s To = 150 s (.5 mn). In ths way the analyss data of oygen ( O ), ntrogen odes ( ) and sulphur dode ( ) for each gas outlet enter every 5 mn, 10 mn, or 15 mn dependng on the number of the workng steam generators (respectvely 1, or 3).

2 The determnaton of the substtuted values of gas emssons of O for each gas outlet (every.5 mn) s accomplshed accordng to the prncpals of the predctve emsson montorng. The hgh accuracy of the predctve emsson montorng makes t equvalent and n some cases better than the contnuous emsson montorng or sometmes t s the only possble method. The necessary nformaton for reconstructon of the mssng data of the components of the flue gases s delvered from two sources: - From the contnuous emsson montorng system, whch analyses the concentratons of O of the waste flue gases n each gas outlet at the above mentoned tme ntervals. - The basc technologcal varables enter from the decentralzed control system of the unt. The usng of smple nterpolaton methods s feasble only under certan regmes. In other cases the obtaned substtuted data contans consderable errors. That mposed development of methods for reconstructon of the mssng data, whch are vald n all the technologcal condtons. These methods could be classfed as follows: - wthout usng of ndrect sources of nformaton nterpolaton and/or fltraton; - by usng ndrect sources of nformaton regresson relatons and/or neural networks; - combned approach wth nterpolaton and by usng of ndrect sources of nformaton. MTHEMTICL MODELS FOR PEMS Detaled analyss of the predctve abltes of eght types of mathematcal models s provded. They descrbe the relatonshps between the O concentraton n one sde flue gases duct as a functon of the drect measurements n the same duct (but wth a bgger sample tme), the drect measurements of gases concentraton n the neghbor duct and all avalable addtonal measurements receved from SCD or DCS. The nvestgated mathematcal models are gven n Table 1. The evaluaton of the nvestgated mathematcal models accuracy s accomplshed by calculaton the relatve mean square error (MSE) ( ) between predcted and drectly measured values for the same dscrete tme. The followng formulas are used: C 100, %, (1) m C N ^ where 1 e C C C, N 1 1 m 1 N e C C N 1, where N s the sze of the test data set; are the predcted values of the gas concentratons obtaned from e the mathematcal models and C are the correspondng measured values. Table 1: Investgated mathematcal models Descrpton of the used sources of nformaton and the type of mathematcal relatonshps 1 Equal concentratons n the output ducts and B Defnton of the concentraton usng drect measurements and nterpolaton 3 Correcton usng the rato between wet oygen concentratons n the output ducts and B 4 Mathematcal models derved on the base of drect measurements 5 Hybrd mathematcal models (drect measurements) 6 Model based on the rato front / back oygen concentraton 7 Mathematcal models on the base of control actons and states - Regressve models - Neural Network models 8 Combned mathematcal model C^ Notaton M1 M M3 M4 M+M3 M+M4 M+M3+M4 MB1 MB MB1 MB M+MB The most approprate mathematcal models for development of predctve montorng systems are presented and dscussed n the followng sectons. MTHEMTICL MODELS USING DIRECT MESUREMENTS Concentraton determnaton by nterpolaton of drect measurements (Model M) Drect measurements only from CEMS durng the correspondng sample tme are used dependng on the load of the power staton. When one, two or three unts work smultaneously drect measurements n each one of the gas outlets left () and rght (B) are carred

3 out respectvely every 5 mn, 10 mn or 15 mn. The opportunty to reconstruct the mssng measurements n order to provde a set wth.5 mn sample nterval by nterpolaton s nvestgated. The reconstructon of the mssng data for the gas concentratons s accomplshed usng Lagrange nterpolaton polynomal. The nterpolaton approach defnes a functon f(), whch pass through gven number of epermental measurements (Fg. 1). Usng ths functon the mssng values B 1, B, B 3, B 4 and B 5 of the relevant component y( ) are calculated. Emsson Montorng Systems (CEMS), are usng lnear B relaton. For eample the mssng concentraton of n the outlet duct B s estmated usng the followng epresson: B O e NÔ () O We WBe In Table 3 the average values of the relatve mean square error for the predcted estmatons of obtaned from the multtude smulatons are presented. Table 3: verage values of Gas, % Fgure 1: Interpolaton procedure The nterpolaton could be realzed n the followng way: - dependng on the number of the ponts used for nterpolaton (, 3, 4,...); - wth averagng or not the nterpolated values, usng or not movng horzon of nterpolaton. Durng the reconstructon of the mssng values of the relatve mean square error ( ) ncreases lnearly dependng on the samplng tme. The results for are presented n Table. Table : Summarzed nterpolaton results Number of the workng unts One unt T 0 5 mn Two unts T 0 10 mn Three unts T 0 15 mn, % Usng of «wet» oygen (Model M3) The nformaton sources are: - specally nstalled sensors on the base of zrconum ode ( ZrO ) n each of the gas outlet ducts and B, measurng contnuously the concentratons of the We WBe wet oygen O and O ; - the Contnuous Emsson Montorng Systems (CEMS). The mssng concentratons of the gas components O, n the gas outlet duct, where there s no current measurement when n the other gas outlet duct of the same unt there s measurement by the Contnuous Mathematcal models usng the drect measurements n the gas outlet ducts and B (Model M4) Ths mathematcal model s created on the base of the data from the Decentralzed Control System (DCS) and the Contnuous Emsson Montorng Systems (CEMS). The correspondng concentratons of the gas components e e e n the ducts and B O respectvely Be Be Be O are measured n accordance wth the the set sequence for analyss. For calculaton of the gas emsson vector n the duct B C ˆ B (k), when only the emssons n the duct are measured, the net relaton s used: ˆ L R f b e WB C (k) f (V, V,, O,, O, O,S ) (3) B The dependence for the emssons n the duct s determned n analogous way. The relatve errors for predcton of the gas emssons from the mathematcal models are gven n Table 4. Table 4: Relatve errors of the gas emssons predcton, % O Et flue Et flue B The epermental values (*) and the predctve values of the concentratons of n the duct B, where =3.9%, are llustrated at Fgure.

4 Regressve models (Model MB1) In Table 5 the relatve mean square errors ( ) of the predcted values of the gas concentratons, gven from the regressve model (Model MB1) n comperson to the real epermental data are shown. Table 5: Results from the regressve model Gas O, % Fgure : Epermental and predcted values of the concentratons MTHEMTICL MODELS BSED ON THE UNIT STTE ND CONTOL DT (MODEL MB) The development of these mathematcal models s based on the data from the unt Decentralzed Control System. It s not necessary ecologcal montorng system to be avalable. The physcal nsghtconsderatons and the prelmnary nvestgatons of the models have shown ther sensblty to the total ar flow rate V and to the fuel flow rate B. The mathematcal models are ndvdually derved for predcton of the gas concentratons n each one of the et ducts and B. For eample the created model for et duct has the followng form: ˆ L R L R f b C (k) f (B, B, V, V,, O, ) (4) where gas components Ĉ s a vector of the predcted concentratons of Ô, N Ô and S Ô. ll the varables n the rght part of the equaton (4) are measured contnuously and are defned as follows: L R B and B are the fuel flow rate correspondngly n the left and n the rght combuston chamber; L R V and V are respectvely the flow rate of the total ar n left and rght ar duct; fm and bm O are the measured concentratons n the front and the back part of the correspondng combuston chamber; T s the temperature before the secondary steam superheater. Relaton appromatons by regressve models (Model MB1) and neural network models (Model MB) are studed. The average values of the unt emssons of both flue gas ducts are accepted as representatve value. Neural network models (Model MB) ccordng to the dependence (4) mathematcal models based on neural networks (Model MB) for predcton of the concentratons of O n each of the gas outlet ducts and B are developed. The hghest predcton accuracy s acheved by usng two layers neural networks of Cascade-Forward Backdrop type wth sgmod transfer functon n the hdden layer and wth lnear one n the output layer. The relatve mean square error ( ), comparng the predcted values of the gas concentratons towards the real epermental data, are gven n Table 6. Table 6: Results from the neural models Gas O, % HYBRID MTHEMTICL MODELS Combnng dfferent types of modelng technques the total estmaton accuracy could be mproved wth 10-15% (Hadjsk 1999). Etensve smulaton studes on the possbltes of combnng the consdered mathematcal models and other created mathematcal models n hybrd ones are carred out. The aggregaton s performed by weghted summaton of the models outputs. Under aggregaton of two mathematcal models the predcted concentraton of the analyzed gas component Ĉ obtaned from the hybrd model s calculated as follows: Ĉ Ĉ (1 ) Ĉ (5) 1 By analogy at aggregaton of three mathematcal models the epresson for Ĉ s: Ĉ Ĉ Ĉ (1 ) Ĉ (6) 1 3

5 The nde n the epressons (5) and (6) s addressed to one of the gases O or. The predcted concentratons of the consdered gas on the ndvdual mathematcal models are denoted by Ĉ 1, Ĉ and Ĉ 3. and represent weght coeffcents, varyng n the nterval [0,1]. The selecton of the combnaton of the ndvdual mathematcal models, whch are nvestgated to be aggregated, s accomplshed takng nto account such crtera as accuracy, relablty of the measurement and the costs mnmzaton for sensors and analyzers of the gas components. procedure for aggregaton of the mathematcal models, based on the methods of the nonlnear programmng s proposed. In ths procedure the ndependent varables are and. The objectve functon s equaton (1) of the relatve mean square error for the concentraton Ĉ, estmated by usng epressons (5) or (6). The suggested procedure s developed on the base of parallel smulaton computatons of the ndvdual models for test data sets completed wth epermental data of the gas concentratons and subsequent aggregaton of the models. On Fgure 3 a graphc llustraton of created hybrd mathematcal model, applyng the proposed aggregaton procedure s presented. varety of combnatons of models M, M3 and M4 wth the models MB1 and MB are possble. The carred out analyss has shown that the hybrd models of type M+MB are more perspectve for the purposes of the ecologcal montorng. The results of the accuracy evaluaton of the ndvdual models and of the hybrd models are summarzed n Table 7. The nvestgated models are ranged accordng to ther accuracy. Table 7: Rangng of the models on ther accuracy Notaton Mean square error, % Range poston M M M3 3.7 M M+M M+M M+M3+M MB MB MB M+MB M+MB DT Model Model 3 Model ggregaton C^ CONCLUSIONS The smulaton results usng developed algorthms for reconstructon of the mssng data of the flue gases concentraton could be summarzed as follows: 1. The methods usng drect measurements guarantee hgher accuracy. 4 Fgure 3: Hybrd mathematcal model Numerous statstcal analyses have shown the domnant effcency of the net three combnatons of models used drect measurements: 1. Interpolaton model (M) and model based on the rato of the oygen content n flue gas ducts and B (M3). Interpolaton model (M) and model based on the drect measurements (M4) 3. Interpolaton model (M), model usng wet oygen (M3) and model based on the drect measurements (M4).. The rangng of the methods on the average relatve mean quadratc error, presented n Table 7, shows dstngush prorty of the methods based on models usng drect measurements (M4) and on measurng the wet oygen (M3). 3. The results obtaned from the model based on measurng of wet oygen (M3) depend only on the drect measurement data and they are not nfluenced by the operatonal condtons. That permts these algorthms to be used n the whole range of technologcal regmes, namely statc, quas-statc and dynamc, wthout applyng of any adaptve procedures. 4. If durng the eploataton of the wet oygen sensor some dffcultes of foulng and smlar ones appear, whch decrease the measurng precson, the nterpolaton model (M) wll

6 reman to be effcent wth the prevous accuracy. 5. The hybrd mathematcal model (M+M3+M4) (Fgure 3) guarantee hgh accuracy. The eventual automatc elmnaton consequently of the models M3 and M4 wll mantan the effcency of the emsson montorng system. Thus the hybrd model (M+M3+M4) possesses robust propertes wth respect to the measurng condtons. 6. The models usng data from the Decentralzed Control System are more mportant n solvng the task of optmzaton of the combuston process n the steam generators under the ecologcal restrctons for the gas emssons and when Contnuous Emsson Montorng Systems (CEMS) s not avalable. CKLEDGMENTS Ths paper s supported n part by the contract No. TH1408/04 of the Bulgaran Natonal Scence Fund. REFERENCES lberta Envronmental Protecton Contnuous Emsson Montorng System (CEMS) Code. Envronmental Servce. Bulgaran Mnstry of Envronmental Protecton Regulaton 6: Measurements of the r Polutants from Industral Sources (n Bulgaran). Bulgaran Mnstry of Envronmental Protecton Instructon for the Requrements for Regstraton, Processng, Storage, Presentaton and Evaluaton of the Results from the Contnuous Emsson Montorng Systems (n Bulgaran). Code of Federal Regulatons, Part 60, ppend. Code of Federal Regulatons, Part 75, ppend E. Cretnk, J.; M. Slbar; M.Vedenk-Novak; and M.Hocewar. Contnuous Emsson Montorng Systems n Termoelektrarna Toplarna Ljubljana. Eberhard, W.H Desgn of an lternatve Emssons Montorng System for Clean r ct Complance. Power-Gen 95, nahem, CF. Ekhus, L. and L.Black. Predctve Emssons Montorng Systems at a TMP Mll. EP Handbook Contnuous Emsson Montorng Systems for Non-crtera Pollutants. EP/65/R- 97/001/. Ghen, T. W.; H. Chu; W. C. Hsu; T. K. Tseng; C. H. Hsu; and K. Y. Chen Feasblty Study on the Predctve Emsson Montorng System, ppled to the snta Power plant of Tawan Power Company. Journal of the r&waste Management ssocaton, Vol. 53. Gmonez,.; M. Estela; B. Benot; F. Peledan; T. bbas; and J. L. P. Neto. dvance Predctve Tool to Optmze Combuston and Emsson Performance. Cords, RTD-Projects, Hadjsk, M Hybrd Modelng and Inference Model-Based Control of Complcated Technologcal Plants. Studes n Informatcs and Control, Vol. 8, No.1. Joseph, J. and I. Macak The Proc. and Cons. of Predctve, Parametrc and lternatve Emsson Montorng Systems for Regulatory Complance. Proc. of the r and Waste Management ssocaton Conference, Nashvlle, TN, US. Topcal Report CLEN Coal Technology, Software Systems n Clean Coal Demonstraton Projects. Unted States Envronmental Protecton gency (EP) Contnuous Emsson Montorng Systems. September. Unted States Envronmental Protecton gency Calculaton and Interpretaton of ccuracy for Contnuous Emsson Montorng Systems (CEMS). US Envronmental Protecton gency Contnuous Emsson Montorng Informaton, Gudance, etc. Technology Transfer Network Emsson Measurement Center, Fnal report.