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Item type:Publication, Access status: without files , A hierarchical approach for solvent selection based on successive model refinementLiquid-liquid extraction has widespread use in industry, e.g., for separation of highly diluted components from fermentation broths. Feasibility and economic operation of the extraction processes depend critically on the selection of a suitable solvent. While the choice for an optimal solvent inherently depends on the overall process performance, common methods for solvent selection focus on much simpler performance indicators which can lead to suboptimal solutions. In order to improve solvent selection, we present a hierarchical approach with successive model refinement. The approach builds on the prediction of thermodynamic properties by COSMO-RS, avoiding the need for experimental data in early conceptual design phase. In the approach, advanced pinch-based shortcut models are combined with rigorous superstructure optimization to determine promising solvent candidates. The approach allows for an evaluation of several thousand potential solvents and identifies highly promising solvents based on the evaluation of process economics for an optimized process. The approach is illustrated for the extraction of γ-valerolactone from an aqueous feed stream.Publicationtype: Conference Paper48 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Analyzing the link between GE-model parameter regression and optimal process designThe importance of accurate thermodynamic models, capable of describing vapor-liquid and liquid-liquid equilibria, is generally acknowledged for the design of chemical processes. However, the parameterization of thermodynamic models and the development of chemical processes are usually treated as two different disciplines. More importantly, the objectives of each discipline do not necessary align. While the quality of a thermodynamic model is often judged purely on the basis of its average mean deviation from experimental data, process design aims at minimizing the overall process costs, assuming that the underlying thermodynamic models (especially model parameters) are reliable/accurate. Assessing the uncertainty of the thermodynamic model parameters is the link between both disciplines. However, this link is rarely established, and even the effect of the consideration of uncertainties in parameter regression by means of a deterministic process design is rarely investigated. Within this work this effect was taken into account for vapor-liquid equilibrium modeling and optimal design of a distillation column. The results indicate the necessity of a consistent consideration of uncertainties both during parameter regression and process optimization. Furthermore, the results highlight that a special treatment of experimental data and uncertainty information is required for specific applications (e.g. distillation of tangent pinch systems) in order to obtain reliable results in process design.Publicationtype: Conference Paper39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Improved modeling of membrane separation in integrated hybrid processesMembrane processes reportedly provide a tremendous potential for improving separation performance and energy efficiency compared to thermal separation processes (Sholl and Lively, 2016). However, the benefits of membrane processes, as e.g. the ability to overcome limitations of other separation techniques introduced by azeotropes or eutectic points, are exploited best when integrated in hybrid processes (Skiborowski et al., 2013). Nevertheless, industrial applications are still limited due to a lack of reliable models that allow for an accurate description of the separation performance of the membrane. Process design approaches oftentimes neglect limiting effects, such as pressure drop, or concentration and temperature polarization. However, the impact of ignoring these limitations was recently demonstrated by Micovic et al. (Micovic et al., 2014) for modeling organic solvent nanofiltration. Additionally, the correct consideration of the flow pattern, such as co- and counter-current as well as cross flow, can have an equally significant impact on the accuracy of the model. However, the consideration of cross flow requires a two dimensional discretization resulting in a more complicated model compared to co- or counter-current flow. In order to consider all these complex effects in a simultaneous process optimization an efficient model reduction without sacrificing model accuracy has to be performed. In this respect, Skiborowski et al. (Skiborowski, 2014) proposed to model a pervaporation-assisted distillation process, using an orthogonal collocation on finite elements (OCFE) for accurately modeling a co-currently operated membrane module, while applying external functions for computing thermodynamic properties. However, only temperature polarization was considered. The current work extends this approach by implementing concentration polarization and pressure drop computations. Furthermore, modifications of the OCFE approach towards the alternative flow patterns are investigated and comparisons with simple discretization schemes are presented. The results indicate the benefits of the efficient model reduction by OCFE in order to allow for the consideration of more accurate modeling of the membrane process.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards predictive models for organic solvent nanofiltrationOrganic solvent nanofiltration (OSN) is a promising technology for an energy-efficient separation of organic mixtures. However, due to the lack of suitable models that allow for a quantitative prediction of the separation performance in different chemical systems OSN is rarely considered during conceptual process design. The feasibility of OSN is usually determined by means of an experimental screening of different membranes. Further experiments are conducted for a selected membrane in order to determine membrane specific parameters for a model-based description of the separation performance for a specific mixture. Obviously, this classical approach is experimentally demanding. The effort in identifying a suitable membrane in the first step could be significantly reduced if a theoretical evaluation of the separation performance was possible. The current article proposes an automatic method for the determination of a suitable predictive model for a given membrane, taking into account a limited set of experimental data. Specially, the rejection of different solutes in a specific solvent is modeled based on a set of physical and chemical descriptors. The proposed approach is based on a combination of genetic programming and global deterministic optimization, allowing for the identification of innovative models, including nonlinear parameter regression. The predictive capability of the generated models is validated on a separate data set. The identified models were able to predict the rejection of different components in the considered case studies with a deviation from the experimental values below 5%.Publicationtype: Conference Paper33