Bilevel and parallel programing‐based operability approaches for process intensification and modularity

Dynamic Operability Analysis for Process Design and Control of Modular Natural Gas Utilization Systems

Process modularization is an alternative process design and construction framework, in which modular units are independent and replaceable blocks of a process system. While modular plants have higher efficiency and are safer to construct than conventional stick-built plants (Roy, S. Chem. Eng. Prog. 2017, 113, 28-31), they are significantly more challenging to operate because of the loss in the control degrees of freedom that comes with process integration and intensification (Bishop, B. A.; Lima, F. V. Processes 2021, 9, 2165). To address this challenge, in this work, operability analyses are performed to consider the design and operation of modular units. Initially, a steady-state operability analysis is employed to find a set of feasible modular designs that are able to operate considering different modular plant conditions. A dynamic operability analysis is then applied to the feasible designs to identify the operable designs that are capable of rejecting the operational disturbances. Lastly, a closed-loop control measure is introduced to compare the performances of the different operable designs. The proposed approach is implemented in a modular membrane reactor to find a set of operable designs considering different natural gas wells, and the respective closed-loop nonlinear model predictive control performance of these units is evaluated.

Dynamic and Statistical Operability of an Experimental Batch Process

The operability approach has been traditionally applied to measure the ability of a continuous process to achieve desired specifications, given physical or design restrictions and considering expected disturbances at steady state. This paper introduces a novel dynamic operability analysis for batch processes based on classical operability concepts. In this analysis, all sets and statistical region delimitations are quantified using mathematical operations involving polytopes at every time step. A statistical operability analysis centered on multivariate correlations is employed for the first time to evaluate desired output sets during transition that serve as references to be followed to achieve the final process specifications. A dynamic design space for a batch process is, thus, generated through this analysis process and can be used in practice to guide process operation. A probabilistic expected disturbance set is also introduced, whereby the disturbances are described by pseudorandom variables and disturbance scenarios other than worst-case scenarios are considered, as is done in traditional operability methods. A case study corresponding to a pilot batch unit is used to illustrate the developed methods and to build a process digital twin to generate large datasets by running an automated digital experimentation strategy. As the primary data source of the analysis is built in a time-series database, the developed framework can be fully integrated into a plant information management system (PIMS) and an Industry 4.0 infrastructure.

Modeling, Simulation, and Operability Analysis of a Nonisothermal, Countercurrent, Polymer Membrane Reactor

As interest in the modularization and intensification of chemical processes continues to grow, more research must be directed towards the modeling and analysis of these units. Intensified process units such as polymer membrane reactors pose unique challenges pertaining to design and operation that have not been fully addressed. In this work, a novel approach for modeling membrane reactors is developed in AVEVA’s Simcentral Simulation Platform. The produced model allows for the simulation of polymer membrane reactors under nonisothermal and countercurrent operation for the first time. This model is then applied to generate an operability mapping to study how operating points translate to overall unit performance. This work demonstrates how operability analyses can be used to identify areas of improvement in membrane reactor design, other than just using operability mapping studies to identify optimal input conditions. The performed analysis enables the quantification of the Pareto frontier that ultimately leads to design improvements that both increase overall performance and decreases the cost of the unit.