OPTIMIZATION OF AN ORGANIC RANKINE CYCLE IN ENERGY RECOVERY FROM EXHAUST GASES OF A DIESEL ENGINE

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1 INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN (Print) ISSN (Online) Volume 5, Issue 12, December (2014), pp IAEME: Journal Impact Factor (2014): (Calculated by GISI) IJMET I A E M E OPTIMIZATION OF AN ORGANIC RANKINE CYCLE IN ENERGY RECOVERY FROM EXHAUST GASES OF A DIESEL ENGINE Zunaid Ahmed Department of Mechanical Engineering, Royal School of Engineering & Technology, Gauhati University, Guwahati, India, Dimbendra K. Mahanta Department of Mechanical Engineering, Assam Engineering College, Gauhati University, Guwahati, India ABSTRACT This paper describes thermal analysis and optimization of an organic Rankine cycle (ORC) integrated with a power generating stationary diesel engine. A simple ORC, with a regenerator, is considered here as a bottoming cycle for producing additional power by recovering waste energy from the exhaust gases of the engine. Taking evaporation pressure andcondensation temperatureastwodecision variables, a genetic algorithm is used for simultaneously maximizing three objective functions - exergy efficiency, thermal efficiency, and specific network.the optimization of the ORC is performed for three different working fluids (n-hexane, isopentane and isobutane)with their dry expansion after taking saturated vapour at the inlet of the turbine.on analysing and comparing the performance of the optimized ORC systemunder the same waste energy condition, several notable aspects are observed among the considered decision variables and objective functions.the results shows that a considerable amount of waste energy can be recovered by the ORC. Keywords: Waste Energy Recovery, Organic Rankine Cycle, Exergy Analysis, Optimization, Genetic Algorithm 97

2 NOMENCLATURE exergy rate ( / ) h specific enthalpy ( / ) rate of exergy loss (kj/s) mass flow rate ( / ) pressure ( ) specific entropy ( /( )) temperature ( ) specific volume ( / ) energy rate (kg/s) power ( / ) specific work output ( / ) % percentage isentropic enthalpy difference (kw) weighted sum Mol.wt molecular weight (g/mol) Greek symbols efficiency effectiveness Σ total difference Subscripts exhaust gases fluid w water each state point inlet out outlet minimum s isentropic o dead state crit critical, specific net exergy h thermal evaporator/evaporation turbine regenerator condenser/condensation pump 1,2,2,3,4,4,, state points 98

3 1. INTRODUCTION Diesel engine (DE), that converts energy from heat to work, has vast applications in road vehicles, marine transport, and power plants. Diesel engines are preferred for their reliability, low specific cost and high electrical efficiency, especially in the power range of hundreds of kw to few MW [1-3]. In a diesel engine, all the energy released during combustion of the fuel cannot be converted into useful work because of some thermodynamic limitations. The balance is released through exhaust gases and cooling systems as low grade energy (heat), and thus it is simply wasted. An organic Rankine cycle (ORC) can be used to produce useful work by exploiting such low grade energy sources. In ORCs, organic fluids are preferred to water when the required power is limited and the energy source temperature is low. This is because of the fact that organic fluids often have lower heat of vaporization and they can better follow the heat source to be cooled, and thus temperature differences and irreversibility at the evaporator are reduced [4-6]. Many works have focused on performance optimization of ORCs. Donghonget al. [7] optimized an ORC, driven by exhaust heat of a gas turbine, using Modelica/Dymola software package as a modeling tool. Chacarteguiet al. [8] studied an ORC as the bottoming cycle for large and medium recuperated gas turbine. Yipinget al. [9] applied a genetic algorithm (GA) for parametric optimization of an ORC for low grade waste energy recovery. Vajaet al. [10] studied the performance of an ORC as bottoming system. Rashidiet al. [11] investigated another GA for optimization of a trans-critical power cycle with regenerator.amir [12] also optimized an ORC using a GA.Shuet al. [13], performed parametric optimization of a combined system of diesel engine with bottoming ORC on alkane-based working fluids. The main objective of this study is to analyze and compare three different dry working fluids for better ORC performance in waste energy recovery from the exhaust gases of a stationary diesel engine. The dry working fluids considered arehydrocarbons (HCs). The thermodynamic characteristics of these HCs are shown in Table 1. These hydrocarbons are attractive since some of them have near-ambient boiling points to enable condensation near atmospheric pressure. For the high-temperature ORC, HCs are selected as working fluids because of their appropriate critical temperature and pressure. In addition, HCs are also environment friendly working fluids with a zero ozone depletion potential (ODP) and relatively low global warming potential (GWP) values [16]. Name Table 1: Thermodynamic characteristic of fluids Type Mol.wt. ODP (kpa) (g/mol) GWP 100 yrs. n-hexane dry isopentane dry isobutane dry The fluids are taken here in the form of saturated vapour at the inlet of the turbine and then their dry expansion is performed. A GA is used to optimize the performance of the ORC system for each working fluid under the same waste heat condition. The optimization is performed for simultaneously maximizing three objective functions, which are exergy efficiency, thermal efficiency and specific network output.multi-objective optimization is performed using the weighted sum method.the performances of each of the working fluids are compared. 99

4 2. SYSTEM MODELING 2.1 Description of the ORC system The components of a simple ORC system are evaporator, turbine, condenser and pump. A regenerator may also be used if the temperature of the working fluid leaving the turbine is markedly higher than that leaving the pump. The fluid streams allowed in the ORC systems include exhaust gases, working fluid and cooling water. Fig.1: ORC-diesel engine system with a regenerator Fig.2: T-s diagram of a simple ORC with a regenerator The schematic of a simple ORC system with a regenerator is shown in Fig.1. The cycle is essentially a rankine cycle. The exhaust gases enter the evaporator at state point and exit at. The working fluid in liquid phase enters the evaporator at state point and exits in the gaseous phase at state point 3while absorbing energy from the exhaust gases. The working fluid in the gaseous 100

5 phase then enters the turbine atstate point 3 and exitsat point 4 while producing work. Ideal expansion of working fluid in the turbine is defined by the process 3-4. In the regenerator the energy exchange takes place between working fluids, i.e. hot gaseous stream (state points4- ) and cold liquid stream (state points2- ). The working fluid atstate point is condensed in the condenser tostate point1 while rejecting energy. The cooling water enters and exits condenser at state points and. The working fluid in liquid phase is then pumped to a higher pressure level by the pump from state point 1 to 2 while absorbing work. Ideal process in the pump is defined by the process 1-2.The working fluid then enters the regenerator. The cyclic processes are shown in the T- s diagram in Fig.2. The ORC operates on two pressure levels i.e. evaporation pressure (2- -3) and condensation pressure (4- -1). In the present study, the sub-critical ORCs with dry expansion are investigated. These fluids are considered to ensure their dry expansion. As depicted in Fig.2, the slope of the saturated vapour curve of a dry fluid is non-negative, which allows dry saturated vapour at the inlet of the turbine and ensures its dry expansion inside the turbine. The considered energy source is the exhaust gases released from a stationarydiesel engine. For performing the simulation of the ORC, it is assumed that the system reaches a steady state, as well as pressure drop in the pipes and heat loss to the environment from the evaporator, condenser, turbine, pump and regenerator are negligible. The specified dead reference state is considered with and as the ambient pressure and temperature, respectively. Because of the thermodynamic irreversibility occurring in each of the components, such as non-isentropic expansion, and compression as well as heat transfer over a finite temperature difference, the exergy analysis is also employed to evaluate the performance of the ORC system in low grade waste energy recovery Thermodynamic model With reference to Fig. 2, a mathematical model of the ORC system is presented below on the basis of the first and second laws of thermodynamics Energy analysis (i) Processes - and -4: For the evaporator, the energy absorbed from the exhaust gasesis: = h, h, = (h h ) (1) (ii) Process 3-4: For the turbine, the energy converted to work output and isentropic turbine efficiency are: = (h h ) (2) = h h h h (3) (iii) Processes 5- and 2- : For the regenerator, the energy exchangeis: (h h )=(h h ) (4) 101

6 (iv) Process -1: For the condenser, the energy rejected to cooling wateris: = (h h )= h, h, (5) (v) Process 1-2: For the pump, the work input and isentropic efficiency of pump are: = (h h ) (6) (vi) The net work output of the system is: = h h h h (7) = (8) (vii) The specific net work (net work output per kg of working fluid) of the system:, = (9) (viii) Thermal efficiency of the cycle can be calculated as: Exergy analysis (i) The exergy of the state point : = (10) = [(h h ) ( )] (11) The o subscripts are used to denote the specified dead reference state under ambient pressure and temperature conditions. In the present work, the dead state is specified by To = 298 K = 100. (ii) The exergy entering into ORC (from exhaust gases) in the evaporator is: =,, (12) (iii) The exergy leaving the ORC (to cooling water) at condenser is: =,, (13) (iv) The exergy balance in k-the component of an open thermodynamic system is:,, = (14) 102

7 (v) The exergy loss in the evaporator is: =, +, + (15) (vi) (vii) (viii) (ix) The exergy loss in the turbine: = The exergy loss in the condenser is: The exergy loss in the pump is: = +,, = + The exergy loss in the regenerator is: (16) (17) (18) = + (19) (x) (xi) The total exergy loss due irreversibility: = The exergy balance of the ORC system is: (20) + = + + (21) The second law efficiency or the exergy efficiency expresses the capability to produce work and it indicates how well the processes in the system perform relative to ideal (and reversible) processes. It reflects the ability to convert energy into usable work [10]. Therefore, to evaluate the performance of the cycle, exergy efficiency is considered. (xii) The overall exergy efficiency of the ORC system: = + =1 + + (22) 3. OPTIMIZATION METHODOLOGY 3.1. Optimization of the ORC system The optimization of the ORC system is performed for three different working fluids of dry type. The ORC has many parameters (variables), which may affect its performance. In the present study, the effects of the evaporation pressure ( ) andcondensation temperature ( ). The exergy efficiency, thermal efficiency and specific net-work, which can evaluate the ORC performance, are selected as the distinct objective functions for optimization of the ORC system. 103

8 The genetic algorithm (GA), first presented by Holland [14], is a robust optimization algorithm, which is designed to reliably locate a global optimum even in the presence of local optima. Further, GAs provides a great flexibility to hybridize with domain-dependent heuristics to make an efficient implementation for a specific problem [12]. The GA starts by initializing a set of individuals that form a population. Then, the population is evolved over generations (iterations) through the repeated application of some GA operators, namely selection, crossover and mutation operators. In each generation, the GA evaluates the individuals according to some objective functions and then selects the above-average individuals through a selection operator. The selected above-average individuals have a higher possibility for participating in the crossover operation for recombining genetic exchange among individuals. Mutation, which periodically changes parts of the individuals, is the main operator for protecting the algorithm from permanently losing genetic materials in the evolution process. Another operator used in GA is migration, which allows movement of individuals among sub-populations of existing individuals, with the best individuals from one sub-population replacing the worst individuals in another sub-population [11] Multi-objective optimization using weighted sum method The weighted sum strategy converts a multi-objective problem of maximizing the vector of criteria functions, into a scalar problem by constructing a weighted sum F(x) of all the objectives. Maximize ( )= ( ) (23) where ( ) is the -th normalized objective function and is the weight of the -th objective function. Initially, each objective function is independently maximized. The maximum value is used to normalize the corresponding objective function. Then, the value of has a range between zero and one. The weights are set such that they are significant relative to each other and relative to the objective values. The weights are chosen such that their sum equals unity ( =1). F(x) can then be optimized using a standard optimization algorithm. This weighted sum method suggests that the solution can be found for a convex (minimization) or concave (maximization) multi-objective optimization problem [15, 17]. 4. NUMERICAL EXPERIMENTS 4.1 Simulation and optimization The simulation of the thermodynamic model for the ORC system, incorporating equations (1) to (22) and input parameters (Table 2 and Table 3), is done first in the EES software [19]. The thermodynamic properties of the selected three working fluids are calculated using the fundamental equation of state developed by Wagner and Pruss [18] as incorporated in the EES software. The thermodynamic properties of the exhaust gases are calculated using equations of state in RefProp [20]. The ORC system is then optimized through the GA module of the software to separately maximize each of the objective functions, namely, exergy efficiency ( ), thermal efficiency ( ) and specific net work output (, ) within the specified bounds of operating parameters evaporation pressure ( ) and condensation temperature ( ) mentioned in section 4.3. In order to evaluate the average performance of the GA, 30 independent runs are performed, for each working fluid against each objective function, with different sets of GA parameter values. In all the runs, the initial solutions are generated randomly satisfying the variable bounds, and the crossover and mutation probabilities are also taken randomly within the ranges of [0.80,0.90] and 104

9 [0.01,0.05], respectively. For the purpose of comparison, however, the population size and the maximum number of generations to be performed are fixed as 40 and 100, respectively, in all runs. In the case of multi-objective optimizing of three objective functions, a scalar function is constructed with weighted sum of the three normalized objectives (Equation 23). Since all the three objectives are considered to be equally important, equal weight is assigned to each, i.e. = =. This function is then maximized using GA within the same bounds of independent variable to achieve a solution which is a trade-off between maxima of the objective functions. 4.2Initial conditions In the present study, power output, parameters of exhaust gases and exhaust compositions of commercial V16 turbocharged diesel engine is employed in the simulations.the important parameters of the diesel engine, under full load conditions, are listed in Table 2. Table 2 Important parameters of the diesel engine Parameter Value Power Output 1000 kw Exhaust gas temperature 868 K Exhaust gas mass flow kg/s The mass-based typical compositions of the considered exhaust gases are as follows: N 2 = 73.04%, H 2 O = 5.37%, O 2 = 6.49%, CO 2 = 15.10% [13]. The exhaust gases enter the evaporator at temperature,. In order to avoid any corrosive effect, the minimum temperature, at the exit of the evaporator is constrained by the dew point temperature of the exhaust gases, which is 403K [11]. The detail input parameters of the ORC system are shown is Table 3 where PPTD is the pinch point temperature difference. 4.3 Boundary conditions The decision variables which are identified as independent variables are evaporation pressure ( ), and condensation temperature ( ). These are also referred to as operating parameters. These three variables have effect on the exergy efficiency ( ), thermal efficiency ( ) and specific net work output (, ) [12]. The evaporation pressure ( ) and the corresponding inlet temperature ( ) of the turbine are the maximum pressure and temperature of the ORC. The minimum pressure of the ORC is the condensation pressure ( ) corresponding to the condensation temperature ( ). The variable bounds of the independent variable (operating parameters) are: 500 _ critical pressure and Table 3: Input parameters of the ORC system Parameter Value Inlet temperature of exhaust gases (, ) 868 K Outlet temperature of exhaust gas (, ) 403 K Inlet temperature of cooling water (, ) 298 K PPTD Evaporator (, ) 10 K PPTD Condenser (, ) 5 K PPTD Regenerator ( ) 10 K Isentropic efficiency of pump ( ) 0.65 Isentropic efficiency of turbine ( )

10 5. RESULTS AND DISCUSSION 5.1. Optimum operating parameters For each working fluid, several runs of the GA are performed to maximize each of objective functions separately. The best, worst, mean and standard deviations of the objective values and independent variables (operating parameters) are obtained. The mean values are presented in Table 4 for maximizing exergy efficiency ( ); Table 5 for maximizing thermal efficiency ( ); and Table 6 for maximizing specific net work (, ). It observed that the maximum, maximum and maximum. for all the fluids do not occur at the same evaporation pressure. This indicates that all the three objectives cannot be maximized simultaneously for the ORC system operating at the same values of the operating parameters. The values of and are the highest when maximizing and the lowest when maximizing. It is also observed that for all the fluids is the almost the same when maximizing each of the objective functions. The ORC system is expected to have maximum of any of the three objectives within the ranges of operating parameters indicated in Table 7. Table 4: Parameters of ORC for maximizing exergy efficiency Working Fluid (kpa) 106 Maximum (%) n-hexane isopentane isobutane Table 5: Parameters of ORC for maximizing thermal efficiency Working Fluid (kpa) Maximum (%) n-hexane isopentane isobutane The need of multi-objective optimization is to ascertain particular values of the operating parameters that will give a adequate trade-off among the maxima of the objective functions. For each working fluid, several runs of the GA are performed to maximize (weighted sum). The best, worst, mean and standard deviations of independent variables (operating parameters) are obtained.for all the fluids the standard deviations of is less than and standard deviations of is less than Since the standard deviations are small the mean values are considered to be the optimum values of operating parameters. The simulation of the ORC system is run with these mean values of operating parameters and the corresponding trade-off values of objective functions are presented in Table 8. Table 6: Parameters of ORC for maximizing specific net work Working Fluid (kpa) Maximum, (kj/kg) n-hexane isopentane isobutane

11 These operating parameters that give the trade-off between the maxima lie within the ranges as indicated in Table7.From the analysis it is observed that the trade-off is most for exergy efficiency and least for specific net work, implying that for the optimum operating parameters, the ORC system will run to produce specific net work close to the its maximum. The trade-off from the maxima for each fluid when ORC is running at optimum parameters is shown in Table 9.It is also seen that the trade-off is most for working fluid isobutaneand minimum forn-hexane.on comparing the working fluids for the ORC system (running under the same given energy source conditions) n-hexane gives the largest values of objective functions with least trade-off and its performance is the highest. The ORC system running within the ranges of operating parameters (Table 7) will have good performance, but best performance can be achieved when ORC system is running at optimum values of operating parameters (Table 8). Working Fluid Table 7: Ranges of operating parameters Working Fluid Range of (kpa) Range of n-hexane isopentane isobutane Table 8: Optimum parameters of ORC Optimum operating parameters Corresponding objective functions, (%) (%) (kj/kg) (kpa) n-hexane isopentane isobutane Working Fluid Table 9: rade-off between maxima, (kj/kg) (%) (%) Max. Opti. Diff. Max. Opti. Diff. Max. Opti. Diff. n-hexane isopentane isobutane The comparative summary of the ORC systems operating at optimum parameters with the three different working fluids is shown in Table 10. The DE under consideration produces 1000 kw of power. For optimal conditions of ORC system, additional kw net power can be recovered using n-hexane as the working fluid with highest thermal and exergy efficiencies (26.215% & %) but at the lowest condenser pressure of kPa and highest turbine inlet temperature of K.It should be noted that for n-hexane the condenser requires low vacuum pressure. A condenser pressure near the atmospheric pressure would be suitable from the view point of condenser design. In this aspect, isopentane has condenser pressure closer to the atmospheric pressure. The mass flow rate of each fluid stream of the ORC system is least for n-hexane. 107

12 Table 10: Comparative summary of ORC system at optimum operating parameters Working Fluid n-hexane isopentane isobutane (kpa) (kpa) (kg/s) (kg/s) (kg/s) (kw) (kw) (kw) (kw) (kw) Σ (kw) (%) (%) (kj/k) CONCLUSION It is observed that for each fluid, the maxima of exergy efficiency, thermal efficiency and specific net work outputoccurs at different values of operating parameters.the optimum values of operating parameters are obtained for the ORC system using each of the working fluid for achieving adequate trade-off between the objectives. The optimum operating parameters are closer to the parameters for obtaining maximum work output (maximum energy recovery). The ORC system using n-hexanerecover more amountof energy ( =149 kw) with higher thermal efficiencies( =26.215% & = %) as compared to the others.the fluids n- hexane and isopentane are found to be suitable for ORC system with regenerator in recovering waste energy from exhaust gases of the diesel engine, from the view point of thermal aspects. However, economic aspects need to be also taken into consideration. Other such drawbacks as higher flammability of these fluids and their toxicity also have to be taken into account. This analysis and optimization of ORC system is done only from the view point of thermal considerations. There is much scope in performing economic and environmental analysis also. The use of other effective multi-objective method to optimize thermal, economic and environmental objectives is quite necessary for such systems. REFERENCES 1 Invernizzi C, Iora P, Silva P. Bottoming micro-rankine cycles for micro-gas turbines (2007), ApplTherEng ;27: Danov SN, Gupta AK. Modeling the performance characteristics of diesel engine based combined-cycle power plants part I: mathematical model (2004), J Eng Gas Turbines Power; 126: Danov SN, Gupta AK. Modeling the performance characteristics of diesel engine based combined-cycle power plants part II: results and applications (2004), J Eng Gas Turbines Power; 126:

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