A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process

Soft sensors are becoming increasingly important in our world today as tools for inferring difficult-to-measure process variables to achieve good operational performance and economic benefits. Recent advancement in machine learning provides an opportunity to integrate machine learning models for soft sensing applications, such as Least Square Support Vector Regression (LSSVR) which copes well with nonlinear process data. However, the LSSVR model usually uses the radial basis function (RBF) kernel function for prediction, which has demonstrated its usefulness in numerous applications. Thus, this study extends the use of non-conventional kernel functions in the LSSVR model with a comparative study against widely used partial least square (PLS) and principal component regression (PCR) models, measured with root mean square error (RMSE), mean absolute error (MAE) and error of approximation (Ea) as the performance benchmark. Based on the empirical result from the case study of the penicillin fermentation process, the Ea of the multiquadric kernel (MQ) is lowered by 63.44% as compared to the RBF kernel for the prediction of penicillin concentration. Hence, the MQ kernel LSSVR has outperformed the RBF kernel LSSVR. The study serves as empirical evidence of LSSVR performance as a machine learning model in soft sensing applications and as reference material for further development of non-conventional kernels in LSSVR-based models because many other functions can be used as well in the hope to increase the prediction accuracy

Comparison of Turbulence Models for the Prediction of Hydrodynamics of Packed Beds under Supercritical Fluid Condition

The rate of transfer processes in packed bed is governed by the hydrodynamics of the bed. Therefore, the study of fluid flow within the bed using suitable turbulence model is crucial. This comparative study of four turbulence models (standard k-ε, RNG k-ε, realizable k¬-ε and SST k¬-ω) was performed for subcritical and supercritical regimes. The results of the predicted hydrodynamics were presented in detail and SST k¬-ω turbulence model is recommended for single sphere simulation

A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process

Soft sensors are becoming increasingly important in our world today as tools for inferring difficult-to-measure process variables to achieve good operational performance and economic benefits. Recent advancement in machine learning provides an opportunity to integrate machine learning models for soft sensing applications, such as Least Square Support Vector Regression (LSSVR) which copes well with nonlinear process data. However, the LSSVR model usually uses the radial basis function (RBF) kernel function for prediction, which has demonstrated its usefulness in numerous applications. Thus, this study extends the use of non-conventional kernel functions in the LSSVR model with a comparative study against widely used partial least square (PLS) and principal component regression (PCR) models, measured with root mean square error (RMSE), mean absolute error (MAE) and error of approximation (Ea) as the performance benchmark. Based on the empirical result from the case study of the penicillin fermentation process, the Ea of the multiquadric kernel (MQ) is lowered by 63.44% as compared to the RBF kernel for the prediction of penicillin concentration. Hence, the MQ kernel LSSVR has outperformed the RBF kernel LSSVR. The study serves as empirical evidence of LSSVR performance as a machine learning model in soft sensing applications and as reference material for further development of non-conventional kernels in LSSVR-based models because many other functions can be used as well in the hope to increase the prediction accuracy

Computational Fluid Dynamics-Based Hydrodynamics Studies in Packed Bed Columns: Current Status and Future Directions

A careful review of the literature reveals that extensive research has been done on the hydrodynamics in packed bed columns using turbulence models. It can be noted that the choice of turbulence model is influenced by the number of phases, type of fluid, Reynolds number range and the type of packing. Thus, comparison of turbulence models for the selection of a suitable model assumes great importance for the better prediction of flow pattern. This is due to the fact that poor prediction of the flow pattern can lead to a limited heat and mass transfer model as the rate of transfer processes in packed bed is governed by the hydrodynamics of the packed bed. The aim of this paper is to give a review of the computational fluid dynamics (CFD)-based hydrodynamics studies of packed bed columns with the primary interest of studying pressure drop and drag coefficient in packed beds. From the literature survey in Science Direct database, more than 48,000 papers related to packed bed columns have been published with more than 3,000 papers focused on the hydrodynamic studies of the bed to date. Unfortunately, there are only a few studies reported on the hydrodynamics of packed columns under supercritical fluid condition. Therefore, it is imperative that the future work has to focus on the hydrodynamics of supercritical packed column and particularly on the selection of suitable turbulence model