Multi stage ash desalination
ithn pl, simd aingobjility
optimization results to improve the products' cost values. The optimization results show that the cost ofl cost impact are reduced by 13.4% and 53.4%, respectively, whereas a 14.8% in-
f our litions [1teractioust, th
Desalination 285 (2012) 123130
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j ourna l homepage: www.e lplay a non-negligible role. A thermoeconomic analysis takes into accountboth fuel and capital costs, and allows determining the product's cost onthe basis of exergy criteria. This requires the determination of a functionalquantitative interdependence between equipment, operations costs andefciency [2,3].
Large dual-purpose plants are built to reduce the cost of electricityproduction and freshwater. The dual purpose power desalination plantsmake use of thermal energy extracted or exhausted from power plantsin the form of low-pressure steam to provide heat input to thermal de-
main objective of a designer is to dene the optimal plant congura-tion and operative conditions according to specied environmentalconstrains and to the user's requests. Therefore, an integrated designoptimization approach would be preferred to be able to deal with allthese aspects in real and complex energy systems. In order to incor-porate the emission assessment, Environomic is proposed to denotethe combination of Thermodynamic, Economic and Emissions. Manystudies have performed environomic consideration of energy systems[1,913].salinations, like multi-stage ash (MSF) or(MED) systems.
Corresponding author. Tel.: +98 21 84063222; fax:E-mail address: firstname.lastname@example.org (M. Amidpour)
0011-9164/$ see front matter 2011 Elsevier B.V. Alldoi:10.1016/j.desal.2011.09.043ic and environmental as-uration is not always thel, labor, and energy costs
equipment and input energy resources, operation and maintenancecosts), and the effects of undesired uxes to the ambient must beevaluated in order to answer environmental concerns. In fact, thepects of the system. The most efcient congoptimal one in terms of cost, since the capita1. Introduction
Energy is the most important part ofound everywhere in a variety of applicaa large number and various types of intheir physical boundaries. The designermwhichdeal primarilywith the energetic,the sensitivity analysis shows the relationship between the fuel cost, pollution damage cost and the objectivefunctions.
2011 Elsevier B.V. All rights reserved.
fe. The usage of energy is]. Energy systems involvens with systems outsideerefore, facemany issues,
Numerous researchers, e.g. [1,48] have conducted exergy andthermoeconomic analyses and optimization for thermal systems.Using the optimization procedure with respect to thermodynamiclaws as well as thermoeconomics then becomes essential . Ther-modynamic laws govern energy conversion processes, costs are in-volved in obtaining the nal products (expenses for the purchase ofmulti-effect distillation To increasesystems, it is imability on the restion of hybridcustomers. Thesystem have alwcan be dealt witSuch links can
+98 21 88674748..
rights reserved.water productions have not changed much. Additionally,
ThermoenvironomicExergy efciencycrease happens in total exergy efciency. Therefore, improvement in all objectives has been achieved usingthe optimization process, although the power andMulti-objective optimization products and environmentaCost optimization of a combined power aenvironment and reliability consideration
Seyed Reza Hosseini, Majid Amidpour , Seyed EhsanFaculty of Mechanical EngineeringEnergy Division, K.N. Toosi University of Technology, P.O
a b s t r a c ta r t i c l e i n f o
Article history:Received 26 July 2011Received in revised form 23 September 2011Accepted 29 September 2011Available online 24 October 2011
The present study deals wmulti stage ash desalinatiopects have been consideredalgorithm (MOGA) is applietion is obtained by integratoptimization approach, thisMoreover, equipment reliabwater desalination plant with exergetic,
hakib: 19395-1999, No. 15-19, Pardis Str., Mollasadra Ave., Vanak Sq., Tehran 1999 143344, Iran
the multi-objective optimization for designing a combined gas turbine andant. In optimization approach, the exergetic, economic and environmental as-ultaneously. In order to achieve the optimal design, Multi-objective genetics a suitable optimization technique. The thermoenvironomic objective func-the environmental impacts and thermoeconomic objective. By applying theective function is minimized, whereas system exergy efciency is maximized.using the state-space and the continuous Markov method is incorporated in
sev ie r .com/ locate /desa lcompetitiveness and market value of cogenerationportant to analyze the inuence of equipment reli-ulting cost of power and water. So reliability evalua-system is very important to both utilities andreliability and economics of a cogeneration supplyays been conicting parameters. These parametersh by establishing quantitative links between them.best be established by using probabilistic criteria
consideration are compared and then the sensitivity of fuel cost andenvironmental damage cost on Pareto frontier of optimal solutionare presented.
2. Cogeneration cycle
Fig. 1 illustrates the schematic of the combined GT-MSF system for si-multaneous generation of the electric power and fresh water. Powergeneration cycle includes compressor, combustion chamber and gas tur-bine that have a nominal output power of 65 MW. Also, a heat recoverysteam boiler was used to produce saturated steam of distillation unit. Allparts of systems were modeled and simulated and energy and exergyequations were developed and applied to evaluate performance of com-
124 S.R. Hosseini et al. / Desalination 285 (2012) 123130Nomenclature
BR Brine circulatingc Unit cost of the exergy rateCC Combustion ChamberCO Carbon monoxideCom CompressorD Distillate Exergy ratee Specic exergyGT Gas turbineHb Brine pool heightHJ Heat Rejectionwhich consider the stochastic nature of component outages, customerdemands, etc. . Many studies have performed reliability modelingof systems .
Our previous paper considered the effect of reliability analysis onthe cost of power and water, which is obtained by thermoeconomicanalysis . This paper exhibits the multi objective optimization ofa combined gas turbine and multi stage ash-brine circulating desali-nation plant. The optimization algorithm is applied for minimizingthe total product cost and maximizing overall exergy efciency ofthe dual-purpose plant. Note that the environmental equations ofpollutant gases are included in the cost of products. In addition,according to our previous paper, the equipment reliability consider-ation is incorporated in the optimization results. Finally, the resultsof base case and optimization design with and without reliability
bined system. Technical characteristics of the proposed plant are shownin Tables 1 and 2. The exergetic, thermoeconomic and reliability analyseswere fully described in our previous paper . Following is a summaryof the thermoeconomic and reliability analysis of the hybrid plant.
3. Summary of thermoeconomic and reliability analysis
The cost balance equation of a component of an energy system iswritten as follows:
k;in ZCIk ZOMk
where cj is the unit cost of exergy ($/kJ) for the jth stream to/from thecomponent, j is the exergy ow for the jth stream to/from the com-ponent (kW) and ZCIk k and Z
OMk ($/s) are the related cost of capital in-
HR Heat RecoveryHRSG Heat recovery steam generatorMED Multi Effect EvaporationMOGA Multi objective genetic algorithmMSF Multi Stage FlashN Number of desalination stagesOMC Operating and Maintenance CostP Probability, Pressureppm Parts per millionsPR Performance ratio (the ratio between the mass of the
produced fresh water to that of the consumed steam)TBT Top Brine TemperatureTRR Total Revenue RequirementTur TurbineTN Temperature of rejected brineTpz Adiabatic temperature in the primary zone of combus-
tion chamber (K)Vv Vapor allowable velocityWnet Net power
Greek Letters Residence time in the combustion zone Exergetic efciency Equivalent fuelair coefcientiT Gas turbine isentropic efciencyiC Compressor isentropic efciency
Subscripts0 Environmental stateCC Combustion Chamberenv EnvironmentF FuelP Producttot Totalvestment and operating and maintenance for the kth componentobtained using the economic model. The economic model is basedon the Total Revenue Requirement (TRR) method (which is basedon procedures adopted by the Electric Power Research Institute) .
An important method for reliability evaluation in continuous anddiscrete systems is Markov approach modeling. Consider the threecomponents as representing the gas turbine, the heat recovery
Fig. 1. Combined gas turbine cycle and desalination. (1,2: Air; 3, 7, 14: Power; 4, 6, 8:combustion products; 5: methane; 9: water; 10: steam; 11, 15: sea water; 12: distil-
late; 13: brine).
steam generator and multi stage ash desalination which are compo-nents in series. To demonstrate the continuous Markov concepts, astate space diagram was applied to represent system state changes.A state is dened as a particular combination of component operationand failure. Satisfactory operation of combine system is dened asgenerating electricity and water. The failure rate and repair rate as-sumptions of the GT, HRSG, and MSF are shown in Table 3.
The product costs with reliability consideration can be obtainedusing the state probabilities as weights for every possible operatingstate [14,15]:
Ce iPei Cei 2
Table 1Specications of the gas turbine power plant system.
Ambient air temp. 25 CRelative air humidity 60%Compression ratio 11Isentropic efciency of compressor 86%Isentropic efciency of turbine 87%Inlet turbine temp. 1100 CHeat loss in combustion chamber 2%Pressure loss in combustion chamber 5%Inlet HRSG water temp. 25 COutlet HRSG ue gas temp. 160 CNet power output 65 MWThermal efciency of power cycle 29.1%
125S.R. Hosseini et al. / Desalination 285 (2012) 123130Cw iPwiCwi : 3
Pei is the probability of the state in which the electricity is pro-duced andCei is the cost of electricity production in that state. This ex-pression is used for water production either. As was shown in ,the effect of the inclusion of equipment reliability is to increase thewater cost due to unexpected equipment downtime resulting fromfailure and subsequent equipment repair.
Table 2Specications of the MSF desalination system.
Capacity 42,165 m3/dayNumber of effects 32Temperature of the inlet seawater 25 CTemperature of the rejected brine 40 CTop brine temperature 106 CSalt composition of the inlet seawater 42,000 ppmSalt composition of the outlet brine 70,000 ppmOutside/inside diameters of the HR condenser tubes 34.9/31.6 mmOutside/inside diameters of the HJ condenser tubes 28.5/25.3 mmNumber of tubes in HR section 2403Number of tubes in HJ section 1653
Brine velocity in the HR condenser tubes 2.37 m/sBrine velocity in the HJ condenser tubes 2.14 m/sPressure loss in the HR condenser tubes 1146 kPaPressure loss in the HJ condenser tubes 129 kPaTemperature of the inlet steam 143.6 CTotal steam consumption 50.4 kg/sTotal feed seawater 1220 kg/sTotal cooling seawater 589 kg/sTotal brine outlet 732 kg/sDesalination length 132.2 mDesalination width 18 mDesalination height 5 mPerformance ratio 9.7Specic area 333 m2/(kg/s)Total head losses outlet the MSFa 127 mPumping power consumption 4.5 kW h/m3
a It is the sum of the following head losses: sea water supply to MSF, saline and cool-ing water rejected to sea, and distillate water transfer to storage tank.4. Environmental consideration
The combustion in a gas turbine is an incomplete process. The ex-haust products mainly are carbon dioxide (CO2), water vapor (H2O),excess atmospheric oxygen (O2) and nitrogen (N2). Carbon dioxideand water vapor have not always been regarded as pollutants becausethey are the natural consequence of complete combustion of a hydro-carbon fuel. However, they both contribute to global warming andcan only be reduced by burning less fuel .
For a gas turbine engine burning a lean mixture of natural gas andair, the emissions of unburned hydrocarbons (UHC) and sulfur (SOx)are negligibly small and therefore most regulations for stationary gasturbines have been directed at oxides of nitrogen and carbon monox-ides. CO and NOx emissions are the pollutant emissions, and have aharmful effect on human health, as well as the environment .
A simple model, based on semi-analytical correlations , is addedhere to the thermoeconomic model to determine pollutant emissions,which are essential for the setup of an environmental objective function.The adiabatic ame temperature in the primary zone of the combustionchamber is derived from the expression by Glder :
Tpz Aexp 2
where is a dimensionless pressure p/pref (p being the combustion pres-sure p2, and pref=101,325 Pa); is a dimensionless temperature T/Tref(T being the inlet temperature T2, and Tref=300 K); is the H/C atomicratio (=4, the fuel being pure methane); = for b1 ( being thefuel to air equivalence ratio) and =0.7 for N1. is equivalentfuel to air ratio that is considered equal to 0.64 in this work. Parametersdenoted as x, y, z,, , and can be found in Appendix A.
The adiabatic ame temperature is used in the semi-analyticalcorrelations proposed by Rizk and Mongia  to determine the pol-lutant emissions in grams per kilogram of fuel:
NOx 0:15E160:5e 71100=Tpz
p0:053 p3=p3 0:55
p23 p3=p3 0:56
where is the residence time in the combustion zone ( is assumedconstant and is equal to 0.002 s); Tpz is the primary zone combustiontemperature; p2 is the combustor inlet pressure; p2=p2 is the non-dimensional pressure drop in the combustor (p2=p2=0.05). Notethat the primary zone temperature is used in the NOx correlation in-stead of the stoichiometric temperature, since the maximum attain-able temperature in premixed ames is Tpz, as pointed out byLefebvre .
Table 3Reliability assumptions of the hybrid plant.
Component Failure per day Repair per day
GT 0.0033 0.03HRSG 0.002 0.19MSF 0.002 0.085. Optimization approach
In order to achieve the optimal parameters, an optimization algo-rithm tool can be used. Although gradient descent methods are themost elegant and precise numerical methods to solve optimizationproblems, however, they have the possibility of being trapped at localoptimum depending on the initial guess of solution. In order to achievea good result, these methods require very good initial guesses for
be introduced as relativeweights of each pollutantmeasure. Theweight-ing criterion may also derive from economic considerations, when theunit damage cost of each pollutant is available. In particular, links maynot exist between the en...