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Consider reactive separations

100%

Kołodziej A. , Jaroszyński M. , Janus B. , Kleszcz T.

2009/2010

tom No. 7

11-20

Reactive separation (RS) is the combination of a chemical (catalytic) reaction and a separation technique. For reactive distillation (RD), the reaction is combined with distillation. Rapid development of RS is the result of growing environmental demands and increasing energy costs. Several processes are performed using RD, such as esterification, etherification (fuel antiknocks), and hydrodesulphurisation. Other RS techniques also develop rapidly. However, the success has not come without some serious problems. The heterogeneous proton catalysts still do not meet the requirements of many processes. The column internals with a built-in catalyst are rather sparse and expensive and their characteristics are not studied well enough. Therefore intensive studies are still being performed. This study presents a brief summary of the processes realisable with the usage of reactive distillation, catalysts, and column internals. Some interesting research results and application examples are also quoted.

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

88%

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

tom 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.

Simulation and sensitivity analysis for biodiesel production in a reactive distillation column

75%

Agarwal M. , Singh K. , Chaurasia S. P.

tom Vol. 14, nr 3

59-65

The conventional process for biodiesel production by transesterification is still expensive due to a need of high excess of alcohol required and its recovery by distillation. The use of a reactive distillation process can reduce the amount of alcohol in the feed stream as it works on a simultaneous reaction and separation. In the present study, a mathematical model has been developed for biodiesel production from triglycerides in a reactive distillation column, which has been validated with the reported data and CHEMCAD results. The effects of process parameters such as methanol to oil feed ratio, feed temperature, and reaction time have been investigated. The sensitivity analysis shows that yield of ester increases with methanol to oil ratio and number of stages, however, it decreases with fl ow rate. The MATLAB simulated results show that methanol to oil molar ratio of 5:1 produces 90% (by wt.) of methyl ester in a residence time of 4.7 minutes.