Dynamic Response Performance Comparison of ... - Science Direct

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ScienceDirect Energy Procedia 105 (2017) 1600 – 1605

The 8th International Conference on Applied Energy – ICAE2016

Dynamic response performance comparison of Ranking Cycles with different working fluids for waste heat recovery of internal combustion engines Xuan Wanga, Gequn Shua*, Hua Tiana, Peng Liua, Xiaoya Lia, Dongzhan Jinga a

State Key Laboratory of Enignes, Tianjin University,Tianjin, China ;

Abstract

ORC (Organic Ranking Cycle) including RC (Ranking Cycle) is regarded as a suitable way of waste heat recovery for internal combustion engines. The working condition of the engine often changes, so the ORC or RC waste heat recovery system also works at different working conditions frequently and thus it is very meaningful to research the system dynamic response performance and control strategy. There are lots of organic working fluids and the systems with different working fluids have different dynamic response performance. In this study, the dynamic math model of four ORCs with different working fluids and RC as waste heat recovery system of a nature gas engine are established with Simulink. Based on this, their dynamic response performance is compared and analyzed, finding that the faster the system responds, the greater the mass flow rate of working fluid is, which provides useful reference for the control design of ORC with different working fluids. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

© 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection peer-review of under responsibility of ICAE Peer-reviewand/or under responsibility the scientific committee of the 8th International Conference on Applied Energy. Kewords: Internal combustion engine; ORC; RC; working fluids; dynamic performance

1. Introduction ORC (Organic Ranking Cycle) including RC (Ranking Cycle) is regarded as suitable method to recover the waste heat of ICEs (internal combustion engines). Under the stable work condition of ICE, the ORC as WHRS (waste heat recovery system) has been studied from different aspects by a lot of researchers. ORC system performance analysis [1]focus on usable percentage of waste heat, output power, recovery efficiency and exergy efficiency et al; Working fluids researches[2] mainly focus on diverse screening and assessment criteria to select the most suitable working fluid for ORC WHRS; Novel * Corresponding author. Tel.: + 86 022 27409558; E-mail address: [email protected].

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.512

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Xuan Wang et al. / Energy Procedia 105 (2017) 1600 – 1605

System designs [3] try to make full use of ICE waste heat, such as dual loop ORC; other researches focus on the optimizations [4] on parameters of turbine inlet pressure, evaporating temperature, pinch point temperature, heat transfer area et al. However, the working condition of ICE often changes. Exhaust is the most important waste heat source and on different working conditions of ICE, exhaust gas temperature of light-duty engine varies from 500 to 900ć and that of heavy-duty engines is in the range of 400 to 650ć [5]. Therefore, the ORC WHRS also often works at different work conditions and thus it is very meaningful to research the ORC system dynamic response performance and control strategy. Some researchers have built the dynamic model of ORC system to do those researches. Moslem et al. [6] presented a dynamic numerical model to capture dynamic response of ORC when the system experiences a change in expander's rotational speed, pump's capacity factor, and conditions of hot and cold heat transfer fluids. Tilmann et al. [7] also built an ORC dynamic model as WHRS for an internal combustion engine to analyze the system dynamic response. Benato et al. [8] used PI controller in their dynamic ORC model and the results indicated the effectiveness of the proposed control strategy. Sylvain Quoilin et al. [9] proposed three control strategies with PI controller and it was found that a model predictive control strategy based on the steady-state optimization of the cycle under various conditions is the best. There are lots of organic working fluids and the systems with different working fluids have different dynamic response performance. There are few researches about the dynamic response performance of ORC with different working fluids. In this study, the dynamic math models of four ORC systems with different working fluids and RC as WHRS of a natural gas engine are established by Simulink. Based on this, their dynamic response performance is compared and analyzed, finding some regularity to provide useful reference for the control design of ORC systems with different working fluids. 2. System description In this study, all the ORC and RC systems are applied to recover the exhaust waste heat of a natural gas engine that is a stationary electric generating plant of rated power 1,000kW. All the systems are designed based on the rated working condition of the engine. The heat balance experiments of the gas engine have been done and main parameters of the exhaust at rated work condition are shown in Table 1. It can be seen from Table 1 that the exhaust temperature after turbo charger is quite high. Therefore, it is very meaningful to recover the exhaust waste heat. According to the actual volume ratio of natural gas and air and assume that the fuel burns completely, the composition of the exhaust can be calculated. Then the exhaust thermo-physical property can be known. Fig. 1 is the schematic diagram of ORC or RC as WHRS for the natural gas engine. Table 1. Main parameters of the natural gas engine Parameter

values

Number of cylinders

8

Rated power

1,000 kW

Endurance speed

600r/min

Exhaust temperature after turbo charger

540ć

Volume flow rate of intake air

1.16 m3/s

Volume flow rate of natural gas

0.089m3/s

Exhaust mass flow rate

1.56kg/s

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Fig. 1. The schematic diagram of ORC or RC WHRS

3. Math model The dynamic models of the main components are built at first and then system model is created by appropriately combining each of the component models according to their interrelationships. Because pump and expander dynamics are very fast compared to the heat exchangers [8], their models are usually replaced by static models. The MB (moving boundary) method is used to build the evaporator and condenser model. Taking evaporator as example, the evaporator is divided into three regions: subcooling region, two-phase region and superheated region. Lumped parameter method is used in each region. The idea of a moving boundary model is to dynamically track the lengths of the different regions in the heat exchanger. The notations used in the moving boundary model are given in Fig. 2. Other notations not appearing in the figure are αo (the heat transfer coefficient between exhaust and pipe wall), α1,α2,α3 (internal pipe heat transfer coefficient in subcooling, two-phase and superheated region), p(the pressure in evaporator), A, Aw (the cross sectional area of inner pipe, and pipe wall). The general differential mass balance for the three regions is˖

³

L1

0

L1 wm w AU dz ³ dz 0 wz wt

0

(1)

The general differential energy balance for the three regions is˖

³

L1

0

L1 wmh w AU h Ap dz ³ dz 0 wt wz

³

L1

0

D iS Di Tw Tr dz

(2)

A simplified differential energy balance for the wall is: c pw U w Aw

dTw dt

D iS Di Tr Tw D oS Do Te Tw

(3)

Equation (1-3) are integrated over the three regions to give the general three region lumped models for a two-phase heat exchanger. Applying Leibniz’s rule (equation (4)) on mass and energy balance equations and simplifying the equations, the moving boundary models of the three regions can be acquired.

³

z2

z1

wf ( z , t ) dz wt

dz dz d z2 f ( z , t )dz f ( z2 , t ) 2 f ( z1 , t ) 1 dt ³z1 dt dt

(4)

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Fig. 2. Notations used in the Moving Boundary Model

Accordingly, the calculation about exhaust is also divided into three regions and the condenser model is also built by the same MB method. The pump model is defined by a simple expression for the mass flow rate˖

m pump

K v U pumpVcylZ

(5) Where ηv is the volumetric efficiency, ρpump is the working fluid density at the pump inlet, Vcyl is the cylinder volume and ω is revolution speed. In the pump, the working fluid goes through a non-isentropic pumping process. The ideal enthalpy of working fluid after isentropic pumping is written as hspout, hpout and hpin are the enthalpy of working fluid at the outlet and inlet of pump, respectively. ηsp is the isentropic efficiency of the pump, so the consumed work of pump can be calculated as:

Wp hpout

m hpout hpin

hpin

hspout hpin

K sp

(6)

(7)

The turbine is simplified as a nozzle:

mt

Cv Uout ( p pc )

(8) Where Cv is a coefficient, ρout is the outlet density from the evaporator, p is the pressure in the evaporator and pc is the condensing pressure. In the evaporator, the working fluid goes through a nonisentropic pumping process. The ideal enthalpy of working fluid after isentropic expanding is written as hstout, htout and htin are respectively the enthalpy of working fluid at the outlet and inlet of turbine. ηst is the isentropic efficiency of the turbine, so the output power can be calculated as: Wt m htin htout (9) htout htin (htin hsout )Kst (10) 4. Results and analysis In this part, four ORC systems with different working fluids and RC are designed to recover the engine exhaust waste heat as much as possible. Then their dynamic response performance are compared by the dynamic math model. The four working fluids are R245fa, R141b, cyclohexane and toluene. According to the decomposition temperature, organic working fluids can be classified to low-temperature working fluid and high-temperature working fluid. Therein, R245fa and R141b are low-temperature working fluids; cyclohexane and toluene are high-temperature working fluid. All the evaporating pressures of the five systems are designed as 2MPa, and all the condensing pressures are 200kPa. The

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lowest exhaust temperature at the outlet of heat exchanger is set as 433K. The design model is very common in lots of researches and it can refer to [3]. Fig. 3 and Fig. 4 describe the dynamic response of evaporating pressure (p) and working fluid enthalpy at the end of heating (hout) under the disturbance of exhaust temperature and working fluid mass flow rate. In Fig. 3, the exhaust inlet temperature decreases by 5% at 100 s. In Fig.4 the exhaust mass flow rate decreases by 3% at 100 s. From these figures, it can be found that under any disturbance the ORC with R245fa as working fluid responds most quickly, while RC responds most slowly and the response speed of RC is much slower than that of the other systems. Besides, the response speed of system with lowtemperature working fluid is shorter than that of system with high-temperature working fluid. Table 2 describes the mass flow rate, evaporating latent heat and settling time of different working fluids. Since the settling time of p and hout is nearly the same as shown in the figures, only evaporating pressure settling time is shown in Table 2. From this table, it can be found that the faster the system responds, the greater the working fluid mass flow rate is. In ORC or RC system, since pump and expander dynamics are very fast compared to the heat exchangers, the system dynamic performance is mainly decided by heat exchangers. Large working mass flow rate contributes to energy exchange by mass exchange, so the system becomes stable more quickly and the settling time is relative short. The working fluid mass flow rate of RC is much smaller than that of the other systems. As a result, the settling time of RC is much longer than the others. Besides, it can be seen that the less the evaporating latent heat of working fluid is, the faster the system responds. According to the system design math model of ORC or RC [3], small evaporating latent heat leads to large working fluid mass flow rate. As shown in Table 2, the working fluid with small evaporating latent heat has larger mass flow rate and large working fluid mass flow rate contributes to mass and energy exchange as mentioned above, so system settling time is shorter. Low-temperature working fluid usually has smaller latent heat than high-temperature working fluid, therefore, ORC with low-temperature working fluid has shorter settling time.

Fig. 3. The dynamic response of p (a) and hout (b) under the disturbance of exhaust temperature

Fig. 4. The dynamic response of p (a) and hout (b) under the disturbance of working fluid mass flow rate Table 2. Mass flow rate, evaporating latent heat, and settling time of different working fluids in different systems

Working fluid Mass flow rate (kg/s) Evaporating latent heat (kJ) Settling time of p in Fig.3/Fig.4 (s)

R245fa 2.725 109.3 38.1/44.3

R141b 2.434 137.7 59.2/62.4

Cyclohexane 1.292 203.1 100.9/116.8

Toluene 1.268 214.4 124.4/134.2

Water 0.251 1889.8 431/629


5. Conclusion In this paper, the dynamic math model of ORC as waste heat recovery system for an internal combustion engine is established. Based on these, the dynamic response performance of four ORC systems with different working fluids (R245fa, R141b, cyclohexane, toluene) and RC is compared and analyzed. It is found that: 1. The faster the system responds, the greater the working fluid mass flow rate is. 2. ORC with Low-temperature working fluids usually response faster than ORC with high-temperature working fluid and RC. Besides, the settling time of RC is much longer than the other ORC systems. 3. The less the evaporating latent heat of working fluid is, the faster the WHRS responds. This is also due to working fluid mass flow rate, because the working fluid with smaller evaporating latent heat often has greater mass flow rate. These can provide useful reference for the control design of ORC with different working fluids or RC as engine WHRS. Acknowledgements This work was supported by National Key Technology Support Program (No.2015BAG16B00). References [1] Daniela Gewald, Konstantinos Siokos. Waste heat recovery from a landfill gas-fired power plant. Renewable and Sustainable Energy Reviews, 2012, 16: 1779–1789. [2] Ulrik Larsen a, Leonardo Pierobon, Fredrik Haglind. Design and optimisation of organic Rankine cycles for waste heat recovery in marine applications using the principles of natural selection. Energy, 2013, 55: 803-812. [3] Hanlon M. BMW unveils the turbo steamer concept. . [4] Roy JP, Mishra MK, Misra A. Parametric optimization and performance analysis of a waste heat recovery system using Organic Rankine Cycle. Energy, 2010, 35:5049-62. [5] Aghaali H, Ångström H. A review of turbo compounding as a waste heat recovery system for internal combustion engines. Renewable and Sustainable Energy Reviews, 2015, 49: 819-824. [6] Moslem Yousefzadeh, Eray Uzgoren. Mass-conserving dynamic organic Rankine cycle model to investigate the link between mass distribution and system state. Energy, 2015, 93: 1128-1139. [7] Tilmann Abbe Horst, Hermann-Sebastian Rottengruber, Marco Seifert, Jürgen Ringler. Dynamic heat exchanger model for performance prediction and control system design of automotive waste heat recovery systems. Applied Energy, 2013, 105:293–303. [8] Sylvain Quoilin, Richard Aumann, Andreas Grill, Andreas Schuster, Vincent Lemort, Hartmut Spliethoff. Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles. Applied Energy, 2011, 88: 2183–2190 [9] Benato A, Kærn M R, Pierobon L. Analysis of hot spots in boilers of Organic Rankine Cycle units during transient operation. Applied Energy, 2015, 151: 119-131.

Biography The presenter Xuan Wang received a bachelor degree in automotive power engineering from Jilin university, Changchun China, in 2012. He is now a Ph.D. student at Tianjin university, and his research interests include waste heat recovery of internal combustion engines and distributed energy systems.

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