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Process synthesis, integration, and intensification are the three pillars of process design. Current synthesis and integration methods are able to find optimal design targets and process configurations when all the alternatives are known beforehand. Process intensification, on the other hand, combines multiple physicochemical phenomena and exploits their interactions to create innovative designs. Often times, these designs are not known beforehand, and a phenomena-level representation of chemical processes are required to identify them. This disconnection between the three paradigms limits the ability to systematically discover optimal design pathways. We demonstrate that the building block representation, originally proposed in our earlier work on process intensification (Demirel, Li, and Hasan, Comput. Chem. Eng., 2017, 150, 2–38), has the potential to bridge this gap. Depending on the attributes assigned to the interior and the boundaries of these two-dimensional abstract building blocks, they can represent various intensified or isolated phenomena at the lowest level, various tasks at the equipment level, and various unit operations at the flowsheet level. This common multiscale representation enables an mixed-integer nonlinear optimization-based single framework for the sequential or simultaneous synthesis, integration, and intensification of chemical processes. Such a general framework is critical to reduce the risk of eliminating potential intensification pathways and candidate flowsheets at the conceptual design stage. The framework is demonstrated using a case study on an ethylene glycol process.

An alternative method for chemical process synthesis using a block-based superstructure representation is proposed. The block-based superstructure is a collection of blocks arranged in a two-dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed-integer nonlinear formulation for automated flowsheet generation and optimization-based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas V C 2018 American Institute of Chemical Engineers AIChE J, 00: 000-000, 2018

We propose a general mathematical model for various process integration problems, involving mass integration, heat integration, simultaneous mass and heat integration, and property integration. The process units, including regenerators/interceptors, mixers, and splitters are represented using blocks. The blocks are arranged in a two-dimensional grid, and the arrangement of these blocks gives rise to various process integration networks. The existence of connecting streams between adjacent blocks and jump flows among all blocks enables the necessary interaction between different blocks via material and energy flows. The size of the block superstructure is determined by the number of layers with mixing operations, process units, product streams, and heat integration stages. The general process integration model is formulated as a mixed-integer nonlinear optimization (MINLP) problem with the minimization of total annual cost as the objective. We demonstrate our approach using four case studies from the process integration literature.

Process intensification (PI) is a design concept that offers innovative solutions for making a substantial improvement in terms of cost, energy efficiency, emission, environmental footprint, processing volume, and safety of a chemical process. Incorporation of PI principles at the conceptual design stage can pave the way for more sustainable solutions. However, it is not trivial to identify effective intensification pathways considering the various trade-offs between multiple conflicting performance metrics. To that end, we combine the building block-based systematic PI (Demirel, Li, and Hasan, Comp. Chem. Eng., 2017, 150, 2–38) with the ε-constraint-based multiobjective optimization to synthesize both economically attractive and environmentally sustainable chemical process systems. We successfully apply this approach to discover several novel intensification pathways for an industrially relevant chemical process for ethylene glycol production. These new pathways suggest nonintuitive flowsheets involving partial intensification, as opposed to complete merging of reaction and separation phenomena. Partial intensification significantly increases the return on investment while reducing the indirect CO2 emissions when compared to traditional nonintensified and intensified designs.

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We propose an intensified process for combined separation and storage (CSS) of natural gas from various unconventional sources using a single multifunctional unit. The CSS process involves selectively storing methane in a column filled with an existing nanoporous zeolite material, while venting out the impurities. A multiscale model is used to optimize the methane storage and screen for the top zeolites under minimum purity requirements and loss constraints. Adsorption properties for different zeolites are obtained via Monte Carlo simulations. Zeolite SBN with a high storage capacity for pure methane is found to be most suitable for combined separation and storage over a range of feed compositions. A rank ordered list of top zeolites is also obtained along with the optimal process conditions. It is, however, observed that the effectiveness of the intensification of natural gas separation and storage is limited by the constraint on the loss of methane exiting the column.