Process Systems Engineering for Complex Industrial Systems
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 10 September 2024 | Viewed by 1186
Special Issue Editors
Interests: process control; transfer learning; estimation; distributed parameter systems; processes systems engineering
Interests: process monitoring; fault diagnosis; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: multivariate statistical analysis; process monitoring; deep neural network
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent control; artificial intelligence; image processing; robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid advancement of modern industrial engineering, process systems engineering plays an increasingly important role in addressing the increasing system complexity and enhancing profitability, safety, and sustainability. Conventional techniques and solutions are often insufficient to deal with the multiple scales and plantwide networks of complex industrial systems. To address the complexity of modern industrial systems, advanced model-based and data-driven methods, algorithms, and solutions are needed for accurate modeling, monitoring, planning, and control.
The special issue on “Process Systems Engineering for Complex Industrial Systems” seeks high-quality works focusing on the recent advances in process systems engineering for industrial applications, especially the model-based, data-driven and hybrid techniques for process modeling, condition monitoring, fault detection and diagnosis, quality prediction and soft sensing, regulation and control design, etc. Moreover, new problems and future research directions on process systems engineering are also welcome.
Topics include, but are not limited to:
- Model-based process modeling
- Data-driven process modeling
- State and parameter estimation
- Condition monitoring, fault detection and diagnosis
- Software sensor modeling
- Process control and regulation
- Thermodynamics-based process modeling and control
- Machine learning-based modeling, estimation, and control
- Process data analytics and multivariate statistical analysis
- Hybrid methods and model calibration
- Applications in chemical, biological, manufacturing, and energy systems
Dr. Junyao Xie
Prof. Dr. Wanke Yu
Dr. Shumei Zhang
Dr. Yiyang Chen
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. Processes is an international peer-reviewed open access monthly 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 2400 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
- process systems engineering
- modeling
- control
- estimation
- systems analysis
- methods
- algorithms
- tools
- design
