Cross Flow Plate-fin Heat Exchanger Research Articles

Fault detection and isolation in uncertain dynamic systems using composite optimization and inferential sensing

Inferential sensing is a cost-effective and reliable approach to replace expensive and impractical hardware sensing, while yielding robust fault detection and isolation (FDI) during system maintenance or operation. Yet, their high computational footprint necessitates the employment of advanced and efficient estimation techniques, along with symbolic mathematics and automatic differentiation. Herein, we attempt to combine model-driven sensor selection methods with inferential sensing to accurately identify faults in the presence of system noise and uncertainty. During sensor selection, we employ criteria from information theory to maximize the estimability of system faults from available system measurements. The most informative sensor set stems from a comparison of all possible sensor suites via information theoretic criteria expressed as functions of the Fisher Information Matrix. Optimal inferential sensors are calculated through a novel fusion of symbolic regression and information theory. For built-in test deployment, we utilize k-Nearest Neighbors classification to assess the capability of each sensor suite for all plausible instantiations of uncertainty and system noise, and evaluate FDI performance benefits from the inclusion of inferential sensor(s). The proposed computational framework is deployed with steady-state and dynamic models of a cross-flow plate-fin heat exchanger, at varying levels of measurement noise and uncertainty. As shown, the augmented system of composite sensors (i.e., inferential and hardware) provides accurate information on system fault(s) and reduces the evidence of uncertainty. Compared to traditional sensor approaches, the proposed techniques are shown to be more precise while simultaneously granting increased insight into details otherwise hidden by noise. Thus, they form a robust and reliable tool when conducting fault diagnosis and possibly toward system safety.

Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm

Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (NEGUs), total annual cost (TAC), and heat exchanger surface area (A), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for NEGUs, 17% for TAC, and 4.5% for A, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE.

A comprehensive design and optimization of an offset strip-fin compact heat exchanger for energy recovery systems

• Parametric, sensitivity, and optimization of compact heat exchanger is presented. • A normalized sensitivity analysis is used to identify the critical parameters. • Geometric parameters and fluid velocity appeared as the most influential parameters. • The optimization reduced the operational cost by 78%, and the total cost by 77%. Energy recovery in conventional thermal systems like power plants, refrigeration systems, and air conditioning systems has enhanced their thermodynamic and economic performance. In this regard, compact heat exchangers are the most employed for gas to gas energy recovery because of their better thermal performance. This paper presents an economic optimization of a crossflow plate-fin heat exchanger with offset strip fins. A detailed software-based numerical code for thermal, hydraulic, economic, and exergy analysis is developed for three fin geometries. Genetic Algorithm, parametric, and normalized sensitivity analyses are used to discover the most influential parameters to optimize the total cost. The parametric study showed that with the increase of mass flow rates and plate spacing, outlet stream cost and operating cost increased due to the rise in pressure drops. Finally, the optimization reduced the operational cost by ∼78.5%, stream cost by ∼64.5%, and total cost by ∼76.8%.

Heat transfer enhancement using non-equally structure in a plate-fin heat exchanger with offset fins

In this study, a cross-flow plate-fin heat exchanger with offset fins is optimized by considering the effects of flow maldistribution for air side. For this purpose, the study is focused on an increase in the rate of heat transfer, which can be achieved by using non-equally fin structure. Numerical simulations have been carried out to investigate the thermodynamic characteristics of the non-equally full-size plate-fin heat exchanger by using the porous media approach. Based on the numerical model, flow distribution, total heat rate and pressure drop of the plate-fin heat exchanger are studied. A asymmetric structure with heat transfer enhancement is presented in this study. After comparing numerical predictions of the total heat transfer rate and the pressure drop under various Reynolds number. It is observed that, the percentages of increase in effectiveness for the final non-equally structure are in the range of 2.5-6.2% and the pressure drop remains almost constant in the cases of air inlet velocity fixed at 9.5627 m/s. The increasing asymmetric structure is numerically verified to improve the flow distribution of the plate-fin heat exchanger.

Inference of faults through symbolic regression of system data

We present the development of inferential sensors that use system input and output measurements to improve the accuracy and robustness of fault detection and isolation. These inferential sensors transform and augment the sensed information of a system to: (i) minimize the evidence of uncertainty in the inferred variables, decreasing the rates of false alarms and nondetections; and (ii) provide distinguishable estimates of the existence and/or severity of faults, decreasing the rate of misdiagnoses. The proposed method symbolically regresses the noisy and uncertain system measurements, using genetic programming, to evolve uniquely explainable mathematical functions that minimize a least-squares objective of the fault inference. A standard workflow utilizing the proposed algorithm for fault diagnostics is presented and illustrated in the classification of the severity of fouling in a cross-flow plate-fin heat exchanger. The effectiveness and robustness of the method are explored at different test designs, assessed using k-nearest neighbors classification, and compared to other traditional fault classification methods. The extension of the inferential sensors to information theoretic metrics, where the system model is augmented to improve the evidence of fault(s) is also discussed.

Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics

An algorithm is presented for the design of inferential sensors for fault diagnostics in thermal management systems. The algorithm uses input and output sensed system information to improve the detection and isolation of a fault by generating inferential sensors that augment the measured information to: (i) reduce the evidence of uncertainty in the inferred variables, and thus decrease false alarm and nondetection rates; and (ii) provide distinguishable responses to faults, and thus reduce reduce the rate of misdiagnoses. The novelty of the algorithm is its use of genetic programming to evolve explainable inferential sensors that maximize information criteria specific to fault diagnostics. The chosen criteria: (i) least squares regression; and (ii) Ds -optimality (calculated from the Fisher Information Matrix), leverage symbolic mathematics and automatic differentiation to obtain parametric sensitivities of the measured outputs and inferential sensors. The algorithm is included in a standard work for fault diagnostics, where its effectiveness is assessed through k-NN classification and illustrated in an application to an aircraft cross-flow plate-fin heat exchanger.

A Novel Equivalence Approximate Model for Second Law Based Optimization of Three-Fluid Cross-Flow Plate-Fin Heat Exchanger Using Genetic Algorithm

Abstract Number of entropy generation is a significant performance evaluation factor for heat exchanger as it results in trade-off between heat transfer and pressure drop. Optimization of the three-fluid heat exchanger (3−FHX) for minimum number of entropy generation unit requires ε−NTU relation which is unavailable for three-fluid cross-flow heat exchanger due to various complexities involved. A new approximate methodology for the optimization is presented by proving the equivalence of 3−FHX with a coupled two-fluid heat exchanger (2−FHX) followed by using ε−NTU relationship for 2−FHX. The experimental and numerical validation of the equivalence model is presented. Three-fluid cross-flow compact heat exchanger with offset strip fins is optimized for minimum number of entropy generation units using genetic algorithm (GA). Optimum design variables including heat distribution factor are determined for different cross-flow arrangements of 3−FHX with heat duty and pressure drop as constraint for both even and uneven fin dimensions case in all the passages. The present complex model including large number of continuous design variables can be handled well by GA. Validation of the results is carried out by using graphical techniques and very good agreement is observed. Additionally, sensitivity of the heat exchanger dimension has also been checked on the number of entropy generation along with the pressure drop in all the three fluids.

Thermodynamic Optimization of Three-Fluid Cross-Flow Heat Exchanger Using GA and PSO Heuristics

Optimization technique for a three-fluid cross-flow plate-fin heat exchanger with offset strip fins is developed, considering significant variables subjected to given constraints. The objective of optimization is focused on maximizing the hot fluid effectiveness and minimizing the number of entropy generation units in the three-fluid cross-flow plate-fin heat exchanger. Governing equations for a cross-cocurrent flow arrangement of a three-fluid heat exchanger are solved using finite element method for given boundary conditions. The obtained mean exit fluid temperatures from FEM are used to determine the rate of entropy generation and hot fluid effectiveness of the heat exchanger. Results obtained from the optimization of a reduced model of two-fluid cross-flow heat exchanger is compared with previously published results, thus serving as a validation of the optimization technique. Geometric parameters of the heat exchanger are varied to get the optimum results. The present investigation uses two different heuristics, namely Genetic algorithm (GA) and Particle Swarm Optimization (PSO) to find the optimum design values based on the objectives. Minimization of number of entropy generation units is treated to be a single objective function and optimum solutions are determined using both GA and PSO, both giving a function value of 0.0534786. Similarly, considering maximization of the hot fluid effectiveness, the optimum function value from both techniques is found to be 0.99997095. The results obtained from both the methods are compared and provides almost identical design values, with PSO taking lesser time for execution. Also, considering minimization of number of entropy generation units and maximization of hot fluid effectiveness of the heat exchanger as objective functions, multi-objective optimization is performed using GA alone and the multiple results obtained has been illustrated as a pareto-front.

A novel single and multi-objective optimization approach based on Bees Algorithm Hybrid with Particle Swarm Optimization (BAHPSO): Application to thermal-economic design of plate fin heat exchangers

As a novel hybrid optimization approach, the single and multi-objective BAHPSO are investigated for thermal designing of the cross-flow plate fin heat exchanger (PFHE) under given heat duty and pressure drop constraints. Because both of the Particle Swarm Optimization (PSO) and Bess Algorithm (BA) are operating with a random primary population of solutions, the current study combined their searching abilities for the first time, and presented a novel searching procedure named BAHPSO. In the current investigation, Multi-Objective optimization (MO) of BAHPSO is simultaneously employed to acquire the maximum effectiveness and the minimum total annual cost (TAC) of a heat exchanger as two contradict objectives and then results are compared with MOPSO and MOBA. Hot and cold side length, fin frequency, number of fin layers, fin thickness, fin height, and fin lance length are chosen as seven decision parameters. Also, a sensitivity analysis is performed to study the impact of geometrical parameters on each objective function. Finally, accuracy and efficiency of the presented algorithm is proven via illustrative single-objective optimization case studies which adopted from the references. Results demonstrate that the BAHPSO can detect optimal shape with higher accuracy compared to other algorithms.

Numerical analysis of compact plate-fin heat exchangers for aerospace applications

PurposeThe purpose of this study is to find the thermo-hydraulic performances of compact heat exchangers (CHE’s), which are strongly depending upon the prediction of performance of various types of heat transfer surfaces such as offset strip fins, wavy fins, rectangular fins, triangular fins, triangular and rectangular perforated fins in terms of Colburn “j” and Fanning friction “f” factors.Design/methodology/approachNumerical methods play a major role for analysis of compact plate-fin heat exchangers, which are cost-effective and fast. This paper presents the on-going research and work carried out earlier for single-phase steady-state heat transfer and pressure drop analysis on CHE passages and fins. An analysis of a cross-flow plate-fin compact heat exchanger, accounting for the individual effects of two-dimensional longitudinal heat conduction through the exchanger wall, inlet fluid flow maldistribution and inlet temperature non-uniformity are carried out using a Finite Element Method (FEM).FindingsThe performance deterioration of high-efficiency cross-flow plate-fin compact heat exchangers have been reviewed with the combined effects of wall longitudinal heat conduction and inlet fluid flow/temperature non-uniformity using a dedicated FEM analysis. It is found that the performance deterioration is quite significant in some typical applications due to the effects of wall longitudinal heat conduction and inlet fluid flow non-uniformity on cross-flow plate-fin heat exchangers. A Computational Fluid Dynamics (CFD) program FLUENT has been used to predict the design data in terms of “j” and “f” factors for plate-fin heat exchanger fins. The suitable design data are generated using CFD analysis covering the laminar, transition and turbulent flow regimes for various types of fins.Originality/valueThe correlations for the friction factor “f” and Colburn factor “j” have been found to be good. The correlations can be used by the heat exchanger designers and can reduce the number of tests and modification of the prototype to a minimum for similar applications and types of fins.

Numerical analysis of the heat recovery efficiency for the post-combustion flue gas treatment in a coffee roaster plant

The heat recovery system for the post-combustion flue gas treatment in a coffee roaster plant is investigated using both a lumped and distributed parameter numerical modelling and a transient CFD approach. The combined analysis of the two numerical approaches enables to estimate the thermo-pneumatic behaviour of the entire plant as well as the optimal design of the heat exchanger.The effects of the heat recovery are evaluated in terms of improved energy efficiency and reduction of the fuel consumption. The counter-flow shell and tube and cross-flow plate fin heat exchangers are designed in order to have similar performance in terms of recovered heat and their performance on the real plant is predicted under actual plant operations. The overall volume of the plate fin device results to be approximately one tenth of the shell and tube architecture's volume, while the pressure drop of the two systems is comparable.Finally, the heat recovery from the flue gas enables to save up to 27% of the fuel necessary to power the post-combustor adopted for the exhausts' treatment and up to 13% of the total fuel consumption of the plant.

Many-objective optimization of cross-flow plate-fin heat exchanger

This paper presents a rigorous investigation of many-objective (four-objective) optimization of cross flow plate-fin heat exchanger and its comparison with multi-objective (two-objective) optimization. Maximization of effectiveness and minimization of total annual cost, total weight and number of entropy generation units are considered simultaneously as objective functions during many-objective optimization. Multi-objective heat transfer search (MOHTS) algorithm is introduced and applied to obtain a set of Pareto-optimal points of many-objective problem. Application example of plate-fin heat exchanger is presented to demonstrate the effectiveness and accuracy of proposed algorithm. Many objective optimization results form a set of solutions in four dimensional hyper objective space and for visualization it is represented on a two dimension objective space. Thus, results of four-objective optimization are represented by six Pareto fronts in two dimension objective space. These six Pareto fronts are compared with their corresponding two-objective Pareto fronts. Different decision making approaches such as LINMAP, TOPSIS and fuzzy are used to select the final optimal solution from Pareto optimal set of the many-objective optimization. Finally, to reveal the level of conflict between these objectives, distribution of each design variables in their allowable range is also shown in two dimensional objective spaces.

Efficiency enhancement and NOx emission reduction of a turbo-compressor gas engine by mass and heat recirculations of flue gases

A simple Turbo-Compressor model-GE MS 6001B PLTG-PLN-Sektor Tello Makassar with 30 MW power generation, 27.71% thermal efficiency and 26.07 exergy efficiency (at ISO condition) was considered for the efficiency enhancement and emission reduction. A cross flow plate-fin heat exchanger (PFHE) as an air pre-heater (heat recirculation) along with direct recirculation of a part of flue gases into the combustion chamber (mass recirculation) were considered as modifications of original turbo-compressor gas engine to increase thermal efficiency and reduce NOx emission. In a multi-objective optimization process, geometric and thermal specifications of the plate-fin heat exchanger as well as the percentage of the recirculated flue gas were obtained. The payback time for the capital investment of the heat exchanger and NOx emission were minimized simultaneously while the exergetic efficiency of the gas cycle was maximized and a frontier of optimal solution called as the Pareto frontier was obtained in objective space. The final optimal solution was selected from the Pareto frontier using three different decision-making methods, including the fuzzy Bellman-Zadeh, TOPSIS and LINMAP methods. It was shown that the best results in comparison to the simple cycle led to 34.7% reduction in NOx emission and 5.8% improvement in exergy efficiency (as difference).

Experimental Investigation of Thermal Behavior of Cross Flow Plate-fin Heat Exchanger with Offset Strip Fin

The Present work is carried out to investigate the thermal behavior of cross flow plate fin heat exchanger having offset strip fin. An experimental set up has been built in the laboratory to test the Plate fin Heat exchanger. Sets of experiments had conducted to determine the thermal performance of the given heat exchanger. Mass flow rate of cold fluid, mass flow rate of hot fluid, hot fluid inlet temperature, cold fluid inlet temperature, hot fluid and cold fluid inlet pressure are considered as input parameters. The values of the heat transfer co-efficient, effectiveness and pressure drops are obtained for the different value of the input parameters. The experimental results are compared with the correlations developed by Maiti and Sarangi, Manglik and Bergles and Joshi and Webb. Finally, based on the experimental results coluburn factor (j) correlation is developed to predict the value of heat transfer co-efficient.

Modeling of transient response of a plate fin and tube heat exchanger

In this paper, a new equation set describing transient heat transfer process in tube and fin cross-flow heat tube exchanger will be given and subsequently solved using the finite volume method. A numerical model of a single-row heat exchanger and double-row car radiator with two passes has been developed. Based on thermal and hydraulic measurements of a double row car radiator the experimental correlations were determined to calculate the Nusselt number on the air- and water-side. The results of numerical modeling reflect very well transient operation of the actual heat exchanger. A good agreement between numerical and experimental results was obtained. Using the method presented in the paper, one can build a mathematical model of a cross-flow plate-fin and tube heat exchanger with complicated tube arrangement.

Comparison of waste heat recovery performances of plate-fin heat exchangers produced from different materials

High amount of waste heat emerging from thermal processes performed in the industry can be recovered by means of equipment such as heat pipes, heat exchangers and heat recovery boilers. In this study, thermal performance of a heat recovery device, which enables air-to-air heat transfer, was examined. For this purpose, cross-flow plate-fin heat exchangers were produced by using three different materials, which are aluminum, polymer and cellulose. With the established apparatus, outlet temperatures with different values were obtained for fresh air and exhaust air. Thermal calculations, were performed with the mathematical model developed by using the Effectiveness- Number of Transfer Unit (e - NTU) method and their results were compared. It was seen that at the same air rate with the same exhaust air inlet temperature and the same fresh air inlet temperature; when the polymer heat exchanger is used, effectiveness value is 12,6% higher on an average than the aluminum heat exchanger. Similarly, when the cellulose heat exchanger is used, effectiveness value is 14,5% higher on an average than the polymer heat exchanger.

Optimal design of plate-fin heat exchangers by a Bees Algorithm

In this study, the application of Bees Algorithm (BA) in the optimum design of a cross flow plate-fin heat exchanger with offset strip fin is investigated. First, heat exchanger is optimized and designed according to the effectiveness optimization. Then, an analysis based on the second law of thermodynamics and minimizations of entropy generation units is performed. Specific heat duty, space restriction and permitted pressure drop are considered as the constraints for maximizing the effectiveness and minimizing the entropy generation units. Hot and cold flow length of the heat exchangers, number of fin layers, fin frequency, fin height, fin strip length and fin thickness are introduced as optimization variables. The effectiveness and accuracy of the suggested algorithm are compared with literature. The results have shown that BA can find optimum configuration with higher accuracy in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and preliminary design.

An imperialist competitive algorithm for optimal design of plate-fin heat exchangers

This paper presents the application of imperialist competitive algorithm (ICA) for optimization of a cross-flow plate fin heat exchanger. Minimization of total weight and total annual cost are considered as objectives. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from literature is presented to show the effectiveness of the proposed algorithm. The numerical results reveal that ICA can find optimum configuration with higher accuracy in less computational time when compared to conventional genetic algorithm (GA).

Second law based optimization of a plate fin heat exchanger using Imperialist Competitive Algorithm

This study explores the application of Imperialist Competitive Algorithm (ICA) in thermodynamic optimization of a cross-flow plate fin heat exchanger. The optimization aims at finding the minimum number of total entropy generation units under a given heat duty and pressure drop constraints. Seven design parameters namely, hot side length, cold side length, fin height, fin frequency, fin thickness, number of fin layers for hot side and offset-strip length are chosen as optimization variables. The constraints are handled using penalty function. To better understand the algorithm, a case study from the literature is presented. The results show that ICA can find better results comparing to traditional genetic algorithms. Key words: Optimization, plate fin heat exchanger, evolutionary algorithms, imperialist competitive algorithm.

Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an ε−NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.