An alternative disjunctive optimization model for heat integration with variable temperatures

Abstract

This paper presents an alternative model to deal with the problem of optimal energy consumption minimization of non-isothermal systems with variable inlet and outlet temperatures.The model is based on an implicit temperature ordering and the "transshipment model"proposed by . It is supplemented with a set of logical relationships related to the relative position of the inlet temperatures of process streams and the dynamic temperature intervals. In the extreme situation of fixed inlet and outlet temperatures, the model reduces to the "transshipment model". Several examples with fixed and variable temperatures are presented to illustrate the model's performance.

Simultaneous chemical process synthesis and heat integration with unclassified hot/cold process streams

“…For instance, Grossmann et al (1998) presented a disjunctive model to explicitly locate a stream above, across or below a potential pinch candidate. Navarro-Amorós et al (2013) presented an alternative disjunctive model that uses temperature intervals and the transshipment model for heat integration with variable temperatures. Hui (2014) proposed a model that utilizes pseudo stream temperatures.…”

Disjunctive model for the simultaneous optimization and heat integration with unclassified streams and area estimation

“…The proposed formulation has proved to be numerically very efficient. The total number of variables and equations is lower than alternative formulations for dealing with the same problem proposed by Navarro-Amorós et al (2013) for problems without unclassified streams, or the extension proposed by Kong et al (2017) that also considers unclassified streams, and the CPU time is reduced by 3-4 orders of magnitude.…”

Optimization of heat integration with variable stream data and non-linear process constraints

“…To demonstrate the effectiveness of this new NLP formulation, the test Problems 1-4 of Navarro- Amorós et al (2013) are solved by the Big-M model proposed by Grossmann et al (1998) and the proposed NLP model. Stream data of these problems are shown in Table 1.…”