Parallel based support vector regression for empirical modeling of nonlinear chemical process systems

In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data

Pressure modification index based on hydrodynamics and mass transfer effects for modeling of CO2 removal from natural gas via absorption at high pressures

In this paper, experimental works involving high concentration CO2 removal at elevated pressures are conducted using a high pressure CO2 pilot plant and the result is used to validate a simulation model based on established models in the literature. A rate based non-equilibrium model using 20 wt% aqueous monoethanolamine (MEA) is developed based on the work of Pandya (1983). The model considers reaction kinetics, mass transfer rate and heat transfer. Since the condition of CO2 removal at atmospheric and high pressure are different, a pressure modification index is proposed and incorporated in the mass transfer flux equation to account for the non-idealities. Comparative study involving the modified model with index-f, original rate-based non-equilibrium model, Aspen Plus equilibrium and non-equilibrium models has also been carried out for the CO2 loading at the top column exit of 1.505 m. It is found that the introduction of the proposed pressure modification index together with proper selection of mass transfer and effective interfacial area correlations results in an improvement in the average error from more than 100% to as low as 18% between the estimated and the pilot plant data