Optimizing of a gas turbine cycle by Genetic and PSO algorithms

Transcription

1 ISSN Online): - Issue, August, pp. - Optimizing of a gas turbine yle by Geneti and algorithms Ahmad Khosravi, Mofid Gorji Band-pay, Farivar Fazelpour Abstrat xergoeonomi is one of the best effetive tools to find the best solutions for optimization of thermal systems. In this paper, operating parameters of a gas turbine power plant that produes MW of eletriity is determined by using exergoeonomi priniples to optimize by geneti and algorithm. volutionary algorithms suh as geneti algorithm ) and partile swarm optimization ) are applied to minimize the ost funtion and optimally adjusting five design parameters. Comparison results exhibit among the algorithm, geneti algorithm and the base. Simulated results indiate that appliation of method leads to better results in terms of ost per unit. Keywords Gas turbine, Partile swarm optimization ), Geneti Algorithm ), exergy. T I. INTRODUCTION HIS researh use xergy analysis, a method that uses the onservation of mass and onservation of energy priniples together with the seond law of thermodynamis for the design and analysis of thermal systems[]. The method that we use to evaluate power plant is an exergy analysis that is partiularly suited for improving the effiieny of a omponent. The results help to have a omprehensive view to a power plant for design. This information an be used to improve thermal systems, guide efforts to redue soures of ineffiieny in existing systems, and evaluate system eonomis. xergy is defined as the maximum theoretial useful work obtained if a system S is brought into thermodynami equilibrium with the environment by means of proesses in whih the S interats only with this environment. The gas turbine yle is still a preferred topi for exergy analysis. Several papers ontinue to appear in arhival publiations, onfirming the idea that the Brayton yle espeially with the most reent advanes in materials and blade ooling tehnology) will see some breakthrough in the Department of nergy Systems ngineering, IAU South Tehran Branh, khosravi.a@mail.om Faulty of Mehanial ngineering, Noshirvani University Department of nergy Systems ngineering, IAU South Tehran Branh near future. Chambadal Int. J. of Thermodynamis, Vol. No. ) 9a,b), Gasparovi & Stapersma 9), Bandura 9), Vivarelli et al. 9a,b), Harvey & Rihter 99), Pak & Suzuki 99), Fiashi & Manfrida 998a,b), Abdallah et al. 999), Di Maria & Mastroianni 999), Faletta & Siubba 999), Lombardi ), Zheng et al. ), Alves & Nebra ), Jin et al. ), Song et al. ), Aronis & Leithner ), Ishida ), Kopa & Zemher ) steam-injeted GT), Sue & Chuang ) all dealt with both global and loal aspets of the problem, and some of the works expliitly addressed transient operation regimes. In reent years, there has been a growing emphasis on optimization of systems of energy to derease energy demand. At the very least, several workable designs should be generated and the final design, whih minimizes or maximizes an appropriately hosen quantity, seleted from these. In general, many parameters affet the performane and ost of a system. Therefore, if the parameters are varied, an optimum an often be obtained in quantities suh as power per unit fuel input, ost, effiieny, energy onsumption per unit output, and other features of the system. It is obvious that the first step in optimization of the power plant an find a way of inreasing effiieny and dereasing onsumption of fuel. When we onserve energy resoures, our nation an enjoy leaner air and a healthier environment, and we an help protet the limate by reduing greenhouse gases. For approahing to these aims, we an use thermoeonomi that is a branh of engineering that ombines exergy analysis and eonomi priniples to provide a design evaluation to optimize a thermal system based on a set of variables alulated for eah omponent of the system. We an onsider thermodynamis as exergy-aided ost minimization and onsumption of energy. The method that we use to evaluate power plant is an exergy analysis that is partiularly suited for improving the effiieny of a omponent. The results help to have a omprehensive view to a power plant for design. This information an be used to improve thermal systems, guide efforts to redue soures of ineffiieny in existing systems, and evaluate system eonomis.

2 ISSN Online): - Issue, August, pp. - II. TH DSCRIPTION OF CYCL Figure shows the shemati diagram of a gas-turbine plant and the exergy flows and the state points whih was aounted for in this analysis[]. All parameters have alulated based on the values of measured properties suh as pressure, temperature and mass flow rate at various points in the gas turbine on ISO ondition by THRMOFLX software []. To find the optimum physial and thermal design parameters of the system, a simulation program is developed in THRMOFLX software. To use realisti variables for the simulation of the gas turbine system, the software THRMOFLX has been used. From the available GT library, the Siemens gas turbine; with net power of MW was hosen. Table Power plant speifiations Compressor pressure ratio effiieny of the ompressor effiieny of the turbine The effetiveness of the air preheater Pressure drop in the air preheater Pressure drop in the ombustion hamber molar analysis N.8), O.8) CO.), H O.) This turbine has these speifiations that some of them have an important role for the deision variables. In this researh, GT produes MW. The mentioned plant is modeled by THRMOFLX. Table State properties and exergy base ST substane methane Figure Regenerative yle gas-turbine kg m ) s T-K P- bar tot MW) In the beginning, we must alulate exergy whole of streams that input or exit to our boundaries of ontrol volume. Thus, we need to have all information about all of the states. xergy an be divided into four distint omponents: physial, kineti, potential, and hemial exergy. xergy is a measure of the departure of the state of the system from that of the environment[]. It is therefore an attribute of the system and the environment together. In the absene of nulear, magneti, eletrial, and surfae tension effets, the total exergy of a system an be divided into four omponents[]. ) Ph KN PT CH = PH Physial xergy CH PT Potential xergy KN Chemial xergy kineti xergy In this we use the levelized ost method of Moran to find a way that helps us to optimize the thermal system[]. Levelized Cost of nergy LCO) is the onstant unit ost per KWh or MWh) of a payment stream that has the same present values the total ost of power plant and operating a power plant over its life. In a onventional eonomi analysis, a ost balane is usually formulated for the overall system subsript tot) operating at steady state[], the annual levelized ost and a ost balane have formulated for eah omponent separately. The interest rate is % that is from Bank of Industry and Mine in IRAN. Also in this, all of omponents of a power plant will work for years old. The 8 hours represents the total annual number of hours of system operation at full load about one month per year the power plant for maintenane will be off). There is a oeffiient fator that is the maintenane fator. k ) []: A detailed thermoeonomi evaluation of the design of a thermal system is based on a set of variables alulated for eah omponent of the system. Compressor ) ) preheater APH) hamber ) ) ) )

3 ISSN Online): - Issue, August, pp. - Gas turbine 8) 9) quations,,, 9 by helping below formula have inferene: = ) Auxiliary equations are: ) The levelization fator depends on, the real esalation rate, CRF apital reovery fator, effetive disount rate, FC the levelized value, and P an asset for eonomi life. The onept of levelization is defined as the use of time value-ofmoney arithmeti to onvert a series of varying quantities to a finanially equivalent onstant quantity annuity) over a speified time interval [, ]. Finally, the ost of the fuel stream to the system and air entering the air ompressor are:. Hour) The below matrixes that take the equations of to out will help to solve them in a simple manner: C=inv A) B= We solve the linear system in MATLAB. Matrix C is the answer and other exergy osting will take from auxiliary equations)[]. A detailed thermo-eonomi evaluation of the design of a thermal system is based on a set of variables alulated for eah omponent of the system. Thus, for the Component we alulate the: Many engineering design problems an be formulated as onstrained optimization problems. So far, penalty funtion methods have been the most popular methods for onstrained optimization due to their simpliity and easy implementation. However, it is often not easy to set suitable penalty fators or to design adaptive mehanism. In this, two objets exist: Inreasing effiieny of gas turbine power plant Dereasing the ost of fuel A mathematial model is a desription in terms of mathematial relations, invariably involving some idealization, of the funtions of a physial system. The mathematial model desribes the manner in whih all problem variables are related and the way in whih the independent variables affet the performane riterion. The mathematial model for an optimization problem onsists of[8, 9]: An objetive funtion to be maximized or minimized quality onstraints Inequality onstraints Generally, a onstrained optimization problem an be desribed by the exergyeonomi method has been developed for multi-period optimization of power plants and other energy systems. Algorithm is a searh tehnique used in omputing to find exat or approximate solutions to optimization and searh problems. Partile swarm optimization ) has been found to be a very effetive engine for multi-objetive optimization, and several multi-objetive partile swarm optimizers MOs) have been proposed in the last few years[].many engineering design problems an be formulated as onstrained optimization problems. So far, penalty funtion methods have been the most popular methods for onstrained optimization due to their simpliity and easy implementation. However, it is often not easy to set suitable penalty fators or to design adaptive mehanism. In this, two objets exist: Inreasing effiieny of gas turbine power plant Dereasing the ost of fuel All about relations had been alulated in these situations: All proesses are of steady-state steady-flow. The air and ombustion are treated as ideal gases. The fuel injeted into the ombustion hamber is assumed to be methane. III. AND GNTIC ALGORITHM Algorithm is a searh tehnique used in omputing to find exat or approximate solutions to optimization and searh problems. Partile swarm optimization ) has been found to be a very effetive engine for multi-objetive optimization, and several multi-objetive partile swarm optimizers MOs) have been proposed in the last few years[]. 8 IV. RSULT is an evolutionary omputation tehnique with the mehanism of individual improvement, population ooperation and ompetition, whih is based on the simulation of simplified soial models, suh as bird floking, fish shooling and the swarming theory[]. In, it starts with the random initialization of a population swarm) of individuals partiles) in the searh spae and works on the soial behavior of the partiles in the swarm. Therefore, it finds the global best solution by simply adjusting the trajetory of eah individual towards its own best loation and towards the best partile of the swarm at eah time step generation). However, the trajetory of eah individual in the searh spae is adjusted by dynamially altering the veloity of eah partile, aording to its own flying experiene and the flying experiene of the other partiles in the searh spae.

4 ISSN Online): - Issue, August, pp. - Cost per unit of exergy, ost rate assoiated with the exergy transfer of the atual running power plant in Iran and the results from algorithm optimization are ompared in Table. State 8 9 Table Comparison results ost per unit of exergy In Table state properties of yle has shown. ST Methane Table state properties-optimized results T P In summary, from this analysis it was onluded exergy analysis in Table. Table xergy analysis-optimized Results ST Methane P h S Ph MW) CH MW) tot MW) It should be noted that this differene between optimized values is just due to the optimization proedure. As evolutionary algorithm like Partile Swarm and is based on random searh this differene is reasonable. The xergoeonomi analysis and optimization of a typial gas-turbine plant were arried out using Geneti Algorithm and. All of results for ontrasting exhibit in Table. f ) GJ p GJ ) D MW) Perentage of destroying by irreversibilities C D Hours ) Z Hours ) C D +Z D f % % Table xergoeonomi analysis ompressor preheater hamber Gas turbine V. DISCUSSTION In this paper, thermodynami and exergoeonomi modeling and optimizing of a gas turbine power plant with regenerator was arried out. To ahieve this aim, the general optimization model known as Partile Swarm Optimization ) was used and the purpose deision variables were onsidered by fuzzy logi method. In the present study, thermodynami and xergoeonomi modeling of a gas turbine power plant with optimization was arried out. To ahieve this aim, the general purpose optimization methods and have used for this purpose. The obtained results show that the is more effiient and better than in this optimization Problem. Thermo-eonomis, or xergoeonomi, an be lassified into the three fields: ost alloation, ost optimization, and ost analysis. In this artile, a omprehensive thermodynami modeling and multi-objetive optimization of a gas turbine were performed and disussed. Choosing appropriate parameter settings for the evolutionary algorithm is a timeonsuming task and many times the result of trial and error. Preliminary examinations have shown that, in this appliation, the diversity in a single population quikly dereases and the algorithm onverges to a suboptimal solution. One of the most effetive parameters that play a key role in thermo-eonomi optimization is the exergy unit prie whih speifies the effet of exergy effiieny on the total ost. For every system omponent, we expet the investment osts to inrease with inreasing apaity and inreasing exergeti effiieny of the 9

5 ISSN Online): - Issue, August, pp. - omponent. In this, ombustion hamber and gas turbine must inrease in apital investments. In summary, from this analysis it was onluded that has the best result in the omparing by algorithm. Minimizing fuel mass flow, whih minimizes exergy losses and destrutions, is target of thermodynami optimization. Nomenlature C Cost rate assoiated with exergy stream Hours ) Cost of exergy destrution C D Hours ) f Cost of fuel per unit of exergy ) GJ nthalpy Kj ) Kmole h m Mass Flow rate Kg S s Kj ntropy Kmole.K ) P Pressure bar) T Temperature K) PC Purhased equipment osts f % exergoeonomi fator GT Gas Turbine isentropi effiieny Compressor isentropi effiieny SC AC CRF i GT LHV maintenane fator Compressor Capital Reovery Fator Interest rate Gas Turbine xergy MW) lower heating value Mj ) Kg p Cost of produt per unit of exergy ) GJ RFRNCS [] A. Bejan and M. J. Moran, Thermal design and optimization: Wiley. om, 99. [] M. lmasri, "GTPRO-Users Manual. Thermoflow," ed: In, 99. [] M. J. Moran, "Availability analysis: a guide to effiient energy use," 98. [] G. Tsatsaronis, "Appliation of thermoeonomis to the design and synthesis of energy plants," xergy, energy system analysis, and optimization, enylopaedia of life support systems. OLSS Publishers, UK website: www. eolss. net) pp, pp. -,. [] P. Ahmadi, I. Diner, and M. A. Rosen, "xergy, exergoeonomi and environmental analyses and evolutionary algorithm based multi-objetive optimization of ombined yle power plants," nergy, vol., pp ,. [] R. berhart and J. Kennedy, "A new optimizer using partile swarm theory," in Miro Mahine and Human Siene, 99. MHS'9., Proeedings of the Sixth International Symposium on, 99, pp. 9-. [] Q. He and L. Wang, "An effetive o-evolutionary partile swarm optimization for onstrained engineering design problems," ngineering Appliations of Artifiial Intelligene, vol., pp ,. [8] M. Gorji-Bandpy and H. Goodarzian, "xergoeonomi optimization of gas turbine power plants operating parameters using geneti algorithms: a study," Thermal Siene, vol., pp. -,. [9] G. Tosano-Pulido, C. A. C. Coello, and L. V. Santana-Quintero, "MO: a multi-objetive partile swarm optimizer with emphasis on effiieny," in volutionary Multi-Criterion Optimization,, pp. -8. [] F.-z. Huang, L. Wang, and Q. He, "An effetive oevolutionary differential evolution for onstrained optimization," Applied Mathematis and omputation, vol. 8, pp. -,. [] G. Tsatsaronis, "xergoeonomis: Is it only a new name?," Chemial engineering & tehnology, vol. 9, pp. -9, 99. [] A. Valero, M. A. Lozano, L. Serra, G. Tsatsaronis, J. Pisa, C. Frangopoulos, et al., "CM problem: definition and onventional solution," nergy, vol. 9, pp. 9-8, 99. [] L. Davis, "Handbook of geneti algorithms," 99. [] M. Karlsson, "The MIND method: a deision support for optimization of industrial energy systems priniples and studies," Applied nergy, vol. 88, pp. -89,. [] C. Koh, F. Cziesla, and G. Tsatsaronis, "Optimization of ombined yle power plants using evolutionary algorithms," Chemial ngineering and Proessing: Proess Intensifiation, vol., pp. -9,. [] Toffolo, A. and A. Lazzaretto ). "volutionary algorithms for multi-objetive energeti and eonomi optimization in thermal system design." nergy ): 9-. [] Tsatsaronis, G. ). "Definitions and nomenlature in exergy analysis and exergoeonomis." nergy ): 9-. [8] G. Tsatsaronis, "xergoeonomis: Is it only a new name?," Chemial engineering & tehnology, vol. 9, pp. -9, 99. [9] Ameri, M., et al. 9). "nergy, exergy and exergoeonomi analysis of a steam power plant: A study." International Journal of nergy Researh ): 99-. [] He, Q. and L. Wang ). "An effetive oevolutionary partile swarm optimization for

6 ISSN Online): - Issue, August, pp. - onstrained engineering design problems." ngineering Appliations of Artifiial Intelligene ):