(118f) Building Block-Based Design and Intensification of Chemical Processes

Demirel, S. E., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Li, J., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Process intensification (PI) targets the development of novel processing equipment and methods with substantial improvements in size, energy efficiency, and/or waste production [1]. Often times, these designs are not known beforehand and identification of PI opportunities demand significant enhancement in driving forces for mass/heat/momentum transfer and/or propelling the upper limits imposed by thermodynamics [2]. Incorporation of these process intensification principles into the conceptual process design necessitates a shift from the modus operandi to new process design paradigms. Accordingly, several successful systematic methodologies have been laid down in the past which depart from unit operation-based representation of process alternatives and employ a set of processing tasks, phenomena, or functions to identify new designs [3]. The leading approach in this vein is the phenomena-based design, where a chemical process is observed as combination of several physicochemical phenomena, e.g. mixing, splitting, phase contact, phase change, etc [4]. However, performing phenomena-based synthesis and intensification in an optimization-based framework still remains as a challenge. To this end, building block superstructure [5-9] provides unique advantages for systematic process intensification by enabling an optimization-based methodology. It relies on a representation based on building blocks positioned in a two-dimensional grid. By using either single or multiple building blocks, many physicochemical phenomena, processing tasks and unit operations can be represented within the grid. A Mixed Integer Nonlinear Programming (MINLP) model describes the superstructure. This optimization formulation is used to determine the position of the active phenomena within the grid which yields an automated intensified flowsheet generation methodology.

In this work, we will demonstrate the benefits of building block-based approach for systematic process intensification on several large-scale design problems and ask whether intensification can break-through the current barriers and lead to novel designs which are more sustainable and, at the same time, more economic compared to their non-intensified counterparts. We first use either an existing flowsheet or generate one through building block superstructure. Following the generation of the base-case design, we utilize the building block-based phenomena representation and corresponding MINLP model with rate-based/equilibrium formulations to describe mass/heat transfer and search for intensified solutions. Here, we also consider simultaneous heat integration strategies that can enhance the base-case and also provide more energy-efficient intensification pathways. We will demonstrate this design and intensification framework through several case studies including an example on commercial grade purity ethylene glycol production. Through the proposed methodology, we found several intensified solutions that use 40-60% less number of equipment than the base-case while achieving the same process target. These intensified flowsheet alternatives feature heat integrated reactive/non-reactive distillation equipment, reactive dividing wall columns, etc., and provide significant savings in capital investment. Furthermore, we also found that, although, intensification results in loss of degrees of freedom in control [10], it adds to design flexibility in terms of sustainability. Intensification enables this by substantial decrease in the lowest emissions possible from the process with significantly less economic trade-off compared to the non-intensified base-case. This leads to more flexibility in the conceptual design stage for sustainability considerations.

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[2] Tian, Y., Demirel, S. E., Hasan, M. M. F., Pistikopoulos, E. N. (2018). An Overview of Process Systems Engineering Approaches for Process Intensification: State of the Art. Chemical Engineering and Processing: Process Intensification, 133, 160-210.

[3] Demirel, S. E., Li, J., & Hasan, M. M. F. (2019). Systematic process intensification. Current Opinion in Chemical Engineering. https://doi.org/10.1016/j.coche.2018.12.001.

[4] Lutze, P., Babi, D.K., Woodley, J.M. and Gani, R. (2013). Phenomena based methodology for process synthesis incorporating process intensification. Industrial & Engineering Chemistry Research, 52(22), 7127-7144.

[5] Demirel, S. E., Li, J., and Hasan, M. M. F., (2017). Systematic Process Intensification using Building Blocks, Computers and Chemical Engineering, 105, 2-38.

[6] Li, J., Demirel, S.E. and Hasan, M.M.F., 2018. Process synthesis using block superstructure with automated flowsheet generation and optimization. AIChE Journal, 64(8), 3082-3100.

[7] Li J., Demirel S.E., Hasan M.M.F. Process Integration using Block Superstructure. Industrial & Engineering Chemistry Research, 2018, 57: 4377–4398.

[8] Li, J., Demirel, S.E., & Hasan, M. M. F. (2019). Building Block-Based Synthesis and Intensification of Work-Heat Exchanger Networks (WHENS). Processes, 7(1), 23.

[9] Demirel, S. E., Li, J., and Hasan, M. M. F. (2019). A General Framework for Process Synthesis, Integration, and Intensification. Industrial & Engineering Chemistry Research. DOI: 10.1021/acs.iecr.8b05961.

[10] Baldea, M. (2015). From process integration to process intensification. Computers & Chemical Engineering, 81, 104-114.