REAL TIME PULVERISED COAL FLOW SOFT SENSOR FOR THERMAL POWER PLANTS USING EVOLUTIONARY COMPUTATION TECHNIQUES

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1 ISSN: (ONLINE) DOI: 0.297/ijs ICAC JOURNAL ON SOF COMPUING: SPECIAL ISSUE ON SOF COMPUING HEORY, APPLICAION AND IMPLICAIONS IN ENGINEERING AND ECHNOLOGY, JANUARY 205, VOLUME: 05, ISSUE: 02 REAL IME PULVERISED COAL FLOW SOF SENSOR FOR HERMAL POWER PLANS USING EVOLUIONARY COMPUAION ECHNIQUES B. Raja Singh, S. Rominus Valsalam 2, H. Pratheesh 3, K.. Sujimon 4 and C. Aditi 5 Control and Instrumentation Group, Centre for Development of Advaned Computing, India rajasingh@da.in, 2 rominus@da.in, 3 pratheesh@da.in, 4 sujimon@da.in, 5 aditi@da.in Abstrat Pulverised oal preparation system (Coal mills) is the heart of oalfired power plants. he omplex nature of a milling proess, together with the omplex interations between oal quality and mill onditions, would lead to immense diffiulties for obtaining an effetive mathematial model of the milling proess. In this paper, vertial spindle oal mills (bowl mill) that are widely used in oal-fired power plants, is onsidered for the model development and its pulverised fuel flow rate is omputed using the model. For the steady state oal mill model development, plant measurements suh as flow rate, differential pressure aross mill et., are onsidered as inputs/puts. he mathematial model is derived from analysis of energy, heat and mass balanes. An Evolutionary omputation tehnique is adopted to identify the unknown model parameters using on-line plant data. Validation results indiate that this model is aurate enough to represent the whole proess of steady state oal mill dynamis. his oal mill model is being implemented on-line in a 20 MW thermal power plant and the results obtained are ompared with plant data. he model is found aurate and robust that will work better in power plants for system monitoring. herefore, the model an be used for online monitoring, fault detetion, and ontrol to improve the effiieny of ombustion. Keywords: Pulverised Coal, Primary Air, Mill Differential Pressure, Fitness Funtion, Raw Coal. INRODUCION In oal fired power plants, pulverised oal (PF) flow from oal mills is to be measured aurately for maximising ombustion effiieny and improved dynami response to load hanges. However, realization of this is diffiult due to the nonavailability of aurate measurement of on-line pulverised oal flow. he funtion of oal mills in power generation station is to grind the large raw oal feed and to supply dry and finely pulverised oal to the furnae for faster ombustion and energy release. In addition to the preise fuel supply, the mill-ontrol system has to ontrol primary flow in a ertain ratio to the PF flow, thereby maintaining the effiieny of unit energy release and absorption thereby reduing environment emissions. An area of power plant ontrol that has reeived muh less attention from modelling and ontrol speialists is the oal mill. It is now aepted that the oal mills and their poor dynami response are major fators in the slow load take-up rate and plant shut-down. A Coal mill model using Evolutionary omputation tehnique reported in []. Expertise in mill modelling has been developed over many years whih are mainly empirial. Although some progress has been made in the development of mill models, it is still very diffiult to obtain exat analytial results beause of the intrinsi omplexity of oal pulverising proess whih omprises of two-phase flow and heat-transfer proess. Using onventional methods, many results are reported in the form of transfer funtions whih are not derived from physial priniples [2]. However with the advane in modern omputer ontrol systems, all measured signals an be stored in data bases overing long periods of time. hese data an be used for modelling with additional field tests, but require suitable modelling tehniques whih are to be identified properly. A novel oal mill modelling tehnique for E-type oal mill and dynami behaviour are developed using geneti algorithms [3]. Geneti Algorithms (GAs) have been suessfully applied to problems in business, engineering, and siene. GAs are stohasti, population-based searh and optimization algorithms inspired by the proess of natural seletion and genetis [4]. A major harateristi of GA is that, it works with a population, unlike other lassial approahes whih operate on a single solution at a time. A fitness funtion is needed for differentiating between good and bad solutions. Unlike lassial optimization tehniques, the fitness funtion of GAs may be presented in mathematial terms, or as a omplex omputer simulation, or even in terms of subjetive human evaluation. Fitness funtion generates a differential signal in aordane with whih GAs guide the evolution of solutions to the problem [5]. Seletion hooses the individuals with higher fitness as parents of the next generation. In other words, seletion operator is intended to improve average quality of the population by giving superior individuals a better hane to get opied into the next generation [6] [7]. GA is tried in some of the thermal power plant modelling and estimation problems [8] - [0]. Coal mill model development is based on measurable variables from physial analysis and real time plant data. Sine a oal mill is a multi-input-multi-put nonlinear system with a oeffiient group to be determined, onventional identifiation methods tend to diverge due to the multivariable harateristis, strong oupling, time delays, as well as to the pollution of onsite data by noise. In this paper, GA is applied to develop the oal mill model and to estimate the pulverised oal flow using real time on-site plant data. 2. COAL MILL MODELLING In thermal power plant, pulverization of oal is arried by oal mill. Raw oal is moved from the storage to the mill by onveyor mehanism. he type of oal mill envisaged for our model is bowl mill whih is shown in Fig.. Raw oal is introdued near the entre of the grinding table through the oal feed pipe. he oal moves ward on the rotating table and it is ground under the roller. he primary is generated by mixing hot and old, and it is introdued through an opening near the base of the oal mill. he primary flows upward at the grinding table 9

2 B RAJA SINGH et al.: REAL IME PULVERISED COAL FLOW SOF SENSOR FOR HERMAL POWER PLANS USING EVOLUIONARY COMPUAION ECHNIQUES periphery and the is mixed with the pulverised oal. Some of the larger partiles fall bak on the table and they are ground again. Finer partiles of the pulverised oal pass into a lassifier. In the lassifier, the oversized oal partiles fall bak to the grinding table and the finer partiles are arried through the let of the oal mill to the burner of the boiler. Q Q oal COAL MILL Q PF Q p Q e Fig.3. Heat Balane Model of Coal Mill Fig.. Setional View of Bowl Mill Coal mill model development involves a mathematial model with sixteen unknown mill speifi parameters and estimation of these model parameters using Geneti Algorithm. he oal mill model is derived from physial mass and heat balane. In order to obtain the simple model for ontrol purpose, it is assumed that the pulverizing mehanism in the mill is simplified, i.e., lassifiation operation is not inluded. Coal is grouped into two ategories only; that are Pulverised oal and Raw oal. he oneptual mass and heat balane models of the oal mill are shown in Fig.2 and Fig.3 respetively. In Fig.2, the raw oal flow (W ) is introdued into the mill and pulverised oal (W ) omes. he oal mass is onverted into pulverised mass through grinding. Aording to the assumption, at a partiular instant there will be ertain amounts of raw oal (M ) and pulverised oal (M ) in the mill. he raw oal is fed into the mill by raw oal feeders for pulverizing at a mass flow rate of W. By grinding the raw oal M in the mill, the pulverized oal M is produed and arried by the warm flow at the mill let with a pulverised oal mass flow rate of W. From the mass balane point of view, the total mass of the pulverized oal put from the mill at the flow rate W should be equal to the total mass of the raw oal flowing into the mill at the flow rate W eventually. W Coal mill W, M M W Fig.2. Mass Balane Model of Coal Mill In Fig.3, the hanges in mill let temperature are onsidered as the result of heat balane. he temperature inreases with the heat ontributed by hot primary entering the mill and the heat generated by grinding. It dereases by the heat energy lost due to oal and moisture entering the mill and to the hot primary, pulverised fuel leaving the mill. From the above given Mass and Heat balane, the model is derived. So the mill let temperature an be written as, where, = Q oal + Q + Q p + Q e Q + k 7 () Q oal - Heat of oal inlet flow Q - Heat of primary inlet Q p - Heat generated due to grinding Q e - Heat emitted from mill body to environment Q - Heat of pulverised oal let from mill he variables used for the Coal mill model is as follows: Input variables are W, in, and W ; Intermediate variables are M, M, P and W ; Output variables are, P mill and P. 2. MAHEMAICAL MODEL he Model onsists of four Differential Equations and three algebrai equations. 2.. Differential Equations: d dp dm dm W k M (2) 5 k5 M k6ppam (3) k M k2m k3p (4) k W k W k W W k M k M k k 7 in 2..2 Algebrai Equations: DISCREISED MODEL (5) P = k 6 M + k 7 M + k 8 (6) P mill = k 9 P pa + P (7) W = k 6 P pa M (8) In order to alulate the on-line Pulverised Coal flow in real time, the model needs to be disretised with a suitable numerial 92

3 ISSN: (ONLINE) ICAC JOURNAL ON SOF COMPUING: SPECIAL ISSUE ON SOF COMPUING HEORY, APPLICAION AND IMPLICAIONS IN ENGINEERING AND ECHNOLOGY, JANUARY 205, VOLUME: 05, ISSUE: 02 tehnique, so the differene equation model is used. Using standard Euler s method, we solve the differential Eq.(9) to Eq.(2). M (k+) = {[W (k) - k 5 M (k)]}+m (k) (9) P M (k+) = {[k 5 M (k)-k 6 P pa (k)m (k)]}+m (k) (0) k {[ km k2m( k) k3p P {[ k {( k k k [ k M in 6 k ] W 2 k 5 k ] } )[ W 7 k W 3 W ]} M k 8 ] ] } () (2) P(k+) = k 6 M (k+) + k 7 M (k+)+k 8 (3) P mill (k+) = k 9 P pa (k+) + P (k+) (4) W (k+) = k 6 P pa (k+)m (k+) (5) where, is the sampling time 3. PARAMEER ESIMAION USING GENEIC ALGORIHM 3. GENEIC ALGORIHM GA belongs to the lass of probabilisti searh proedure known as evolutionary algorithms that use omputational models resembling natural evolutionary proesses to develop omputer-based problem solving tehnique. It provides a robust way of exploring virtually any solution spae where an objetive funtion is defined, in pursuit of its global optimum and it manipulates a population of solutions in parallel. Eah trial solution in the population is oded as a single vetor, is termed as hromosome. A shared environment determines the objetive value or raw performane of eah individual in the population. hese objetive values are, in turn transformed into fitness values for the requirements assoiated with the subsequent statistial seletion. he GA is typially omposed of three fundamental operator s viz., Seletion, Crossover and Mutation. Seletion is a proess in whih eah hromosome is reprodued in proportion to its own fitness relative to the other hromosomes in the population. Following the seletion proess, other geneti operators rossover and mutation are performed to generate the offspring of the seleted hromosomes. obtained easily with noise-ontained plant data. he onventional gradient-based optimization tehniques, suh as least-squares and steepest-desent methods apply to loal optimization problems, not taking into aount of the fat that there may be many loal minima of the merit funtion in the spae of all variables. hey are not suitable for this underlying modelling issue, arisen from unobservable states involved in the model and a wide range of parameter searh domain leads to inadequateness for identifying so many parameters in parallel. In order to identify the 6 unknown parameters k, k 2 k 7 in the oal mill mathematial model in Eq.(9) to Eq.(5), the Geneti Algorithm (GA) is used sine it has been proved that GA is a robust optimization method for the parameter identifiation problems. he single-population real-value GA is hosen suh that the fitness funtion ompromises the errors between the normalized oal mill measured puts and the normalized model simulated puts. Applying the sheme of the parameters identifiation shown in Fig.5, the 6 unknown parameters are identified. he performane of the GA is influened strongly by the fitness funtions used whih is given in Eq.(6) to Eq.(9). e e n n ˆ ˆ (6) n n P Pˆ P Pˆ mill mill 2 mill mill (7) P n n P Pˆ e P Pˆ 3 (8) P k, Pˆ k Pˆ mill k are the measured puts of k, Pˆ k ˆ k are the where, ˆ and the mill model at instant k ; ˆ and simulated puts of the mill model at instant k. P mill, P mill and P are the maximum limit value of these variables. he sequene of Parameter estimation by GA is shown in Fig.6. he ultimate target of this optimization is to minimize the error between the model put and measured plant data. 3.2 OPIMIZAION OF COAL MILL MODEL WIH GA he oal mill mathematial model equations given in Eq.(9) to Eq.(5) ontain 6 unknown oeffiients whih are to be determined. It should be noted that in this model, some model variables are non-measurable. For suh an intrinsially omplex dynami milling proess, the analytial solution annot be Fig.5. GA Optimization for Coal mill model 93

4 B RAJA SINGH et al.: REAL IME PULVERISED COAL FLOW SOF SENSOR FOR HERMAL POWER PLANS USING EVOLUIONARY COMPUAION ECHNIQUES SAR icon GENERAE INIIAL POPULAION RANDOY AND INIIALIZE CROSS OVER AND MUAION PROBABILIY CALCULAE FINESS VALUE OF EACH INDIVIDUAL DACS Communiation 42 AI SIGNALS FROM PLAN Geneti Algorithm SELECION CROSSOVER SERVER NEWORK SWICH WORK SAION Fig.6. Flow hart of Geneti Algorithm he fitness funtion used for GA is given below: Fitness N NO N W e W2 e2 W3 e3 t0 MUAION NEW GENERAION BES FI SOLUION SOP YES (9) 4. ON-LINE IMPLEMENAION OF SOF SENSOR FOR PULVERISED COAL FLOW MEASUREMEN he onfiguration diagram for the on-line implementation of Pulverised oal flow soft sensor setup is given in Fig.7. For eah mill, seven field signals are identified as the mill model input (4 nos.) and put variables (3 nos.) whih are onneted to a Remote erminal Unit with proper field isolation and are then given to CDAC s icon ontroller (Industrial Controller). he Geneti Algorithm driven system identifiation to ompute the unknown model parameters runs in the work station and the oal mill model runs in the icon ontroller in order to provide real time measurement of pulverised oal flow. Eah GA run of a mill requires 500 data points of the seven signals logged at seond interval from the database logger. he san time for eah oal mill model loop is seond. One the unknown parameters are estimated using GA, parameters are downloaded to icon at a definite time interval to ompute the Pulverised oal flow from the model. Every 4 hours, the unknown parameters of the model are identified on-line using the Evolutionary omputational tehnique (Geneti Algorithm) and updated in the model to math the urrent dynamis of the oal mill proess. Similar omputations are done for all running mills of a 20 MW boiler and summed up to arrive at the total pulverised oal flow for the boiler. Fig.7. System onfiguration diagram of the Pulverised oal flow soft sensor he real time Coal mill model response with respetive plant signals is shown in Fig.8 to Fig.. In Fig.8, the total pulverised oal flow (from all the running oal mills) to the furnae is ompared with raw oal flow signal of the plant. he auray of the pulverised oal flow soft sensor for the plant steady state ondition is ± 0.05%. Subsequently the soft sensor put for one mill in full load ondition is shown in Fig.9. he Fig.0 to Fig.2 show the omparison between model put variables and the respetive plant signal for a single mill. From the graph, it is lear that the model is aurate enough and exatly follows the real time dynamis of the mill. Currently in the thermal power plant monitoring senario, there is no physial measurement tehnique is readily available to measure the pulverised oal flow. So this on-line pulverised oal flow soft sensor paves a way to get best possible ontrol auray on the master pressure ontrol of oal based thermal power plant where one of the input for the steam pressure ontrol is amount of oal flow to the ombustion. Also, plant Engineers/Operators will have a better visibility ab the exat pulverised oal whih atually goes to the furnae for ombustion rather than having an approximated approah based on raw oal flow (measurement prior to pulverisation proess). Fig.8. HMI frame of otal Pulversied oal flow (model put) vs otal raw oal flow (plant signal) 94

5 ISSN: (ONLINE) ICAC JOURNAL ON SOF COMPUING: SPECIAL ISSUE ON SOF COMPUING HEORY, APPLICAION AND IMPLICAIONS IN ENGINEERING AND ECHNOLOGY, JANUARY 205, VOLUME: 05, ISSUE: 02 Fig.9. Comparison of Model put and Plant data for Pulverised oal flow (one oal mill) Fig.2. Comparison of Model put and Plant data for Mill Current 5. CONCLUSION Fig.0. Comparison of Model put and Plant data for Mill Outlet emperature In this paper, a Coal mill model is developed for steady state ondition and is used to estimate/predit the internal dynamis of oal mill and to ompute the pulverised oal flow measurement of the oal pulverising system. he Geneti Algorithm is used to identify the unknown model parameters of the oal mill. he system is implemented in a 20 MW thermal power plant for a bowl mill type pulverising system and the results are ompared and validated with on-site plant data. From the results, it has been observed that the model puts are mathed exatly to the measured plant signals. he ome of this work will be useful in assessment and predition of mill performane and in seeking improvement in design, operating proedures and ontrol strategies for pulverizing proess of oal mill. It has opened up a way to implement advaned ontrol algorithms in oal pulverizing proess for better ontrol and state diagnosis to improve the pulverising proess. he Coal mill model is also a pre-requisite for implementing advaned ontrol algorithms for master pressure ontrol of a thermal power plant. here is enormous sope for applying estimation tehniques like Kalman Filter to fine tune the model and novel intelligent hybrid Evolutionary tehniques in plae of GA to ahieve further auray in the result. ACKNOWLEDGEMEN Fig.. Comparison of Model put and Plant data for Mill Differential Pressure he paper is based upon the R&D projet funded by Department of Eletronis and Information ehnology (DeitY), Ministry of Communiations and Information ehnology (MCI), Govt. of India, under the Automation Systems ehnology Centre (ASeC) programme implemented in the Control and Instrumentation Group (CIG) of CDAC, hiruvananthapuram, Kerala, India. he authors would like to thank ESDA division of DeitY for extending their exeptional enouragement and tehnial support for our projets. We express our heartfelt gratitude to Late Dr. V. Sundararajan of CDAC Pune who ontributed in framing the GA methodology. 95

6 B RAJA SINGH et al.: REAL IME PULVERISED COAL FLOW SOF SENSOR FOR HERMAL POWER PLANS USING EVOLUIONARY COMPUAION ECHNIQUES hanks to M/s Evonik Energy systems, Noida for providing valuable suggestions and help during the system design and field implementation. Prof. Henry Wu, University of Liverpool, UK has offered us valuable suggestions on using his oal mill mathematial model equations for suessfully developing the on-line soft sensor. NOMENCLAURE W : Mass flow rate of Raw oal into mill (kg/s) in : Inlet temperature of oal mill ( C) P pa : Primary differential pressure (mbar) W : Primary flow rate into oal mill (kg/s) M : Mass of Raw oal in mill (kg) M : Mass of pulverised oal in mill (kg) P : Mill produt differential pressure (mbar) W : Mass flow rate of pulverised oal of mill (kg/s) : Outlet temperature of oal mill ( C) P mill : Mill differential pressure (mbar) P : Mill Current (A) REFERENCES [] Damian Flynn, hermal Power Plant Simulation and ontrol, he Institution of Eletrial Engineers, [2] K. E. Bollinger and H. R. Snowden, he Experimental Determination of Coal Mill Models, IEEE ransations on Power Apparatus and Systems, Vol. PAS-02, No. 6, pp , 983. [3] J. L. Wei, J. H. Wang and Q. H. Wu, Further study of oal mill modeling by mahine learning based on on-site measurement, Proeedings of the 6 th International Conferene on Systems Engineering, pp , [4]. Bak, D. B. Fogel and Z. Mihalewiz, Handbook of Evolutionary Computation, Institution of Physis Publishing and Oxford University Press, 997. [5] Y. P. Chen, Extending the Salability of Linkage Learning Geneti Algorithms, Springer - Artifiial Intelligene, Vol. 90, [6]. Bak, Seletive pressure in evolutionary algorithms: A haraterization of seletion mehanisms, Proeedings of the First IEEE Conferene on Evolutionary Computation, Vol., pp , 994. [7] Y. G. Zhang, Q. H. Wu, J. Wang, G. Oluwande, D. Matts and X. X. Zhou, Coal mill modeling by mahine learning based on on-site measurement, IEEE ransations on Energy Conversion, Vol. 7, No. 4, pp , [8] P. Zahariades, J. L. Wei and J. Wang, Development of a ube-ball Coal Mill Mathematial Model Using Partile Swarm Optimization, Proeedings of the World Congress on Engineering, Vol. I, [9] Omar Mohamed, Jihong Wang, Shen Guo, Bushra Al-Duri and Jianlin Wei, Modelling Study of Superritial Power Plant And Parameter Identifiation Using Geneti Algorithms, Proeedings of the World Congress on Engineering, Vol. II, 200. [0] Heng Wang, Min-ping Jia, Peng Huang and Zuo-liang Chen, A study on a new algorithm to optimize ball mill system based on modeling and GA, Elsevier Energy Conversion and Management, Vol. 5, No. 4, pp ,