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

Abstract

We propose a mixed-integer nonlinear programing (MINLP) model for the simultaneous chemical process synthesis and heat integration with unclassified process streams. The model accounts for (1) streams that cannot be classified as hot or cold, and (2) variable stream temperatures and flow rates, thereby facilitating integration with a process synthesis model. The hot/cold stream "identities" are represented by classification binary variables which are (de)activated based on the relative stream inlet and outlet temperatures. Variables including stream temperatures and heat loads are disaggregated into hot and cold variables, and each variable is (de)activated by the corresponding classification binary variable. Stream inlet/outlet temperatures are positioned onto "dynamic" temperature intervals so that heat loads at each interval can be properly calculated. The proposed model is applied to two illustrative examples with variable stream flow rates and temperatures, and is integrated with a superstructure-based process synthesis model to illustrate its applicability.

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

“…stream (Kong et al, 2017). The objective of this paper is to extend the research made in our last work (Quirante et al, 2017) to the case in which there are unclassified process streams.…”

“…Temperature and flowrate bounds are shown in Table 4. This problem was originally proposed by Kong et al (2017). We use it as a means to validate the model -as far as we know, the work by Kong et al (2017) is the only one that deals with unclassified stream-and show the performance of the proposed approach.…”

“…This problem was originally proposed by Kong et al (2017). We use it as a means to validate the model -as far as we know, the work by Kong et al (2017) is the only one that deals with unclassified stream-and show the performance of the proposed approach. Hot 440 130 1 2 Hot 380 380 100 3 Cold 180 415 3 4 Cold 100 250 3 5 Hot 240 190 1 6 Hot 430 210 2 7 Hot 280 140 2 8 Hot 355 The results show that the number of continuous and binary variables and the total number of equations is much lower in the proposed model in comparison to the MINLP model developed by Kong et al (2017).…”

“…The model is solved in around three seconds of CPU time. Although the model in this work and the one presented by Kong et al (2017) have been solved in different computers and therefore we cannot do a direct comparison, the four orders of magnitude reduction in CPU time and the lower number of variables (specially the number of binaries) and constraints show the potential applicability of the new approach.…”

Process synthesis using block superstructure with automated flowsheet generation and optimization

“…All problem parameters along with the process descriptions are provided in the Supporting Information. This problem was originally posed by Kong et al in a different context of simultaneous process synthesis and heat integration. In this work, we adapt to only consider synthesis without heat integration, and therefore, our results are not comparable with the results reported previously.…”