Application of Mathematical Modeling, Optimization, Artificial Intelligence in Chemical Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1665

Special Issue Editors

Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, "Gheorghe Asachi" Technical University of Iasi, Bd. Dimitrie Mangeron, nr. 67, 700050 Iaşi, România
Interests: modelling; simulation; analysis; optimization; machine learning; bio-inspired metaheuristics
Special Issues, Collections and Topics in MDPI journals
Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, "Gheorghe Asachi" Technical University of Iasi, Bd. Dimitrie Mangeron, nr. 67, 700050 Iaşi, România
Interests: chemical reactor modeling and design; transfer phenomena; catalysis; photocatalysis; sonocatalysis; unit operations of particulate solids

Special Issue Information

Dear Colleagues,

The relevance of classical mathematics has not diminished, although it has changed little over the years. In addition, new mathematical applications in chemical engineering are constantly emerging, particularly in process modeling and design, process optimization, and control. The field of chemical engineering will continue to rely heavily on mathematics as it develops and expands.

Thus, this Special Issue aims to provide a platform for the dissemination of works focusing on the expansion of knowledge (gained via experimental analysis or simulation) through different modeling, optimization, and/or control strategies. Areas of interest include, but are not limited to:

  • Statistical analysis for modeling and simulating chemical processes;
  • Phenomenological models (focusing, for example, on the ordinary differential equation, calculations of finite differences or approximate solutions);
  • Computing and artificial intelligence approaches (focusing, for instance, on deep learning or metaheuristics).

We welcome studies of both a theoretical and an empirical nature.

Dr. Elena Niculina Dragoi
Dr. Mircea Teodor Nechita
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • modeling
  • optimization
  • control
  • statistics
  • artificial intelligence
  • deep learning
  • metaheuristics

Published Papers (1 paper)

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Research

26 pages, 18006 KiB  
Article
Stochastic Levenberg–Marquardt Neural Network Implementation for Analyzing the Convective Heat Transfer in a Wavy Fin
by R. S. Varun Kumar, M. D. Alsulami, I. E. Sarris, G. Sowmya and Fehmi Gamaoun
Mathematics 2023, 11(10), 2401; https://doi.org/10.3390/math11102401 - 22 May 2023
Cited by 12 | Viewed by 1251
Abstract
The present research examines the steady, one-dimensional thermal distribution and heat transfer of a wavy fin. This heat transfer analysis considers convective effects as well as temperature-dependent thermal conductivity. Furthermore, a novel implementation of a neural network with backpropagated Levenberg–Marquardt algorithm (NN-BLMA)-based machine [...] Read more.
The present research examines the steady, one-dimensional thermal distribution and heat transfer of a wavy fin. This heat transfer analysis considers convective effects as well as temperature-dependent thermal conductivity. Furthermore, a novel implementation of a neural network with backpropagated Levenberg–Marquardt algorithm (NN-BLMA)-based machine learning intelligent strategies is provided to interpret the heat transfer analysis of a convective wavy fin. The non-linear ordinary differential equation (ODE) of the study problem is converted into its non-dimensional form using the similarity transformation technique. The dimensionless equation obtained is then numerically explored via the Runge–Kutta–Fehlberg scheme. A data set for varying the pertinent parameters is generated, and an artificial neural network model is designed to estimate the heat transfer behavior of the wavy fin. The effectiveness of the proposed NN-BLMA is subsequently endorsed by analyses using a regression model, mean square error, and histograms. The findings of comprehensive computational parametric studies illustrate that the presented technique, NN-BLMA is an effective convergent stochastic numerical solver employed for the heat transfer model of the convective wavy fin. The wavy fin’s temperature dispersion optimizes as the thermal conductivity parameter rises. Heat transfer rate is higher in wavy fin compared to rectangular fin. Full article
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