Sebastian Recker

A Nonlinear Programming Approach to Conceptual Design of Reaction-Separation Systems

Abstract The conceptual design of chemical processes is a challenging task. Although systematic methods for process design have been developed in the last 25 years, only a few contributions address the simultaneous design and optimization of reaction and separation. Promising process variants should be identified in an early design stage to narrow down the usually large number of variants and to shorten the effort and time for rigorous process design. In this paper, we present a nonlinear programming approach to the conceptual design of integrated reaction-separation systems using shortcut models point of operation. Furthermore, it is shown how the novel reactor shortcut is combined with available separator shortcuts to screen alternatives of combined reaction-separation processes.

Systematic and Optimization-Based Design of Integrated Reaction-Separation Processes

Abstract The conceptual design of chemical processes is a challenging task. Although systematic methods for process design have been developed in the last 25 years, only a few contributions address the simultaneous design and optimization of integrated reaction and separation processes. In this paper, we present a systematic and optimization-based approach to the design of such processes. Shortcut methods are utilized to screen alternative flowsheet structures. For the most promising alternatives a rigorous optimization of the entire flowsheet is consequently executed to determine the best alternative. The whole process design framework constitutes a procedure of incremental refinement and successive initialization and thus allows for the systematic generation and evaluation of reaction-separation processes. Consequently, it helps to shorten the time for developing new and innovative processes. In this work the methodology is illustrated by means of the case study of ethyl tert-butyl ether production.

On the integration of model identification and process optimization

Abstract Reliable models for rate-based phenomena are the backbone of model-based process design. These models are often unknown in the early design phase and need to be determined from laboratory experiments. Although model-based experimental analysis and process design are often executed sequentially, the kinetic models might not be suitable to reliably design a process. In this paper, we address this problem and present a first step on the integration of model identification and process optimization. Rather than decoupling model identification and process optimization, we use information from process optimization to design optimal experiments for improving the quality of the kinetic model given the intended use of the model. Sensitivities, which describe the influence of parametric uncertainties on the economic objective used in process optimization, are used as weights for optimal experimental design. This way, the confidence in the parameter values is maximized to reduce their influence on the process optimization objective. This first step on the integration of model identification and process optimization improves the predictive quality of a reaction kinetic model for process design without any further experimental effort.

Bio-based Value Chains of the Future - An Opportunity for Process Systems Engineering

Abstract This paper argues for a paradigm shift to properly address the requirements of modelbased design of future bio-based value chains. Rather than focusing on the process with emphasis on separations and heat integration, the decisions on the molecular level require much more attention. A rough sketch of a systematic sequential and iterative model-based design strategy is presented and illustrated by a few examples related to the manufacturing of future bio-based fuels.