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(357e) Towards Safe and Operable Process Intensification Systems

Authors: 
Pappas, I. S., Texas A&M University
Tian, Y., Texas A&M University
Katz, J., Texas A&M University
Burnak, B., Texas A&M University
Avraamidou, S., Imperial College London
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
The challenges that process industries face to enhance their economic, environmental and safety performance have led to the development and incorporation of Process Intensification (PI) solutions. Several Process Systems Engineering (PSE) approaches have been proposed in the literature to address the synthesis, design, operation and optimization of PI systems [1,2,3,4,5]. As far as the operability of these systems is concerned, the combination of multiple process operations in a single unit results in a reduction of the degrees of freedom in comparison to having consecutive process units, which limits control opportunities and rises safety concerns [6,7]. Apart from that, the steady state and dynamic models that are used for the description of intensified systems are coupled and highly nonlinear, making the construction of accurate approximation representations to reduce the computational complexity, challenging [8]. Thus, the derivation of operable PI designs which incorporate safety considerations is critical and still an active area of research.

This work presents a strategy for the systematic assessment of the safety and operability performance of intensified process systems. Utilizing the PARametric Optimization & Control (PAROC) Framework [9], we are able to benchmark and compare PI systems. Firstly, we develop high-fidelity dynamic models of conventional and the equivalently derived intensified designs. Based on these models, we derive optimal control policies using multiparametric programming and define and compare the regions of operation of the aforementioned systems. Safety considerations are incorporated in the control design problem from which the failure and consequence severity are quantified [7,10]. The proposed approach is highlighted through a reactive separator case study.

References:

[1] Tian, Y., Demirel, S. E., Hasan, M. F., & Pistikopoulos, E. N. An overview of process systems engineering approaches for process intensification: state of the art. Chemical Engineering and Processing-Process Intensification, 2018. DOI: 10.1016/j.cep.2018.07.014.

[2] Papalexandri, K. P., & Pistikopoulos, E. N. Generalized modular representation framework for process synthesis. AIChE Journal, 1996, 42(4), 1010-1032.

[3] Demirel, S. E., Li, J., & Hasan, M. F. Systematic process intensification using building blocks. Computers & Chemical Engineering, 2017, 105, 2-38.

[4] Pichardo, P., & Manousiouthakis, V. I. Infinite DimEnsionAl State-space as a systematic process intensification tool: Energetic intensification of hydrogen production. Chemical Engineering Research and Design, 2017, 120, 372-395.

[5] Tula, A. K., Babi, D. K., Bottlaender, J., Eden, M. R., & Gani, R. A computer-aided software-tool for sustainable process synthesis-intensification. Computers & Chemical Engineering, 2017, 105, 74-95.

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

[7] Tian, Y., & Pistikopoulos, E. N. Synthesis of Operable Process Intensification Systems—Steady-State Design with Safety and Operability Considerations. Industrial & Engineering Chemistry Research, 2019. DOI: 10.1021/acs.iecr.8b04389.

[8] Nikačević, N. M., Huesman, A. E., Van den Hof, P. M., & Stankiewicz, A. I. Opportunities and challenges for process control in process intensification. Chemical Engineering and Processing: Process Intensification, 2012, 52, 1-15.

[9] Pistikopoulos, E. N., Diangelakis, N. A., Oberdieck, R., Papathanasiou, M. M., Nascu, I., & Sun, M. PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 2015, 136, 115-138.

[10] Freeman, R. A. (1990). CCPS guidelines for chemical process quantitative risk analysis. Plant/Operations Progress, 9(4), 231-235.