Abstract-In this paper we have used the heuristic search algorithm for the process optimization of Reactive Distillation column. Basically, Process optimization is the manipulation of process variables, so as to optimize some of the parameters without violating the constraints. Gravitational Search Algorithm (GSA) is a new heuristic optimization technique based on law of gravity and mass interactions. This technique is used for process optimization of Methyl-Tert-Butyl-Ether (MTBE) reactive distillation. This work highlights the potential of GSA for an optimization of MTBE reactive distillation that involves complex reaction system. The results obtained gives better performance of MTBE reactive distillation.

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Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Chemical process industries deal with production which further utilizes reaction followed by separation of the reaction mixtures. Reactive distillation is a new technique of combination of both reaction and separation in a single unit beneficial for equilibrium-limited reactions and also cost-effective. This makes it a highly complex process because many parameters involved in both reaction and separation are interactive in nature. In this chapter, modeling, simulation, and optimization of reactive distillation are presented. Methyl acetate production via reactive distillation is chosen as a case study. The results are compared for both experimental and simulation studies. The synthesis of methyl acetate was carried out in a packed RDC by catalytic esterification using acetic acid and methanol as reactants in a pilot-scale experimental setup. A strong acidic ion exchange catalyst, Amberlyst-15, was used to enhance the rate of heterogeneous esterification reaction. The result obtained was observed with change in various variables including the reflux ratio (RR), distillateto-feed (D/F) ratio, and bottom-to-feed (B/F) ratio with respect to product composition. The optimization and sensitivity analysis was carried out using Aspen Plus process simulation software.

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 modifi ed using the steepest descent method. This modifi cation 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 profi le 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 modifi ed 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.