In this work, DAE Tools modelling, simulation and optimisation software, its programming paradigms and main features are presented. The current approaches to mathematical modelling such as the use of modelling languages and general-purpose programming languages are analysed. The common set of capabilities required by the typical simulation software are discussed, and the shortcomings of the current approaches recognised. A new hybrid approach is introduced, and the modelling languages and the hybrid approach are compared in terms of the grammar, compiler, parser and interpreter requirements, maintainability and portability. The most important characteristics of the new approach are discussed, such as: (1) support for the runtime model generation; (2) support for the runtime simulation set-up; (3) support for complex runtime operating procedures; (4) interoperability with the third party software packages (i.e. NumPy/SciPy); (5) suitability for embedding and use as a web application or software as a service; and (6) code-generation, model exchange and co-simulation capabilities. The benefits of an equation-based approach to modelling, implemented in a fourth generation object-oriented general purpose programming language such as Python are discussed. The architecture and the software implementation details as well as the type of problems that can be solved using DAE Tools software are described. Finally, some applications of the software at different levels of abstraction are presented, and its embedding capabilities and suitability for use as a software as a service is demonstrated. Subjects Scientific Computing and Simulation

This work presents a generic modeling framework for the separation of gas mixtures using multibed pressure swing adsorption (PSA) processes. Salient features of the model include various mass, heat, and momentum transport mechanisms, gas valve models, complex boundary conditions, and realistic operating procedures. All models have been implemented in the gPROMS modeling environment and a formal and user-friendly automatic procedure for generating multibed configurations of arbitrary complexity has been developed. The predictive power of the developed modeling framework for various PSA multibed configurations has been validated against literature and experimental data. The effect of various operating conditions on the product purity and recovery is systematically analyzed. Typical trade offs between capital and operating costs are revealed.

This work presents an optimization framework for complex pressure swing adsorption (PSA) processes including multibed configurations and multilayered adsorbents. The number of beds, PSA cycle configuration, and various operating and design parameters have been systematically optimized using recent advances on process optimization. The Unibed principle has been adopted relying on the simulation over times of only one bed while storage buffers have been used to model bed interactions. A novel state transition network (STN) representation is employed for the efficient simulation and optimization of the processes. Two largescale multicomponent separation processes have been used to illustrate the applicability and potential of the proposed approach in terms of improvement of product purity and recovery. Results indicate that significant improvements can be achieved over base case designs.

No abstract