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This work reports on the synthesis of a wide range of ferrocenyl-substituted amino acids and peptides in excellent yield. Conjugation is established via copper-catalyzed 1,3-dipolar cycloaddition. Two complementary strategies were employed for conjugation, one involving cycloaddition of amino acid derived azides with ethynyl ferrocene 1 and the other involves cycloaddition between amino acid derived alkynes with ferrocene-derived azides 2 and 3. Labeling of amino acids at multiple sites with ferrocene is discussed. A

The reaction kinetics of reversible liquid-phase esterification of acetic acid with methanol is investigated in the temperature range 26-50 • C using sulfuric acid catalyst. The main goal of this work is to study the effect of catalyst concentration and sensitivity to the presence of water on the rate expression of this industrially important reaction. Experiments are conducted in an isothermal batch reactor and a second-order kinetic model is used to correlate the experimental data, which are found to fit well with the assumed kinetic model in terms of the concentrations of reactants and products. Furthermore, an activity-based kinetic model is also developed employing the UNIQUAC (universal quasi-chemical equation) model to compute the activities. It is observed that the rate constant is influenced by the concentration of catalyst, and the reaction rate increased with an increase in the catalyst concentration. It is also observed that the catalyst activity is slightly inhibited by the water present in the reaction mixture. The performance of the proposed models is compared with that of other models reported in the literature, and it is found that the proposed models outperformed all the other models reported in the literature. C

Stochastic optimization algorithms such as genetic algorithm (GA) and simulated annealing (SA) are combined with a polynomial-type empirical process model to develop nonlinear model predictive control (NMPC) strategies, namely, GANMPC and SANMPC, in the perspective of control of a nonlinear reactive distillation column. In these strategies, the nonlinear input-output process model is cascaded itself to generate future predictions for the process output based on which the control sequence is computed by stochastic optimizers while satisfying the specified performance criteria. The performance of the proposed controllers is evaluated by applying to single input-single output (SISO) control of an ethyl acetate reactive distillation column with double-feed configuration involving an esterification reaction with azeotropism. The results demonstrate better performance of the stochastic optimization based NMPCs over a conventional proportional-integral (PI) controller, a linear model predictive controller (LMPC), and a NMPC based on sequential quadratic programming (SQP) in tracking the setpoint changes as well as stabilizing the operation in the presence of input disturbances. Although both the GANMPC and SANMPC are found to exhibit almost equal performance, the easier tuning and the lower computational effort suggests the better suitability of SANMPC for the control of a nonlinear reactive distillation column.

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