Design of decanter in heterogeneous azeotropic distillation column by minimizing Gibbs free energy - Rahman, Imran; Ubaidullah, S.; Das, Anwesh Kr. (Asia-Pacific Journal of Chemical Engineering, 2013)
In this paper, liquid-liquid equilibrium in decanter of heterogeneous azeotropic distillation is solved by minimizing Gibbs free energy, by using global optimization technique. The azeotropic distillation column is modeled by solving material balance, equilibrium and summation equations, and repulsive particle swarm optimization (RPSO), a stochastic global optimization formalism, is employed to predict stable steady state solution in decanter. To verify the performance of the RPSO algorithm, it is compared with equation solving method for liquid-liquid equilibria by considering two azeotropic systems, namely (1) benzene-ethanol-water and (2) furfural-water. The proposed methodology shows feasibility of the RPSO algorithm in predicting liquid-liquid equilibrium in decanter of heterogeneous azeotropic distillation. (c) 2013 Curtin University of Technology and John Wiley & Sons, Ltd.
Keywords: decanter; Gibbs free energy; heterogeneous azeotropic distillation; liquid-liquid equilibrium; Optimization; repulsive particle swarm optimization method

Deep learning based data driven soft sensor for bioprocesses - Gopakumar, Vineet; Tiwari, Sarthak; Rahman, Imran (Biochemical Engineering Journal, 2018)
Developing accurate and robust sensors for nonlinear and highly varying systems is a challenge. Deep learning, an advanced technique to learn deep architectures, has become a popular training strategy while dealing with complex problems. In this paper, deep learning has been introduced to develop data driven soft sensors for estimating crucial parameters in two fermentation processes, namely, Streptokinase and Penicillin. Additionally, the performance of the developed soft sensor is compared to an SVR based soft sensor. The results clearly indicate that deep learning is an attractive alternative to traditional techniques for soft sensor modelling as it represents nonlinear systems better, makes full advantage of process data by also incorporating unlabelled data and handles large datasets efficiently. Deep learning proves to be a promising technique for soft sensor modelling in highly data driven complex bioprocesses.