(236a) An Open-Source Python-Based Toolbox for Enabling Fast Process Operability Calculations

Process Operability [1] has emerged in the past two decades as an effective and systematic framework for simultaneous design and control of chemical processes early in the conceptual phase, as opposed to the classical sequential approach of designing a process first and then assessing the achievability of its control objectives later. This way, Process Operability poses itself as a valuable resource for enabling the generation of novel intensified [2] and modular process [3] designs. In addition, Process Operability allows users to rank competing design/control structures via the quantification of the Operability Index (OI). However, current Process Operability methods involve the solution of nonlinear programming problems (NLP) [2] and computational geometry calculations [4] that are challenging in nature and may need a considerable amount of coding and transitioning among different software packages to be performed. The objective in this work is to develop and present a user-friendly Process Operability toolbox in Python, in which the NLP formulation and computational geometry calculations are fully integrated. Such a toolbox would allow researchers and practitioners to seamlessly use Process Operability as a tool directly, reducing their effort by addressing the aforementioned challenges.

To achieve this goal, the new tool is developed entirely in Python, inspired by the previous Operability App project in MATLAB [5]. The new tool now leverages state-of-the-art optimization solvers such as Ipopt [6] and Differential Evolution algorithms [7] for generating direct/inverse input-output mappings, and Computational Geometry [8] packages for enabling OI calculations. Therefore, the developed tool encapsulates all Process Operability required calculations in a single bundle using an open-source and free programming language, readily available for the academic/scientific community.

Case studies of nonlinear nature that are representative of industrial energy and chemical systems are addressed to illustrate the effectiveness of the proposed toolbox. Such studies involve emerging process intensification and modularization concepts towards enabling a modular manufacturing economy. The results obtained are consistent with the literature reported results, with the advantage of being easy to set-up and run when compared to traditional Process Operability approaches. The developed toolbox thus facilitates Process Operability analysis, seamlessly helping to generate design and control structures in a comprehensive software environment. This project therefore enables the further dissemination of operability concepts throughout academia and industry as an open-source application.

References

[1] C. Georgakis, D. Uztürk, S. Subramanian and D. R. Vinson, “On the operability of continuous processes,” Control Engineering Practice, vol. 11, pp. 859-869, 2003.

[2] J. C. Carrasco and F. V. Lima, “An optimization-based operability framework for process design and intensification of modular natural gas utilization systems,” Computers & Chemical Engineering, vol. 105, pp. 246-258, 2017.

[3] J. C. Carrasco and F. V. Lima, “Bilevel and parallel programing-based operability approaches for process intensification and modularity,” AIChE Journal, vol. 64, pp. 3042-3054, 2018.

[4] V. Gazzaneo and F. V. Lima, “Multilayer Operability Framework for Process Design, Intensification, and Modularization of Nonlinear Energy Systems,” Industrial & Engineering Chemistry Research, vol. 58, pp. 6069-6079, 2019.

[5] V. Gazzaneo, J. C. Carrasco, D. R. Vinson, and F. V. Lima, “Process Operability Algorithms: Past, Present, and Future Developments,” Ind. Eng. Chem. Res., vol. 59, no. 6, pp. 2457–2470, 2020.

[6] A. Wächter and L. T. Biegler, “On the Implementation of a Primal-Dual Interior Point Filter Line Search Algorithm for Large-Scale Nonlinear Programming”, Mathematical Programming 106(1), pp. 25-57, 2006.

[7] R. Storn and K. Price, “Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, 11, pp. 341 – 359, 1997.

[8] Baotić, Mato. "Polytopic computations in constrained optimal control." Automatika: 50.3-4 pp. 119-134, 2009.