Robust prediction and optimization of gasoline quality using data-driven adaptive modeling for a light naphtha isomerization reactor

Abdolkarimi, Vahid; Sari, Ataallah; Shokri, Saeed

doi:10.1016/j.fuel.2022.125304

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…Tables 4 and 5 show the comparison of the results of the LSTM network models presented above with MLP, SVM, and dynamic polynomial models (FIR, ARX, OE, NARX, and HW). [35] 0.974 0.949 0.179 0.141 SVM [36] 0.988 0.977 0.118 0.077 FIR [38] 0.948 0.878 0.266 0.213 ARX [38] 0.963 0.925 0.209 0.163 OE [38] 0.965 0.928 0.204 0.152 NARX [38] 0.965 0.927 0.206 0.163 HW [38] 0.967 0.934 0.196 0.149 [37] 0.982 0.965 0.069 0.045 FIR [37] 0.942 0.883 0.105 0.083 ARX [37] 0.983 0.965 0.058 0.049 OE [37] 0.989 0.977 0.046 0.037 NARX [37] 0.985 0.969 0.054 0.046 HW [37] 0.995 0.989 0.033 0.026…”

Section: Resultsmentioning

confidence: 99%

“…As for the isomerization process, several papers have been published recently on the subject of mathematically modeling this process. The authors have presented the development and optimization of the kinetic model of catalytic reactions [36], the development of the MLP-based model [37], and the development of the SVM-based model [38] for estimating the quality of isomerate gasoline. One of the first papers dealing with the development of data-driven mathematical models for the estimation of product quality in an isomerization process is the work by Lukec et al in 2007 [39].…”

Section: Introductionmentioning

confidence: 99%

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Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Herceg,

Ujević Andrijić,

Rimac

et al. 2023

Mathematics

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Dynamic neural networks (DNNs) are a type of artificial neural network (ANN) designed to work with sequential data where context in time is important. Unlike traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if the dynamic of a certain process is emphasized. They are commonly used in natural language processing, speech recognition, and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial isomerization process, it is crucial to measure the quality attributes that affect the octane number of gasoline. Process analyzers commonly used for this purpose are expensive and subject to failure. Therefore, to achieve continuous production in the event of a malfunction, mathematical models for estimating product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNNs), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM), and dynamic polynomial models. The obtained results are satisfactory, suggesting a good possibility of application.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Herceg,

Ujević Andrijić,

Rimac

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Within the past ten years, few studies have been conducted on kinetic models for the isomerization of n-alkanes [12][13][14][15][16][17][18][19][20][21]. The type of feedstock and operating conditions of the most significant studies are reported in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Modeling of Light Naphtha Hydroisomerization in an Industrial Universal Oil Products Penex™ Unit

Hamied¹,

Shakor²,

Sadeiq³

et al. 2023

Energy Engineering

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Recently, the isomerization of light naphtha has been increasingly significant in assisting refiners in meeting sternness specifications for gasoline. Isomerization process provides refiners with the advantage of reducing sulfur, olefin, and benzene in the gasoline basin without significantly victimizing the octane. The mathematical modeling of a chemical reaction is a critical tool due to it can used to optimize the experimental data to estimate the optimum operating conditions for industrial reactors. This paper describes light naphtha isomerization reactions over a Pt/Al 2 O 3 -Cl catalyst at the Al-Dura Oil Refinery (Baghdad, Iraq) using a newly developed universal mathematical model. The proposed kinetic model involves 117 isomerization reactions and 90 cracking reactions to describe 52 real components graded from methane to n-octane. A Genetic Algorithm stochastic optimization technique applied in MATLAB R2020a software was employed to estimate the optimal set of kinetic parameters. The calculated activation energies for hydrocracking reactions was found to be higher than the other reactions because of hydrocracking reactions occur at higher range of temperatures. By benchmarking between the experimental and theoretical results for all 117 data sets, the mean absolute error was obtained to be 0.00360 for all 52 components. Also, a positive effect of increasing reaction temperatures was recognized on enhancing the research octane number (RON).

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Optimized energy efficient reactive distillation for octane upgrading with economic and environmental considerations

Muhammed

2024

Fuel

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Robust prediction and optimization of gasoline quality using data-driven adaptive modeling for a light naphtha isomerization reactor

Cited by 6 publications

References 31 publications

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Kinetic Modeling of Light Naphtha Hydroisomerization in an Industrial Universal Oil Products Penex™ Unit

Optimized energy efficient reactive distillation for octane upgrading with economic and environmental considerations

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