Simulation and optimisation of vacuum (pressure) swing adsorption with simultaneous consideration of real vacuum pump data and bed fluidisation

•Vacuum pump performance models are developed using realistic manufacturing data.•New V(P)SA model is developed without estimated BCs and efficiency for vacuum pump.•Vacuum pump efficiency typically varies from 20 % to 40 %.•Real vacuum pump data and fluidisation constraints are incorporated simultaneously.•Integrating vacuum pump data and fluidisation limits affect optimal results notably. Pressure swing adsorption (PSA) is a promising technology for gas separation and purification, receiving considerable attention in the past decades. Existing studies on simulating PSA involving a vacuum pump, the vacuum (pressure) swing adsorption [V(P)SA] process, typically estimate vacuum pump energy consumption using an approximate constant energy efficiency or an empirical energy efficiency correlation, leading to inaccurate representation of realistic vacuum pump performance. In this work, we propose an enhanced computational approach for simulation and optimisation of V(P)SA processes through simultaneous incorporation of realistic vacuum pump performance prediction models and adsorption bed fluidisation limits. The vacuum pump performance prediction models are developed using data-driven modelling techniques with realistic vacuum pump performance data. The enhanced computational approach more accurately accounts for the V(P)SA performance, without relying on an estimated vacuum pump efficiency and assumed pressure/flow rate boundary conditions at the vacuum pump end of the adsorption bed. Furthermore, the impact of bed fluidisation on optimal PSA design is addressed. The computational results show that the developed prediction models accurately represent the actual performance curves of the vacuum pump. It is also demonstrated that incorporating the vacuum pump performance models and fluidisation constraints in PSA optimisation leads to significantly different optimal solutions compared to when these factors are not considered.