Multilayer Operability Framework for Process Design, Intensification, and Modularization of Nonlinear Energy Systems

Abstract

An optimization-based multilayer operability framework is introduced for the process design of nonlinear energy systems that are challenged by complexity and highly constrained environments. In the first layer of this framework, a mixed-integer linear programming (MILP)-based iterative algorithm considers the minimization of the footprint and the achievement of process intensification targets. Then, in the second layer, an operability analysis is performed to incorporate into the approach key features for optimality and feasibility, accounting for the system operation with changeable input conditions. The outcome of the framework consists of a set of modular designs, considering the aspects of both size and process operability. For this study, the nonlinear system is represented by multiple linearized models, resulting in low computational expense and efficient quantification of operability regions. The developed framework is applied to a membrane reactor for direct methane aromatization conversion to hydrogen and benzene. Subsystems of dimensionalities of 2 × 2 and 3 × 3 (design inputs × outputs) are considered in the first layer to obtain a modular design region. The possible modular designs inside this region are then ranked according to an operability index obtained from an additional 3 × 3 (operational inputs × outputs) mapping. This step analyzes the effect of operational inputs, producing a mapping of total dimensionality of 6 × 3 (inputs × outputs). The application of the developed framework generates two candidate designs for system modularity, the most operable design and the optimal design with respect to process intensification in terms of footprint minimization. The developed framework thus provides guidelines for obtaining modular designs that simultaneously consider process intensification and operability aspects.

Novel Module-Based Membrane Reactor Design Approach for Improved Operability Performance

“…In the chemical industry, a modular plant is a unit where “the process equipment, instrumentation, valves, piping components, and electrical wiring are mounted within a structural steel framework” [ 6 ]. In even simpler terms, the main focus of modularity with respect to the chemical industry is to scale down traditionally large processes in such a way that they can fit inside a structure to be transported via a flatbed truck [ 7 ]. This allows for them to be transported to various sites such as natural gas or oil wells and deployed more easily without the need of pipelines.…”

Operability and control in process intensification and modular design: Challenges and opportunities

“…Stepping to the equipment‐based steady state and dynamic design, significant progresses have been made in the PSE community to explore design under uncertainty under multiple time scales. Detailed reviews can be found in Yuan et al 97 and Burnak et al, 82 while an indicative list is given as follows: Steady‐state flexibility with multiperiod design — for example, heat exchanger network, 83 mass and heat exchange network 98 Dynamic flexibility with multiperiod design — for example, distillation column, 99 air separation system 100 Design with controllability considerations — for example, reaction‐separation system, 101 divided wall column 102 Operability‐based design — for example, membrane reactor 31,103 Integrated design and control — for example, combined heat and power system, 104 reactive distillation 92,105 …”

Dynamic and Statistical Operability of an Experimental Batch Process

“…Operability concepts enable the understanding of how a process can reach desired specifications when subjected to input conditions and limited by design constraints, while considering expected disturbances. In the process operability framework, a generic process model is defined as shown in Figure 1, in which inputs, u ∈ R m , are manipulated inside their available limits, and along with disturbances, d ∈ R q , stimulate the model (M) to generate the outputs, y ∈ R n [3,15].…”

“…Processes 2021, 9, 441 2 of 18 In this work, a dynamic operability study is conducted based on classical operability concepts [11,15]. The operability analysis previously developed for steady-state conditions is performed for each time step as the process evolves, and thus the sets and OI values are indexed by timestamp.…”