Phenomena-based Process Synthesis-Intensification

Nipun Garg*

*Corresponding author for this work

Research output: Book/ReportPh.D. thesis

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Abstract

Process synthesis methods generally deals with identification of reactions required to produce desired products, identification of downstream processes to obtain desired product purity and decision making in terms of their sequencing. These process synthesis problems are generally open ended and combinatorial that can generate number of solutions using different approaches. However, the solution entirely depends on the considered search space and thus are limited to
existing unit-operations hindering the generation of innovative solutions that could significantly improve the process performance and efficiency by effectively using the maximum driving force available for a task. Thus, one of the practical ways to generate more efficient, economic and sustainable process alternatives, counter ongoing challenges and future problems is to develop approaches and methods that are generic in nature and can be applied over a wide search space
to determine innovative and hybrid/intensified solutions. Process Intensification (PI) is one of the approach that has enormous potential to achieve this objective. A recent trend in terms of holistic PI approaches is the use of bottom-up approach that diverts from traditional unitoperation based approaches within process synthesis and process intensification. These bottomup approaches are based on the physicochemical phenomena/functions/building blocks at the
lower level of aggregation increasing the search space and thus generating novel and innovative solutions at higher level i.e. unit-operation level. The research work done in this project is based on phenomena-based bottom-up approach.
The main objective of this work is the development and application of systematic phenomenabased synthesis-intensification framework for direct and indirect synthesis of novel, innovative and intensified solutions without pre-postulation of possible unit-operations. The fundamental pillars of this framework are definition and use of the phenomena building blocks (PBBs) that includes all possible phases (spanning vapor, liquid and solid), identification of phenomena using thermodynamic insights that are combined using the combination rules and generation of a phenomena-based superstructure to systematically identify novel, innovative and intensified flowsheet alternatives. The generated flowsheet options are ranked based on Enthalpy Index (EI) to identify potential alternatives for detailed analysis (economics, sustainability and life cycle analysis). One of the novel features of this framework is that it is capable of not only generating
more economic and sustainable novel intensified solutions for an existing process flowsheet (indirect synthesis or retrofit) but also allows the simultaneous direct synthesis-intensification by generating phenomena-based superstructure using the phenomena-based approach without any prior information about the process. Alongside, new phenomena and their classes are introduced over entire search space, systematic algorithms based on thermodynamic insights are
developed to identify the desirable phenomena and combine them in order to generate novel and intensified solutions. The developed framework is multiscale as it operates at phenomena, task and unit-operation scale. The framework developed in this work along with associated algorithms, knowledge bases and
tools are tested with three case studies: production of Dimethyl Ether (DME) from methanol, production of benzene by Hydrodealkylation (HDA) of toluene and biological production of succinic acid. The framework is tested for both direct and indirect synthesis-intensification application. In each of the case study, several novel, innovative and intensified alternatives are systematically generated using this approach.
Original languageEnglish
Place of PublicationLyngby, Denmark
PublisherTechnical University of Denmark
Number of pages243
Publication statusPublished - 2019

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