Neural networks for cost estimation of shell and tube heat exchangers (original) (raw)

Comparisons between two types of neural networks for manufacturing cost estimation of piping elements

Expert Systems with Applications, 2012

The objective of this paper is to develop and test a model of manufacturing cost estimating of piping elements during the early design phase through the application of artificial neural networks (ANN). The developed model can help designers to make decisions at the early phases of the design process. An ANN model would allow obtaining a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. The developed model is compared with traditional neural networks and conventional regression models. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of shell and tube heat exchangers.

Thermal analysis of shell and tube heat exchangers using artificial neural networks

The Ethiopian journal of science and technology, 2016

The shell and tube heat exchangers are most commonly used industrial equipment to transfer heat from one fluid to another. The design process is specified by Tubular Exchanger Manufacturers Association (TEMA). The initial design has to be iteratively optimized for increasing the heat transfer, minimize the pressure drop and reduce the fluid pumping power. LMTD method is chosen for its simplicity and quick analysis. The design procedure if approached by Finite element method, needs high computing power and tedious to converge. A feed-forward artificial neural network is set up to simplify the iterative nature of the design process. This also gives the necessary quick design iteration cycles to reach the optimized design. A design space is created with heat exchanger parameters, and the feed forward network is trained with semi-empirical data. The trained network is used in the design performance evaluation. This approach shows promise of quick design changes and can accommodate variable thermo-physical properties of fluids, and can be trained for different fouling patterns in the heat exchangers from real time data. The neural network can predict the steady state performance within the design space and results match well with LMTD calculations. Subsequent to the steady state analysis, dynamic modeling is attempted. A neural network method is used to reduce the complication of the model. Simplified mathematical model is used initially to train the network. It is found that the feed forward networks can predict the dynamic behavior, but it needs additional parameters to improve its predictions.

Detection of significant parameters for shell and tube heat exchanger using polynomial neural network approach

Vacuum, 2018

Shell-and-tube heat exchangers (STHXs) are extensively used for numerous industrial applications. The exchange efficiency (EE) of heat exchanger (HX) greatly influences production quality and plant performance. Present paper explores significant parameters to boost up the exchange efficiency of HX. The polynomial neural network (PNN) was applied to optimize HX indicators for maximizing exchange efficiency. Six PNN models were employed to vary the nature of the training data and demand scenarios encountered by the heat exchanger. Model selection index (MSI) determines the models with better accuracy in contrast with other PNN models. It has been found that geometrical indicators: tube area (A), tube layout (Tl), tube pitch (TP) and tube diameter (TD) were found to be most sensitive indicators for enhancing exchange efficiency.

New Method for the Cost Assessment Analysis of Shell-And-Tube Heat Exchangers

Latin American Applied Research - An international journal, 2021

A new simplified method for the cost assessment analysis of shell-and-tube heat exchangers (STHE) is presented in this paper. A comparison was carried out between the values obtained by the proposed method with the FOB average real cost for a total of 410 heat exchangers, with heat transfer surfaces ranging between 5 to 1150 m2. It was found that the proposed method correlates well with an average error of 10.1% , for 84.15% of the available data. The method presents a lower adjustment in the range , with an average error of 12.9%, for 82.97 % of the available data. The best adjustments are located in the range, with an average error of only 8.0%, for 80.95 % of the available data. In all cases, the agreement of the proposed method is good enough to be considered satisfactory for practical design.

INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENT Online performance assessment of heat exchanger using artificial neural networks

Heat exchanger is a device in which heat is transferred from one medium to another across a solid surface. The performance of heat exchanger deteriorates with time due to fouling on the heat transfer surface. It is necessary to assess periodically the heat exchanger performance, in order to maintain at high efficiency level. Industries follow adopted practices to monitor but it is limited to some degree. Online monitoring has an advantage to understand and improve the heat exchanger performance. In this paper, online performance monitoring system for shell and tube heat exchanger is developed using artificial neural networks (ANNs). Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. ANN model for overall heat transfer coefficient of a design/ clean heat exchanger system is developed using a feed forward back propagation neural network and trained. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF), which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction interms of production loss.

Online performance assessment of heat exchanger using artificial neural networks

Heat exchanger is a device in which heat is transferred from one medium to another across a solid surface. The performance of heat exchanger deteriorates with time due to fouling on the heat transfer surface. It is necessary to assess periodically the heat exchanger performance, in order to maintain at high efficiency level. Industries follow adopted practices to monitor but it is limited to some degree. Online monitoring has an advantage to understand and improve the heat exchanger performance. In this paper, online performance monitoring system for shell and tube heat exchanger is developed using artificial neural networks (ANNs). Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. ANN model for overall heat transfer coefficient of a design/ clean heat exchanger system is developed using a feed forward back propagation neural network and trained. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF), which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction interms of production loss.

Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data

International Journal of Heat and Mass Transfer, 2001

We consider the problem of accuracy in heat rate estimations from arti®cial neural network (ANN) models of heat exchangers used for refrigeration applications. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer phenomena in these systems. A well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under dierent operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to ®nd regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates. The procedure outlined here can also help the manufacturer to ®nd where new measurements are needed. 7

Applications of artificial neural networks for thermal analysis of heat exchangers - A review

Artificial neural networks (ANN) have been widely used for thermal analysis of heat exchangers during the last two decades. In this paper, the applications of ANN for thermal analysis of heat exchangers are reviewed. The reported investigations on thermal analysis of heat exchangers are categorized into four major groups, namely (i) modeling of heat exchangers, (ii) estimation of heat exchanger parameters, (iii) estimation of phase change characteristics in heat exchangers and (iv) control of heat exchangers. Most of the papers related to the applications of ANN for thermal analysis of heat exchangers are discussed. The limitations of ANN for thermal analysis of heat exchangers and its further research needs in this field are highlighted. ANN is gaining popularity as a tool, which can be successfully used for the thermal analysis of heat exchangers with acceptable accuracy.

Economic optimization of shell and tube heat exchanger based on constructal theory

Energy, 2011

In this paper, the new approach of constructal theory has been employed to design shell and tube heat exchangers. Constructal theory is a new method for optimal design in engineering applications. The purpose of this paper is optimization of shell and tube heat exchangers by reduction of total cost of the exchanger using the constructal theory. The total cost of the heat exchanger is the sum of operational costs and capital costs. The overall heat transfer coefficient of the shell and tube heat exchanger is increased by the use of constructal theory. Therefore, the capital cost required for making the heat transfer surface is reduced. Moreover, the operational energy costs involving pumping in order to overcome frictional pressure loss are minimized in this method. Genetic algorithm is used to optimize the objective function which is a mathematical model for the cost of the shell and tube heat exchanger and is based on constructal theory. The results of this research represent more than 50% reduction in costs of the heat exchanger.► In this paper, constructal theory has been employed to optimization of shell and tube heat exchangers. ► Heat exchangers optimized by reduction of total cost using Constructal theory. ► The overall heat transfer coefficient of the heat exchanger is increased. ► Genetic algorithm is used to optimize the objective function. ► The results of this research represent more than 50% reduction in cost.

Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger

Neural Computing and Applications, 2016

In the present work, the performance of an airto-refrigerant laminated type evaporator is predicted using a genetic algorithm (GA)-integrated feed-forward neural network (FFNN) and recurrent neural network (RNN). The obtained results are compared with the results of the FFNN with back-propagation learning algorithm, as the most recommended algorithm in the literature. The considered evaporator consists of single-phase and two-phase regions in the refrigerant side which makes the ANN-based methods so suitable for its modeling. To train the mentioned neural networks, the steady-state experimental data of the evaporator performance include capacity, outlet refrigerant pressure and temperature and outlet air dry-and wet-bulb temperatures is collected with varying input parameters. The results show a good agreement with experimental data, and it is observed that RNN-based method has the best average root-mean-square error (1.169 against 5.017, 4.791 and 2.286 for FFNN, GA-trained FFNN and numerical modeling, respectively). In fact, using GA to optimize FFNN structure makes better results than conventional FFNN, but the RNN method provides the best results because of using suitable intelligent configuration. Also, in contrary to numerical method, it is much faster and calculation processing load is lower. Therefore, RNN is proposed as a substitute for FFNN and the GA-trained FFNN. Finally, a sensitivity analysis determined the inlet refrigerant pressure as the most important parameter in predicting the evaporator capacity. Keywords Automotive air conditioning system Á Compact heat exchanger Á Genetic algorithm Á Feedforward neural network Á Recurrent neural network & Javad Zare