Optimal design and operation of reactive distillation systems based on a superstructure methodology

A novel methodology for the simultaneous optimisation of design and operation of a complex reactive distillation process, considering a number of process alternatives (e.g. pre-/side-reactor, side-stripper, additional columns etc.), is presented. The methodology is based on a superstructure approach, and a detailed cost-based objective function, solved by MINLP optimisation. The methodology is illustrated using different case studies of industrial interest with varying separation and reaction characteristics. For easy separations, in terms of relative volatilities and boiling points order, a single reactive distillation column is found to be optimal for both fast and slower kinetics. However, when the separation is more challenging (i.e. product is a middle-boiler), the design is more complex, even for fast kinetics, and additional processing units, such as a pre-reactor and/or additional distillation columns, are required to meet the product quality specifications. It is found that the design, i.e. the capital cost, mainly depends on the relative boiling point rankings. For operation, chemical reaction equilibrium is the dominant factor. It is demonstrated, however, that the combined effects of separation and reaction must be considered carefully when designing a reactive distillation process. The liquid holdup has an impact on the reaction performance, and proper choice of holdup can lead to a more flexible design, able to mitigate production failure issues even for slower reactions

Systematic Methods for Reaction Solvent Design and Integrated Solvent and Process Design

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016by: M. Sc. Teng ZhouLiteraturverzeichnis: Seite 100-10

Methodologies for the optimisation, control and consideration of uncertainty of reactive distillation

The work presented in this thesis is motivated by the current obstacles hindering the implementation of reactive distillation in industry, mainly related to the complexities of its design and control, as well as the impact of uncertainties thereupon. This work presents a rigorous methodology for the optimal design and control under uncertainty of reactive distillation. The methodology can also be used to identify and investigate mitigation strategies for process failures arising due to design and/or operation deficiencies under changed processing conditions, based on the evaluation of different design and/or control alternatives. The first step of the methodology is the simultaneous (MINLP) optimisation of the design and operation of a reactive distillation process superstructure, used to explore the possible steady-state design alternatives available, including ancillary equipment such as pre- and side-reactors, side-strippers and additional distillation columns, based on product-related constraints and a detailed objective cost function. The next step is the investigation of the dynamic control performance of this optimal system, where conventional and advanced process control strategies are considered in order to investigate how robust the system is towards operational disturbances, or whether revising the optimal steady-state design is required. As the optimisation depends heavily on accurate data for reaction kinetics and separation performance, the final step of the methodology is the evaluation of the impact of parameter uncertainty on the performance of the optimal controlled system, including redesigning the controlled system if required. The methodology is demonstrated using a number of industrially relevant case studies with different reaction and separation characteristics in order to investigate how these determine the design and control of an economically attractive and rigorous reactive distillation process. It is demonstrated that the process characteristics have a significant impact on the design of the system, and that auxiliary equipment may be required to meet production specifications and/or to ensure robust controlled behaviour. It is also shown that, under parameter uncertainty, an optimal controlled system may nevertheless face performance issues, and revising the design and/or operation of the process may be required in order to mitigate such situations