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JMMP
Special Issues
Sustainable Manufacturing for a Better Future
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Sustainable Manufacturing for a Better Future

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3069

Special Issue Editor

Dr. Jingchao Jiang

E-Mail Website
Guest Editor
Department of Engineering, Faculty of Environment, Science and Economy, The University of Exeter, Exeter EX4 4RJ, UK
Interests: 3D/4D printing; additive manufacturing; smart manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many countries, including major economies like the European Union, the United Kingdom, Japan, and South Korea, have committed to reaching net-zero emissions by 2050 or earlier. In recent years, manufacturing has rapidly developed with the continued advancement of artificial intelligence and big data techniques. By integrating these emerging technologies, manufacturing can become even smarter, resulting in numerous opportunities for enhancing sustainability and efficiency. In this Special Issue, the aim is to bring together diverse researchers into a common forum, exploring how big data, machine learning, digital twin or other new techniques could contribute to the advancement of sustainable manufacturing, thus creating a better future.

This Special Issue welcomes papers on the following themes:

  • Big data, machine learning and/or digital twin-aided sustainable traditional manufacturing, additive manufacturing, hybrid manufacturing or other novel manufacturing processes.
  • Sustainable additive manufacturing, hybrid manufacturing or other novel manufacturing process developments.

All types of papers are welcome.

Dr. Jingchao Jiang
Guest Editor

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. Journal of Manufacturing and Materials Processing 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 1800 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

  • big data
  • machine learning
  • digital twin
  • sustainable manufacturing
  • novel manufacturing processes

Published Papers (1 paper)

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Research

30 pages, 8620 KiB  
Article
Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland
by Kwaku Boakye, Kevin Fenton and Steve Simske
J. Manuf. Mater. Process. 2023, 7(6), 199; https://doi.org/10.3390/jmmp7060199 - 08 Nov 2023
Viewed by 2746
Abstract
This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO2 during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO [...] Read more.
This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO2 during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO2 emissions in a cement-producing facility. Due to the intricacy of the calcination process, it has historically been challenging to precisely anticipate the CO2 produced. The purpose of this study is to determine a direct association between CO2 generation from the manufacture of raw materials and the process factors. In this paper, six machine learning techniques are investigated to explore two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. CO2 molecular composition (dry basis) sensitivity analysis uses over 6000 historical manufacturing health data points as input variables, and the results are used to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models is examined, and the models are then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable has the biggest impact on CO2 emissions, the significance of the other factors is also assessed. Full article
(This article belongs to the Special Issue Sustainable Manufacturing for a Better Future)
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