A process integration method with multiple heat exchange interfaces

In recent decades, energy efficiency has become one of the key issues facing industrial producers. Mounting economic, environmental and social factors motivate energy-intensive industries to improve their efficiency. Identifying energy saving retrofit opportunities in large-scale problems is extremely complex due to numerous interconnections and dependencies between process units, sub-units and utilities present on most industrial sites. Therefore, when attempting to identify promising retrofit opportunities, early design decisions that reduce the problem size are crucial. Techniques applying heat integration (HI) often use mathematical models and optimisation to survey potential solutions. Mixed integer linear programming (MILP) is often used for industrial energy efficiency case studies due to its flexibility and solution speed, while taking advantage of the extensive bodies of work dedicated to this type of problem. This work proposes a methodology based on HI to represent process energy requirements with different heat exchange interfaces. Switching from the currently used utility to another interface requires additional heat transfer area while it might bring operational benefits due to better integration of the system. The optimal combination of the processes with different interfaces is obtained by considering the trade-off between the additional heat exchanger area required and decrease in the operating cost. The proposed method provides early design decisions for retrofit solutions on industrial sites. Utilising this methodology provides a dual benefit of identifying the most promising options for retrofit applications while also eliminating inconsequential ones at an early stage of the analysis. The proposed method is applied to an industrial case study and exhibits the intended problem size reduction for further analysis. The initial problem is reduced by 40% in terms of the number of streams which promises a large reduction in the number of variables.