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Mathematics
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Mathematical Modeling and Optimization of Process Industries
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Mathematical Modeling and Optimization of Process Industries

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 2283

Special Issue Editors

Prof. Dr. Feng Qian

E-Mail Website
Guest Editor
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: process control; industrial intelligence; process industry; plant-wide optimization
Prof. Dr. Weimin Zhong

E-Mail Website
Guest Editor
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: process control; industrial intelligence; process industry; plant-wide optimization
Prof. Dr. Jingyi Lu

E-Mail Website
Guest Editor
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: process control; plant-wide optimization; learning-based control; model predictive control

Special Issue Information

Dear Colleagues,

The process manufacturing industry refers to elementary raw material industries, including those of petroleum, chemicals, steel, nonferrous metals, and building materials. With the tremendous progress in mathematics and information technologies, smart manufacturing has become the core technology in the transformation and upgrading of process industries. In order to achieve the goals of greenization and low-carbon, high-value, and high-end digitalization and intellectualization in process industries, it is necessary to develop new approaches of mathematical modeling and optimization for process industries. Hence, this Special Issue aims to investigate the mathematical modeling and optimization of process industries from the perspective of applications. All related original research that contributes to the mathematical modeling and optimization theory of process industries, along with their applications, is particularly welcome and encouraged.

Prof. Dr. Feng Qian
Prof. Dr. Weimin Zhong
Prof. Dr. Jingyi Lu
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

  • mathematical modeling
  • data-driven modeling
  • planning and scheduling
  • robust optimization
  • Bayes optimization
  • distributed optimization
  • process industry
  • industrial intelligence
  • reinforcement learning
  • few-shot/zero-shot learning
  • carbon peak and carbon neutrality
  • digitalization
  • risk early warning
  • knowledge graph

Published Papers (2 papers)

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Research

11 pages, 282 KiB  
Article
Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations
by Yahia Abdel-Aty, Mohamed Kayid and Ghadah Alomani
Mathematics 2023, 11(9), 2190; https://doi.org/10.3390/math11092190 - 06 May 2023
Cited by 2 | Viewed by 879
Abstract
Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II exponential populations. Squared [...] Read more.
Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II exponential populations. Squared error, Linex, and general entropy loss functions are used in the Bayesian approach. Monte Carlo simulations were performed to assess how well the different approaches perform. The simulation study compares the Bayesian estimators for different values of the learning rate parameter and different losses. Full article
(This article belongs to the Special Issue Mathematical Modeling and Optimization of Process Industries)
15 pages, 2922 KiB  
Article
Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process
by Qiang Li, Chang Liu and Qingxin Guo
Mathematics 2022, 10(15), 2659; https://doi.org/10.3390/math10152659 - 28 Jul 2022
Cited by 2 | Viewed by 1030
Abstract
In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, [...] Read more.
In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios. Full article
(This article belongs to the Special Issue Mathematical Modeling and Optimization of Process Industries)
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