01825nas a2200241   4500008004100000022001400041245010300055210006900158260006700227300001200294490000600306520090900312653001301221653002201234653004201256653003001298653001701328653004901345100001801394700001901412700002101431856013101452       2013                        eng d  a1932-213500aDesign of decanter in heterogeneous azeotropic distillation column by minimizing Gibbs free energy0 aDesign of decanter in heterogeneous azeotropic distillation colu  a111 RIVER ST, HOBOKEN 07030-5774, NJ USAbWILEY-BLACKWELLcNOV  a843-8480 v83 a<p>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 &amp; Sons, Ltd.</p>10adecanter10aGibbs free energy10aheterogeneous azeotropic distillation10aliquid-liquid equilibrium10aOptimization10arepulsive particle swarm optimization method1 aRahman, Imran1 aUbaidullah, S.1 aDas, Anwesh, Kr.  uhttps://library.ncl.res.in/content/design-decanter-heterogeneous-azeotropic-distillation-column-minimizing-gibbs-free-energy-001454nas a2200157   4500008004100000245006500041210006500106260000800171300001000179490000800189520094300197100002201140700002001162700001801182856009601200       2018                        eng d00aDeep learning based data driven soft sensor for bioprocesses0 aDeep learning based data driven soft sensor for bioprocesses  cAUG  a28-390 v1363 a<p>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.</p>1 aGopakumar, Vineet1 aTiwari, Sarthak1 aRahman, Imran  uhttps://library.ncl.res.in/content/deep-learning-based-data-driven-soft-sensor-bioprocesses