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Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO 2 /N 2 separation. A 5 times reduction in computational effort is observed. [ABSTRACT FROM AUTHOR]

Abstract: LNG liquefaction is an energy-intensive process. Energy efficiency is the major concern in LNG liquefaction process design and operation. In this paper, a new methodology for LNG liquefaction synthesis targeting energy consumption minimization is presented. It is based on thermodynamic analysis, mathematical programming, and rigorous simulation. The procedure for MINLP (mixed-integer non-linear programming) model development and simplification is described in detail. LINDOGlobal solver in GAMS is employed to solve the MINLP problem and the optimization results are further validated through rigorous simulations. Based on the development, the optimal design and operational conditions for LNG liquefaction process can be simultaneously achieved. A C3MR LNG liquefaction process is employed to demonstrate the efficacy of the developed methodology. Comprehensive thermodynamic analysis is also conducted to give insights of the optimization results. [Copyright &y& Elsevier]

Abstract: The design and optimization of a coupled multicomponent distillation system is a non-linear and multivariable problem. The complexity of this kind of problem results in high solving difficulty. This paper addresses the application of genetic algorithms to the optimization of intensified distillation systems for quaternary distillations. We used a multiobjective genetic algorithm with restrictions coupled to the Aspen Plus™ process simulator for the evaluation of the objective function. Several mixtures to test the effect of relative volatilities of feed mixtures on energy consumption, second law efficiency, total annual cost and theoretical control properties have been studied. Numerical performance shows that this design tool is robust and suitable for the design of coupled multicomponent distillation sequences. [Copyright &y& Elsevier]