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

Title:
ANN for the prediction of isobutylene dimerization through catalytic distillation for a preliminary energy and environmental evaluation
Authors:
Daniel Chuquín-Vasco, Geancarlo Torres-Yanacallo, Cristina Calderón-Tapia, Juan Chuquin-Vasco, Nelson Chuquin-Vasco, Ramiro Cepeda-Godoy
Source:
AIMS Environmental Science, Vol 11, Iss 2, Pp 157-183 (2024)
Publisher Information:
American Institute of Mathematical Sciences (AIMS), 2024.
Publication Year:
2024
Subject Terms:
Artificial neural network, Mole fraction, Artificial intelligence, distillation, diisobutylene, 0211 other engineering and technologies, 02 engineering and technology, Water Network Synthesis, Process Fault Detection and Diagnosis in Industries, Analytical Chemistry (journal), Engineering, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, GE1-350, State-of-the-Art in Process Optimization under Uncertainty, Linear regression, dwsim, ann, Biology, Distillation, Chromatography, Statistics, tert-butyl alcohol, Computer science, Environmental sciences, Chemistry, Stability Analysis, Model Predictive Control in Industrial Processes, Physical chemistry, Control and Systems Engineering, Biological system, Physical Sciences, Mean squared error, Mathematics
Document Type:
Fachzeitschrift Article<br />Other literature type
ISSN:
2372-0352
DOI:
10.3934/environsci.2024009
DOI:
10.60692/fdqen-edj54
DOI:
10.60692/2wn12-c9r63
Access URL:
https://doaj.org/article/420c110bd7c2440bb40f7842ace90c33
Accession Number:
edsair.doi.dedup.....fba6eb417b0bf337b685320db28e22e8
Database:
OpenAIRE

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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.