ANN for the prediction of isobutylene dimerization through catalytic distillation for a preliminary energy and environmental evaluation

Abstract

This study aimed to develop an artificial neural network (ANN) capable of predicting the molar concentration of diisobutylene (DIB), 3, 4, 4-trimethyl-1-pentene (DIM), and tert-butyl alcohol (TBA) in the distillate and residue streams within three specific columns: reactive (CDC), high pressure (ADC), and low pressure (TDC). The process simulation was conducted using DWSIM, an open-source platform. Following its validation, a sensitivity analysis was performed to identify the operational variables that influenced the molar fraction of DIB, DIM, and TBA in the outputs of the three columns. The input variables included the molar fraction of isobutylene (IB) and 2-butene (2-Bu) in the butane (C4) feed, the temperature of the C4 and TBA feeds, and the operating pressure of the CDC, ADC, and TDC columns. The network's design, training, validation, and testing were performed in MATLAB using the Neural FittinG app. The network structure was based on the Bayesian regularization (BR) algorithm, that consisted of 7 inputs and seven outputs with 30 neurons in the hidden layer. The designed, trained, and validated ANN demonstrated a high performance, with a mean squared error (MSE) of 0.0008 and a linear regression coefficient (R) of 0.9946. The statistical validation using an analysis of variance (ANOVA) (p-value > 0.05) supported the ANN's capability to reliably predict molar fractions. Future research will focus on the in-situ validation of the predictions and explore hybrid technologies for energy and environmental optimization in the process.

Keywords

• ann
• diisobutylene
• dwsim
• distillation
• tert-butyl alcohol