Development of a soft sensor for a batch distillation column using support vector regression techniques - Jain, P.; Rahman, I.; Kulkarni, B. D. (Chemical Engineering Research & Design, 2007)
A support vector regression (SVR)-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Kernel function such as linear, polynomial and RBF are employed for SVR modelling. The original process data was generated by simulating the batch distillation process, varying the initial feed composition and boilup rate from batch to batch. Within each batch reflux ratio was also randomly changed to represent the true dynamics of the batch distillation. The results show the potential of the method for developing softsensor for chemical processes.
Keywords: Batch distillation; composition estimation; soft sensor; support vector regression

Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems - Gandhi, Ankit B.; Joshi, Jyeshtharaj B.; Jayaraman, Valadi K.; Kulkarni, Bhaskar D. (Chemical Engineering Science, 2007)
The objective of this study was to develop a unified data-driven correlation for the overall gas hold-up for various gas-liquid systems using support vector regression (SVR)-based modeling technique. Over the years, researchers have amply quantified the hydrodynamics of bubble column reactors in terms of the overall gas hold-up. In this work, about 1810 experimental points were collected from 40 open sources spanning the years 1965-2007. The model for overall gas hold-up was established as a function of several parameters which include 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, operating temperature, operating pressure and column diameter of the gas-liquid system. For understanding the hold-up behavior, the data used for training the model was grouped into various gas-liquid systems viz., air-water, gas-aqueous viscous liquids, gas-organic liquids, gas-aqueous electrolyte solutions and gas-liquid systems operated over a wide range of pressure. A generalized model established using SVR was evaluated for its performance for various gas-liquid systems. Statistical analysis showed that the proposed generalized SVR-based correlation for overall gas hold-up has prediction accuracy of 97% with average absolute relative error (% AARE) of 12.11%. A comparison of this correlation with the selected system specific correlations in the literature showed that the developed SVR-based correlation significantly gives enhanced prediction of overall gas hold-up. (C) 2007 Published by Elsevier Ltd.
Keywords: bubble column reactor; overall gas hold-up; support vector regression

High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms - Patil-Shinde, Veena; Saha, Sujan; Sharma, Bijay K.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (Chemical Engineering Communications, 2016)
The coal gasification is a cleaner and more efficient process than the coal combustion. Although high ash coals are commonly utilized in the energy generation, systematic gasification kinetic studies using chars derived from these coals are scarce. Accordingly, this paper reports the development of the data-driven models for the gasification of chars derived from the high ash coals. Specifically, the models predict two important gasification performance parameters, viz. gasification rate constant and reactivity index. These models have been constructed using three computational intelligence (CI) methods, namely genetic programming (GP), multilayer perceptron (MLP) neural network (NN), and support vector regression (SVR). The inputs to the CI-based models consist of seven parameters representing the gasification reaction conditions and properties of high ash coals and chars. The data used in the modeling were collected by performing extensive gasification experiments in the CO2 atmosphere in a thermo-gravimetric analyzer (TGA) using char samples derived from the Indian coals containing high ash content. Values of the two gasification performance parameters were obtained by fitting the experimental data to the shrinking unreacted core (SUC) model. It has been observed that all the CI-based models possess an excellent prediction accuracy and generalization capability. Accordingly, these models can be gainfully employed in the design and operation of the fixed and fluidized bed gasifiers using high ash coals.
Keywords: Char gasification kinetic modeling; Data-driven modeling; genetic programming; Multilayer perceptron neural network; support vector regression; Thermo-gravimetric analyzer

Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling - Tiwary, Shishir; Ghugare, Suhas B.; Chavan, Prakash D.; Saha, Sujan; Datta, Sudipta; Sahu, Gajanan; Tambe, Sanjeev S. (Waste and Biomass Valorization, 2018)
Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and biomass for generating the product gas; it is environment-friendly since it emits lesser quantities of pollutants compared to the coal gasification process. Although coals found in many countries contain high percentages of ash, co-gasification studies involving such coals, and the process modeling thereof, are rare. Accordingly, this study presents results of the co-gasification experiments conducted in a fluidized-bed gasifier (FBG) pilot plant using as a feed the blends of high ash Indian coals with three biomasses, namely, rice husk, press mud, and sawdust. Since the underlying physicochemical phenomena are complex and nonlinear, modeling of the COG process has been performed using three computational intelligence (CI)-based methods namely, genetic programming, artificial neural networks, and support vector regression. Each of these formalisms was employed separately to develop models predicting four COG performance variables, namely, total gas yield, carbon conversion efficiency, heating value of product gas, and cold gas efficiency. All the CI-based models exhibit an excellent prediction accuracy and generalization performance. The co-gasification experiments and their modeling presented here for a pilot-plant FBG can be gainfully utilized in the efficient design and operation of the corresponding commercial scale co-gasifiers utilizing high ash coals.
Keywords: Artificial neural networks; Co-gasification; Computational intelligence; Fluidized bed gasifier; genetic programming; support vector regression