Area, Cost and Resilience Targets for Heat Exchanger Networks

This thesis presents improved area and capital cost targets for synthesis of heat exchanger networks (HEN) for fixed operating conditions, and a new resilience target for synthesis of HENs for changing, uncertain operating conditions. In addition, methods are presented to predict, before synthesis, the trade-off between cost and resilience. A pair of "transshipment" nonlinear programs (NLP) is formulated to calculate the area and capital cost targets for HEN synthesis with unequal heat transfer coefficients and different capital cost laws (for different materials of construction, pressure ratings, etc.) when there are constraints on the number of matches, forbidden matches, and required matches with specified areas (for revamp synthesis). With these NLPs, the trade-off between area and number of units can be evaluated before synthesis. In addition to the targets themselves, solution of the NLPs yields "ideal" temperature profiles (much like the composite curves) for a HEN achieving the targets, and a selection of stream matches and their heat loads which provide an excellent starting point for synthesis of HENs achieving (within a few percent) the area and capital cost targets. For changing or uncertain operating conditions, a Class 1 resilience target is presented which predicts, given the nominal operating conditions, the largest uncertainty range for which a "practical" HEN (with few more units and stream splits than that required for nominal conditions) can be synthesized. This resilience target also predicts whether trade-offs (in utilities, number of units, or size of uncertainty range) must be made to achieve resilience, and the operating condition and constraint most likely to limit resilience. A nonlinear program is formulated to calculate the Class 1 HEN resilience target. Trade-offs with minimum approach temperature, utility consumption, and nominal network area are presented. The use of the Class 1 resilience target as a synthesis tool is discussed. Finally, a simple procedure to predict the trade-off between cost and resilience is introduced so that a process engineer can design for an economically "optimal" amount of resilience.