Optimal Design and Operation of Heat Exchanger Network

摘要

Heat exchanger networks (HENs) are the backbone of heat integration due totheir ability in energy and environmental managements. This thesis deals withtwo issues on HENs. The first concerns with designing of economically optimalHeat exchanger network (HEN) whereas the second focus on optimal operationof HEN in the presence of uncertainties and disturbances within the network. Inthe first issue, a pinch technology based optimal HEN design is firstlyimplemented on a 3–streams heat recovery case study to design a simple HENand then, a more complex HEN is designed for a coal-fired power plant retrofittedwith CO2 capture unit to achieve the objectives of minimising energy penalty onthe power plant due to its integration with the CO2 capture plant. The benchmarkin this case study is a stream data from (Khalilpour and Abbas, 2011).Improvement to their work includes: (1) the use of economic data to evaluateachievable trade-offs between energy, capital and utility cost for determination ofminimum temperature difference; (2) redesigning of the HEN based on the newminimum temperature difference and (3) its comparison with the base casedesign. The results shows that the energy burden imposed on the power plantwith CO2 capture is significantly reduced through HEN leading to utility costsaving maximisation. The cost of addition of HEN is recoverable within a shortpayback period of about 2.8 years. In the second issue, optimal HEN operationconsidering range of uncertainties and disturbances in flowrates and inlet streamtemperatures while minimizing utility consumption at constant targettemperatures based on self-optimizing control (SOC) strategy. The new SOCmethod developed in this thesis is a data-driven SOC method which uses processdata collected overtime during plant operation to select control variables (CVs).This is in contrast to the existing SOC strategies in which the CV selectionrequires process model to be linearized for nonlinear processes which leads tounaccounted losses due to linearization errors. The new approach selects CVsin which the necessary condition of optimality (NCO) is directly approximated bythe CV through a single regression step. This work was inspired by Ye et al.,(2013) regression based globally optimal CV selection with no model linearizationand Ye et al., (2012) two steps regression based data-driven CV selection but with poor optimal results due to regression errors in the two steps procedures.The advantage of this work is that it doesn’t require evaluation of derivativeshence CVs can be evaluated even with commercial simulators such as HYSYSand UNISIM from among others. The effectiveness of the proposed method isagain applied to the 3-streams HEN case study and also the HEN for coal-firedpower plant with CO2 capture unit. The case studies show that the proposedmethodology provides better optimal operation under uncertainties whencompared to the existing model-based SOC techniques.