Abstract
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.Keywords: Shell and tube heat exchanger, feed forward, artificial neural network
Highlights
Efficient transfer of heat between two non-mixing fluid streams is a basic requirement in many process industries
The thermal characteristics of such a heat exchanger depends on the tube side design, shell design, the thermo-physical properties of fluids, and the phase change during heat transfer if any
Log Mean Temperature Difference (LMTD) method is simpler, it involves several challenges such as complicated geometries like finned tubes, fouling patterns and phase change. These complications can be simplified using some common assumptions in the shell and tube heat exchanger design:
Summary
Efficient transfer of heat between two non-mixing fluid streams is a basic requirement in many process industries. The thermal characteristics of such a heat exchanger depends on the tube side design, shell design, the thermo-physical properties of fluids, and the phase change during heat transfer if any. There are two common methods of steady state heat exchanger design namely, Log Mean Temperature Difference (LMTD) method and Effectiveness ‒ Number of Transfer Units (ε-NTU) technique. LMTD method is an iterative process that involves recalculation of design steps to get the best overall design. LMTD method is simpler, it involves several challenges such as complicated geometries like finned tubes, fouling patterns and phase change. These complications can be simplified using some common assumptions in the shell and tube heat exchanger design:
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
