(246k) Simultaneous Process Synthesis and Heat Integration Using a Single Superstructure

Demirel, S. E., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Li, J., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Optimization-based process synthesis approaches aim to obtain the best processing route among multiple alternatives by assembling alternative units into a process network and uses optimization as the screening tool in deciding for the most desirable flowsheet alternative [1]. Performance measure for this screening process, i.e. objective function for the optimization problem, can be either related with the economics of the process or it can be pertained to environmental, and/or social impact of the overall flowsheet. All of these objectives are functions of the energy input into the process flowsheet which makes the energy efficiency, hence the degree of heat integration, a major decision criterion. If one decides on the process flowsheet first and then considers the integration alternatives to increase the energy efficiency, interactions among these two components of the process are not accounted for which may result in suboptimal solutions. Currently, there are several methods available in the literature for the simultaneous process synthesis and heat integration. Papoulias and Grosmann [2] proposed the first method for simultaneous synthesis of the process flowsheet and its heat and utility networks based on an MILP model in which temperatures has to be taken at discrete levels. Later, Duran and Grossman [3] proposed a model based on pinch point location which can deal with variable process flow rates and temperatures. Yee and Grossmann [4] proposed a new superstructure for simultaneous area and energy targeting for heat exchanger netwoks which does not rely on the pre-specified HRAT or EMAT and they showed that their model can be also used in tandem with process optimization and may be solved to optimality to get the optimal process flowsheet as well as its corresponding heat exchanger network. In 1998, Grossmann and Yeomans [5] proposed a model for minimum utility consumption with the concept of dynamic intervals to handle variable process flow rates and temperatures. Recently, Kong et. al. [6] published a method which extended the model proposed by Grossmann and Yeomans to unclassified process streams with variable stream temperature and flow rates.

Although aforementioned works lay the foundation for the simultaneous process synthesis and heat integration, their efficiency is directly related with the quality of the process synthesis superstructure. Recently, Demirel et. al. [7] proposed a building-block based superstructure for simultaneous process synthesis and intensification. They showed that the proposed superstructure can yield non-intuitive flow sheets while considering different mass integration alternatives without a priori postulation of the processing steps and the connectivity between them. In this work, we will build further on the building block superstructure and show that same superstructure can be also embedded with heat integration. In the proposed model, flow rates and temperature values are variables which make the overall problem an MINLP problem. We use dynamic interval construction for minimum utility targeting as proposed by Grossmann and Yeomans [5] and Kong et. al. [6] and solve the process synthesis and heat integration models simultaneously. We will demonstrate the applicability of the proposed approach by several case studies and show that it can be used as a generic tool for simultaneous process synthesis and heat integration eliminating the need for suggesting new superstructures for different synthesis problems.


[1] Chen, Q. and Grossman, I.E., 2017. Recent Developments and Challenges in Optimization-Based Process Synthesis. Annual Review of Chemical and Biomolecular Engineering, 8(1).

[2] Papoulias, S.A. and Grossmann, I.E., 1983. A structural optimization approach in process synthesis—III: total processing systems. Computers & chemical engineering, 7(6), pp.723-734.

[3] Duran, M.A. and Grossmann, I.E., 1986. Simultaneous optimization and heat integration of chemical processes. AIChE Journal, 32(1), pp.123-138.

[4] Yee, T.F. and Grossmann, I.E., 1990. Simultaneous optimization models for heat integration—III. Process and heat exchanger network optimization. Computers & Chemical Engineering, 14(10), pp.1165-1184.

[5] Grossmann, I.E., Yeomans, H. and Kravanja, Z., 1998. A rigorous disjunctive optimization model for simultaneous flowsheet optimization and heat integration. Computers & chemical engineering, 22, pp.S157-S164.

[6] Kong, L., Avadiappan, V., Huang, K. and Maravelias, C.T., 2017. Simultaneous chemical process synthesis and heat integration with unclassified hot/cold process streams. Computers & Chemical Engineering, 101, pp.210-225.

[7] Demirel, S.E., Li, J., Hasan, M.M.F., 2017. Systematic process intensification using building blocks. Computers and Chemical Engineering, http://dx.doi.org/10.1016/j.compchemeng.2017.01.044.


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