This article presents a novel energy flow redistribution methodology to achieve optimal operation of heat exchanger networks. The proposed method aims to manipulate the propagation path of a disturbance through the network to reduce its impact on utility consumption. Specifically, an optimization problem is formulated to generate new duty targets for heat exchangers of the network when a disturbance is encountered. Subsequently, a feedback control system is designed to track these targets by manipulating bypasses around the process heat exchangers. The effectiveness of the proposed framework is illustrated with the help of three benchmark examples. The proposed approach can handle disturbances in inlet as well as target temperature, inlet flow and heat transfer coefficient of individual heat exchangers.

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This paper presents a novel energy flow redistribution methodology to achieve optimal operation of heat exchanger networks (HENs). The proposed method aims to manipulate the propagation path of a disturbance through the network to reduce its impact on utility consumption. Specifically, an optimization problem is formulated to generate new duty targets for heat exchangers of the network when a disturbance is encountered. Subsequently, a feedback control system is designed to track these targets by manipulating bypasses around the process heat exchangers. The effectiveness of the proposed framework is illustrated with the help of three benchmark examples. The proposed approach can handle disturbances in inlet as well as target temperature, inlet flow and heat transfer coefficient of individual heat exchangers.

This paper focuses on the optimal operation of heat exchanger networks (HENs) by using a concept of energy flow redistribution (EFR). EFR methodology uses the structure of the HEN to direct the energetic impact of a disturbance along a favorable (e.g., minimum utility) path. Previously, EFR has been implemented using a two-step approach, wherein a steady state optimizer uses EFR to compute a new optimal operating point and the HEN is transitioned to this point using a feedback control strategy. Using a motivating example, it is shown that under practical conditions of unmeasured disturbances and/or plant–model mismatch, this two-step approach results in suboptimal performance. To this end, using a model predictive control (MPC) framework, these steps are combined to facilitate closed-loop EFR. Specifically, the governing equations of EFR are embedded inside the MPC to guide it toward the minimum utility point. Using two case study examples, it is shown that the closed-loop EFR-based controller successfully handles unmeasured disturbances as well as plant–model mismatch and achieves better performance as compared to the existing methodologies.