Abstract

This work aims at developing a heat exchanger (HEX) sizing approach considering the need to maximize the heat recovery within the limitations of pressure drop and space. The application consists in the recovery of the energy contained in exhaust gases coming from an internal combustion engine (ICE). Two heat exchanger geometries are selected as case studies. The design approach involves the application of design of experiments (DOE) techniques and computational fluid dynamics (CFD) simulations. DOE techniques are used to observe the influence of some selected parameters (factors) in the design of the heat exchangers, and CFD simulations are carried out to determine the performance of the heat exchanger. The information obtained is used to determine local Nusselt number correlations that are used for the design of the heat exchangers.

Keywords: heat exchanger, heat waste, CFD, neural network, optimization

## 1. Introduction

Industrial applications of waste heat recovery require several types of heat exchangers. The correct selection and optimization of the heat exchangers are critical for heat transfer. Several papers have been published that deal with the selection of the most suitable heat exchanger technology for a specific application. Hatami et al. [1] developed a numerical study to model two types of heat exchangers (HEXs) used to recover the exhaust waste heat from internal combustion engines (ICEs). In the work, authors aimed at finding the best viscous model to fit experimental data. One of the exchangers belongs to a compression ignition (CI) engine with water as cold fluid, while the second exchanger belonged to a spark ignition (SI) engine with a mixture of 50% water and 50% ethylene glycol (EG) as cold fluid. From the study, authors concluded that the heat recovery can be improved by increasing the number of fins and length, where maximum heat recovery occurs with high engine load and speeds. On a different work, Hatami et al. [2] applied a response surface methodology (RSM) based on central composite design (CCD) to derive an optimization approach of finned-type heat exchangers to recover waste heat from the exhaust of a diesel engine. The design is performed for a single-point operation (1600 rpm and 60 N m) of an OM314 diesel engine. Based on the CCD principle, 15 exchangers with different fin heights (FH), fin numbers,

and fin thicknesses (FT) were numerically modeled, and optimization was carried out to maximize heat recovery and minimize pressure drop along the heat exchanger. The results showed that the height of the fins has a higher impact on pressure drop than fin number and thicknesses. On the other hand, fin number enhances heat recovery.

Bari et al. [3] performed a study on pancake-shaped heat exchangers to be fitted in a vehicle. The heat exchangers studied were of the shell-tube and U-tube type. CFD simulations were carried out to optimize the design and calculate the additional power that could be achieved by using these optimized heat exchangers. The effectiveness of pancake-shaped heat exchanger is on average 3% higher than that of the optimized round-shaped heat exchanger. Bari et al. [4] conducted experiments using water as the working fluid to estimate the exhaust waste heat recoverable from a diesel engine using two available heat exchangers. Two identical shell and tube heat exchangers were fitted into the exhaust of the engine, and experiments were conducted to estimate the additional energy that could be gained with this setup. Simulation tools were used to compare the performance of the heat exchangers with experimental data. Then the effects of changing important parameters such as length, diameter of shell, and number and diameter of tubes on the heat recovery were investigated. It was found that the effectiveness was higher for smaller shell diameters. After optimization, the additional power increased from 16 to 23.7%.

Tan et al. [5] reported the use of artificial neural network (ANN) models to simulate the thermal performance of a compact fin-tube heat exchanger with air and water/ethylene glycol antifreeze mixtures as the working fluids. They demonstrated that, once trained, an artificial neural network could predict the overall heat transfer rate between the liquid and air steams with a high degree of accuracy. The neural network predictions were in much closer agreement to the experimental data than corresponding predictions derived using a conventional nonlinear regression model.

Shivakumar et al. [6] tested the applicability of neural networks in order to correlate the experimentally determined heat transfer parameters of a multi-pass cross-flow heat exchanger. The waste heat from an internal combustion (IC) engine was used to heat the water in a cross-flow heat exchanger. The experimental results were used to train the ANN model. A multilayer perceptron (MLP) with backpropagation algorithm was used for training the network. The predicted results by the ANN model were compared with experimental data. They concluded that an MLP network can be used to predict the thermal performance characteristics of multi-pass cross-flow heat exchanger using a limited number of experimental data.

Hatami et al. [7] used a multi-objective optimization approach based on ANN and genetic algorithm (GA) to the numerical outcomes of a finned-tube heat exchanger in a diesel exhaust heat recovery application. The results confirm that the optimized case widely increased the recovered heat and exergy while keeping the pressure drop at low levels. Although the optimized case exhibited higher irreversibility, its second law efficiency is significantly greater than the non-optimized case, especially at high engine loads. The average efficiency of the proposed HEX is about 8% for the exergy recovery from the exhaust of a light diesel engine.

Aly et al. [8] investigated the 3D turbulent flow and heat transfer of coiled tubein-tube heat exchangers. Heat exchangers are analyzed considering conjugate heat transfer from the hot fluid in the inner-coiled tube to the cold fluid in the annulus region. After simulations, the Taguchi method was used to find the optimum condition for some design parameters in the range of coil diameter from 0.18 to 0.3 m and tube and annulus flow rates from 2 to 4 and 10 to 20 l/min, respectively. Results showed that the Gnielinski correlation (used extensively for predicting Nusselt number for turbulent flow in ducts) can be used to predict Nusselt number for both the inner-coiled tube and the annular coiled tube using the friction factor correlation

#### Exhaust Gas Heat Recovery for an ORC: A Case Study DOI: http://dx.doi.org/10.5772/intechopen.86075

for helical tubes. The application of the Taguchi method showed that the annulus side flow rate, the tube side flow rate, the coil diameter, and the flow configuration are the most important design parameters in coiled tube-in-tube heat exchangers.

Hossain et al. [9] optimized heat exchangers used in the recovery of exhaust heat from a 40-kW diesel generator. With the available experimental data, computer simulations were carried out to optimize the design of the heat exchangers. The optimized heat exchangers were then used to estimate additional power gained considering the turbine isentropic efficiency. The proposed heat exchangers could produce 11% additional power using water as the working fluid at a pressure of 15 bar. The effects of the working fluid pressure were also investigated to maximize the additional power production. The pressure was limited to 15 bar which was constrained by the exhaust gas temperature. However, higher pressure is possible for higher exhaust gas temperatures from higher capacity engines.

This work aims at showing a stepwise approach for the sizing of a heat exchanger for waste heat recovery and subsequent use in an Organic Rankine Cycle (ORC). For maximum power production and minimum pressure drop, the exchanger must be optimized. Besides, space limitation poses an additional constraint to the design. The approach introduced in this work allows the designer to simultaneously achieve all these design objectives.

## 2. Method description

The proposed method seeks to maximize heat transfer and minimize pressure drop. Besides, within the exchanger, overheated areas (to avoid evaporation of the cold fluid) and overcooled areas (to avoid condensation and corrosion on the hot side) must also be minimized [10]. A combination of different tools is used to solve the complex problem. To avoid overdesign, accurate Nusselt correlation must be developed. Given the space limitations, two exchanger geometries, namely, a finned heat exchanger and a helical heat exchanger, are analyzed to have an additional degree of freedom between heat recovery and pressure drop. For the same heat load, the finned tube will exhibit lower heat transfer area but higher pressure drop, while the helical tube will have larger surface area but lower pressure drop. Given the constraints in terms of mass flow rate, pressure drop, heat transfer, and space, maximum and minimum values for these parameters must be fixed. Since a very large possible combination of operating conditions can result, it is important to discriminate between them. One way of doing this is by designing the experiments or identifying the most representative set of design variables that allow to reduce the search space. Once this is done, in principle the geometry should be constructed and tested to see which of the designs exhibit overheating and overcooling areas. Computational fluid dynamics techniques can be used to this end. Besides, CFD can also provide local heat transfer coefficients which can be correlated for design purposes. As mentioned earlier, the approach used in this work is by means of artificial neural networks.

Since maximum power production is the final desired outcome, the design with the maximum exergy recovery will lead to maximum power production. Thus, exergy analysis is included, and the Organic Rankine Cycle is modeled using the HYSYS simulator [11].

#### 2.1 Process description

The steps followed in the analysis introduced in this work are detailed described below and graphically shown in Figure 1.

Heat and Mass Transfer - Advances in Science and Technology Applications

Figure 1. Sequence of the proposed analysis and design method.


Exhaust Gas Heat Recovery for an ORC: A Case Study DOI: http://dx.doi.org/10.5772/intechopen.86075

9. Selection and design of final heat exchanger.

The diagram of the process is presented in Figure 1.
