Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation

Isopropyl myristate (IPM) is an important chemical in the cosmetic and pharmaceutical industries. The IPM can be produced either through esterification or the transesterification process in semibatch reactive distillation (BRD). However, the latter process is not widely explored. The transesterification process in BRD can be represented by a mathematical model, however, this model will end with a large number of differential equations and be very expensive to solve and will also be time consuming. Hence, the empirical model such as the artificial neural network (ANN) model provides better solution as it can deal with highly nonlinear and complex structures. In this work, the production of industrial scaled IPM in BRD through the transesterification process is simulated using Aspen Plus and the simulation result achieved shows a comparable result as reported in the literature. The validated model is then used for sensitivity analysis to determine the relationship between the process input-output variables. The nonparametric test is used and the selected inputs are ranked according to their mean overall sensitivity. From the results, the reboiler duty, the initial mole of isopropanol, methyl mysistate, the reflux ratio, the feed flowrate and the temperature at stage 32 are considered as the input variables in the ANN model development to predict the bottom and distillate composition

Feed Forward Neural Network Model for Isopropyl Myristate Production in Industrial-scale Semi-batch Reactive Distillation Columns

The application of the artificial neural network (ANN) model in chemical industries has grown due to its ability to solve complex model and online application problems. Typically, the ANN model is good at predicting data within the training range but is limited when predicting extrapolated data. Thus, in this paper, selected optimum multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) models are used to predict the bottom (xb) compositions of extrapolated data. The MIMO and MISO models both managed to predict the extrapolated data with MSE values of 0.0078 and 0.0063 and with R2 values of 0.9986 and 0.9975, respectively

Electrochemical Removal of Copper Ion Using Coconut Shell Activated Carbon

In this work, coconut shell activated carbon (CSAC) electrode was evaluated to remove copper ion via electrochemical processes. CSAC electrode and graphite were applied as the cathode and the anode, respectively. The reusability of the electrode, the effects of initial pH, applied voltage and initial concentration were studied. The electrochemical process was carried out for 3 h of treatment time, and the electrodes (anode and cathode) were separated by 1 cm. The results revealed that CSAC is proven as a reusable electrode to remove copper ion, up to 99% of removal efficiency from an initial concentration of 50 ppm after it had been used three times. From the observation, the removal efficiency was optimum at an initial pH of 4.33 (without any initial pH adjustment). The applied voltage at 8 V showed a higher removal efficiency of copper ion compared to at 5 V