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2 Polish Journal of Chemical Technology

2 2016

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Novel process integration for biodiesel blend in membrane reactive divided wall (MRDW) column

Sakhre V., Jain S., Sapkal V. S., Agarwal D. P.

Polish Journal of Chemical Technology

2016

Vol. 18, nr 1

105--112

The paper proposes a novel process integration for biodiesel blend in the Membrane assisted Reactive Divided Wall Distillation (MRDW) column. Biodiesel is a green fuel and grade of biodiesel blend is B20 (%) which consist of 20% biodiesel and rest 80% commercial diesel. Instead of commercial diesel, Tertiary Amyl Ethyl Ether (TAEE) was used as an environment friendly fuel for blending biodiesel. Biodiesel and TAEE were synthesized in a pilot scale reactive distillation column. Dual reactive distillation and MRDW were simulated using aspen plus. B20 (%) limit calculation was performed using feed flow rates of both TAEE and biodiesel. MRDW was compared with dual reactive distillation column and it was observed that MRDW is comparatively cost effective and suitable in terms of improved heat integration and flow pattern.

Modified neural network based cascaded control for product composition of reactive distillation

Sakhre V., Jain S., Sapkal V. S., Agarwal D. P.

Polish Journal of Chemical Technology

2016

Vol. 18, nr 2

111--121

In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.