This article introduces a novel operability‐based approach for process design and intensification of energy systems described by nonlinear models. This approach is applied to a membrane reactor (MR) for the direct methane aromatization (DMA) conversion to benzene and hydrogen. The proposed method broadens the scope of the traditional path of the operability approaches for design and control, mainly oriented to obtain the achievable output set (AOS) from the available input set, and compare the computed AOS to a desired output set. In particular, an optimization algorithm based on nonlinear programming tools is formulated for the calculation of the desired input set that is feasible considering process constraints and intensification targets. Results on the application of the operability method as a tool for process intensification show reduction of the DMA‐MR footprint (≈77% reactor volume and 80% membrane area reduction) for an equivalent level of performance, when compared to the base case. This case study indicates that the novel approach can be a powerful tool for process intensification of membrane reactors and other complex chemical processes. © 2016 American Institute of Chemical Engineers AIChE J, 63: 975–983, 2017

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Process operability emerged in the last decades as a powerful tool for the design and control of chemical processes. Recent efforts in operability have been focused on the calculation of the desired input set for process design and intensification of natural gas utilization applications described by nonlinear models. However, there is still a gap in terms of problem dimensionality that nonlinear operability methods can handle. To fill this gap, in this article, the incorporation of bilevel and parallel programing approaches into classical process operability concepts is discussed. Results on the implementation of the proposed method show a reduction in computational time up to two orders of magnitude, when compared to the original results without parallelization. These results could be extrapolated for use in a supercomputer as presented in the computational time analysis performed. In terms of intensification, the proposed approach can produce a natural gas combined cycle plant modular design with a dramatic reduction in size, from the original 400 to 0.11 MW, while still keeping the high net plant efficiency. This approach thus provides a computationally efficient framework for process intensification of high-dimensional nonlinear energy systems toward modularity. The proposed approach also enables the verification of a modular design and conditions that can be obtained according to economic and physical constraints associated with a specific natural gas well production.

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In this Article, the recently developed multimodel and NLP-based operability approaches are reviewed and further developed in terms of theory, application, and software infrastructure. Classical process operability concepts are also revisited and contrasted with new extensions present in both approaches. A focus is given on the comparison between distinct measures of the operability index and the handling of infeasible portions of the desired operating region. For the software infrastructure outcome, a framework for the development of process operability algorithms is provided. In particular, generated codes from the studied approaches are included in an open-source platform that will grant access of the algorithms to researchers from academia and industry. This platform has the purpose of dissemination and future improvement of process operability algorithms and methods. For the development of a versatile process operability tool, the considered approaches are adapted to support process models of generic dimensionalities. New contributions are also incorporated such as the conventional operability index calculation and other methods to find optimal design regions. Nonlinear energy systems of different input × output dimensionalities (6 × 3, 7 × 3, and 3 × 3) are addressed to demonstrate the applicability of the features available in the developed software tool to process achievability analysis, intensification, and modularization.