Abstract— Key process parameters in the synthesis of heat exchanger networks, such as process stream supply and target temperatures and process stream flowrates, may vary from time to time due to issues such as changing environmental conditions, plant start-ups/shut-downs, changes in product quality demand, etc. Also some other key design parameters which may also change from time to time include the availability of utilities as well as their costs. These changes may be due to factors such as seasonality issues, e.g. for utilities sourced from renewable energies, or government policies in form of tax, availability of utilities due to shortage of supply, etc. This implies that heat exchanger networks should not only be designed to be flexible in order to satisfy heat demand under changing process parameter scenarios, but should also be flexible in situations where utility costs as well as their availability change from time to time. Hence this paper aims to extend existing stage-wise superstructure (SWS) based multi-period heat exchanger network synthesis methods to be capable of satisfying the heat demand under scenarios where both process stream parameters and utility parameters such costs change from time to time in a pre-defined manner. The approach used entails extending the current multi- period SWS model through the inclusion of additional time index to represent future costs of utilities. The model is applied to one example so as to demonstrate its benefits. I. INTRODUCTION The synthesis of heat exchanger networks (HENs) has received significant attention in the last four decades due to issues such as global energy crises and climate change. However, the focus has mostly been on achieving a simultaneous reduction in both energy usage and the associated capital costs in single period scenarios [1]. Single period in this context implies that process stream parameters such as supply and target temperatures and flow rates do not change with time. However, in reality, this is not the case due to the fact that changes in environmental conditions, plant start-ups/shut-downs, changes in feed quality, changes in product quality demand, process upsets, and even deliberate changes in some of these parameters by plant operators, may influence stream parameters which may result in them changing from time to time. Changes of this nature, especially those that can be pre-determined, can be referred to as multi- period changes. This implies that heat exchanger networks need to be designed to be flexible in order to satisfy the multi- period profile of process heat demand in a cost efficient manner. Some of the methods that have been developed for multi-period heat exchanger network synthesis have been based on sequential, simultaneous and stochastic approaches. Under the sequential approach, we have the technique of *Research supported by NRF. A. J. Isafiade is with the Department of Chemical Engineering, University of Cape Town, Rondebosch, 7701 South Africa (phone: 27-216504869; e-mail: aj.isafiade@uct.ac.za). Floudas and Grossmann [2], which is a multi-period version of the linear program (LP) and mixed integer linear program (MILP) of Papoulias and Grossmann [3] for single period problems. The aim in this method is to determine the minimum utility required for each period of operation in a minimum number of units network. This method was further extended by Floudas and Grossmann [4] to a scenario where the multi-period minimum investment cost network that corresponds to the minimum utility and minimum number of units targets obtained from the LP-MILP model of Floudas and Grossmann [2], is automatically generated based on a non-linear program (NLP). This extension by Floudas and Grossmann [4] is based on insights from the single period case previously presented by Floudas, et al. [5]. Other multi- period sequential based approaches include the works of Mian, et al. [6] and Mian, et al. [7]. The work of Mian, et al. [6] which is also an extension of the multi-period models of Floudas and Grossmann [2] and Floudas and Grossmann [4], also involves the multi-period utility integration and scheduling technique presented by Marechal and Kalitventzeff [8]. The technique aims to select an optimal utility, including its scheduling, among a host of options of utilities. Mian, et al. [7] further extended the work of Mian, et al. [6] through the inclusion of material and electrical storage. Some of the papers under the category of simultaneous based approaches for the synthesis of multi-period HENs include the works of Aaltola [9], Verheyen and Zhang [10], Isafiade and Fraser [11], Isafiade, et al. [12], Isafiade and Short [13], Short, et al. [14], Sadeli and Chang [15], Jiang and Chang [16]. The technique presented by Aaltola [9], used an average area approach in the multi-period objective function. The average area approach implies that the size of the heat exchanger connecting the same pair of streams in more than one period of operation and in the same interval of the multi- period SWS model is the average of the areas required by the stream pair in the different periods of operations. Verheyen and Zhang [10] on the other hand used the maximum area approach in the SWS multi-period objective function. The maximum area approach ensures that the size of the heat exchanger selected to exchange heat between the same pair of streams in the same interval of the superstructure, but at different periods, is the maximum area required. It is worth stating that according to Isafiade and Fraser [11], these two approaches would fail to give the correct weighting in terms of quantity of utilities used in each period for cases where the period durations are unequal. Hence Isafiade and Fraser [11] modified the objective function of the multi-period SWS based model to address the aforementioned limitation in the Synthesis of Flexible Multi-Period Heat Exchanger Networks for a Changing Utility Cost Scenario* Adeniyi J. Isafiade 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP) May 28-31, 2017. Taipei, Taiwan 978-1-5090-4396-5/17/$31.00 ©2017 IEEE 499