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|Title:||Heat Exchanger Network Design, Monitoring and Optimization|
|Authors:||Ati, Maheshwar Kiran Uma|
|Keywords:||Chemical Engineering;Chemical Engineering|
|Abstract:||<p>In process industries, heat exchanger networks represent an important part of the plant structure. The purpose of the networks is to maximize heat recovery, thereby lowering the overall plant costs. Previously published research on heat exchanger networks deals with two categories:</p> <p>• Synthesis of heat exchanger networks with the goal of designing a structure that provides the lowest total (capital plus operating) costs.</p> <p>• Data reconciliation with the goal of establishing true performance of the network and identifying correct heat transfer coefficients for individual exchangers in the network.</p> <p>Since heat exchanger models are highly nonlinear due to presence of log mean temperature difference term, solution of the network models is not always guaranteed. Most of the published results have used some form of approximation of the log mean temperature difference term. The approximations have been designed to provide reasonable accuracy while providing better convergence properties. Nevertheless, these are approximations and lead to the results that are not quite accurate. The goal of this research is to develop heat exchanger network models and algorithms for design, monitoring and optimization that are easy to implement in engineering practice and have excellent convergence properties.</p> <p>Presented here is a new heat exchanger network simulation algorithm which solves rigorously heat exchanger network equations in three phases:</p> <p>Phase 1: Solve mass balance equations for the network, i.e. determine flows in all branches of the network. These equations are linear.</p> <p>• Phase 2: Compute heat exchanger heat transfer factor for each exchanger in the network. Computation of the factor for each heat exchanger employs current flows through the exchanger and values of the exchanger variables at some base operating conditions.</p> <p>• Phase 3: Compute all heat exchanger outlet temperatures, given temperatures of the inlet streams and the results from Phase 1 and Phase 2. The computation in this phase is also employing a set of linear equations, while retaining full rigorous of the heat transfer equations.</p> <p>Hence, we have successfully transformed solution of a heat exchanger network into multi-phase solutions of sets of linear equations. This approach is then used for HEN synthesis and data reconciliation of HENs.</p> <p>HEN synthesis has been extensively studied over years and significant progress has been achieved in the development of robust methods for design of cost-optimal networks but one of the relatively less addressed issues is to deign HENs based on standard or modular sizes of heat exchangers. The major complexities in HEN synthesis are handling the combinatorial nature of the problem and finding a feasible and optimum solution using simultaneous synthesis methods. In this research, HEN simulation algorithm combined with differential evolutionary optimization is used for design of HENs with modular sizes of heat exchangers. This approach is successfully applied to examples available in the literature. Previously published results have used heat exchangers that have been sized for a placement at a specific location in a heat exchanger network, thereby aiming to provide the lowest cost solution. The research presented here shows that equally good or better solutions can be obtained by using standard, modular sizes of the heat exchangers. The approach used in this work is more realistic, since in practice heat exchangers are available in standards sizes, not custom made (in other words, a heat exchanger typically would have a size of 50 or 100 sq ft, but not 49.8 or 101.9 sq ft as may be calculated by the methods published in the literature).</p> <p>Data reconciliation and parameter estimation is an important step in HEN performance monitoring. In the current research, the HEN simulation algorithm is extended to develop a framework for data reconciliation of HEN and to estimate the change in the overall heat transfer coefficient of heat exchangers. This methodology is successfully implemented on two case studies from the literature.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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