Unified correlation for overall gas hold‐up in bubble column reactors for various gas–liquid systems using hybrid genetic algorithm‐support vector regression technique

The objective of this study is to collect the data on overall gas hold‐up (∈_G) for bubble column reactors handling various gas–liquid systems and further develop a unified data‐driven model for the estimation of the same. In this work, around 3300 experimental points for ∈_G have been collected from 85 open sources spanning the years 1963–2008. The data‐driven model for overall gas hold‐up has been established using hybrid Genetic Algorithm‐Support Vector Regression (GA‐SVR)‐based methodology. In the present study, GA has been used for nonlinear rescaling of the parameters. These exponentially scaled parameters are subsequently subjected for SVR training. The technique is an extension of conventional SVR technique, showing relatively enhanced results. The proposed hybrid model is based on various prominent design and operating parameters (15 in number) which includes superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, ionic strength of liquid, operating temperature, operating pressure, liquid height, and the column diameter. The estimations made by the SVR‐based unified model for ∈_G shows an excellent agreement with actual values with estimation accuracy of 98.5% and % AARE of 9.32%. For ease in applicability and ready reference of the practicing engineers, the hybrid GA‐SVR‐based model in the form of software and the entire database for ∈_G has been uploaded on the link http://www.esnips.com/web/UICT‐NCL .