10th Anniversary of Processes—Recent Advances in Process Control and Monitoring

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 4368

Special Issue Editors


E-Mail Website
Guest Editor
Dipartimento di Ingegneria Meccanica, Chimica e dei Materiali, Università degli Studi di Cagliari, I-09123 Cagliari, Italy
Interests: process control; state estimation; process modelling and simulation; digital twins; model predictive control; stochastic systems

E-Mail Website
Guest Editor
Department of Green Technology, University of Southern Denmark, 5230 Odense, Denmark
Interests: process design and optimization; process synthesis; process simulation; biofuels; natural product recovery and purification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Industrial and Civil Engineering, University of Pisa, Pisa, Italy
Interests: process control; process simulation and optimization; system identification; model predictive control; performance monitoring; chemical engineering

E-Mail Website
Guest Editor
BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Copure Links 653, 9000 Ghent, Belgium
Interests: model-based design and optimization; computational fluid dynamics (CFD); population balance modelling (PBM)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Interests: mathematical modelling; hybrid models; process control; potable water; water distribution networks; wastewater treatment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Chemical and Process Engineering, University of Surrey, Guildford, UK
Interests: process systems engineering; chemometrics; process synthesis; surrogate modelling; real-time optimization

Special Issue Information

Dear Colleagues,

Process control and monitoring systems have gained paramount importance in industrial processes since they guarantee efficient and effective management. With the growing complexity of industrial processes, there is a need for advanced control and monitoring techniques that can ensure the quality, safety, and reliability of processes. In this period of rapid technological advancements and digital transformation, process control and monitoring systems are critical to achieve operational excellence meeting the demands of customers and stakeholders. These motivations are particularly relevant in the context of the green transition, where process control is a fundamental tool that is used for achieving sustainable production goals. The design of green processes is expected to be challenged due to different factors such as variability in the feedstock used and limitations in operative conditions. As a consequence, it is valuable and important to investigate interactions among design, control, and process operability which are necessary in order to improve the dynamic performance of chemical processes during the early design stage.

This Special Issue aims to collect up-to-date and high-quality studies in the area of process control and process monitoring. Case studies and new applications in the context of green transitions are welcomed together with simultaneous process optimization and control for the recovery of high-added-value compounds. Contributions are encouraged to quantify how the proposed solution contributed to reducing wastes, emissions, and carbon footprints, and more generally how they positively contribute to the sustainability of the process. 

Topics of interest include, but are not limited to, the following:

  • Advanced control algorithms;
  • Closed-loop performance monitoring;
  • Robust control;
  • Optimization-based control;
  • Plant-wide control;
  • Controllability analysis;
  • Energy saving and waste reduction in production processes through control design;
  • State estimation;
  • Data-driven approaches for process control and monitoring.

Dr. Stefania Tronci
Dr. Massimiliano Errico
Dr. Riccardo Bacci Di Capaci
Prof. Dr. Ingmar Nopens
Dr. Elena Torfs
Dr. Michael Short
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.

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 3608 KiB  
Article
Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach
by Tawesin Jitchaiyapoom, Chanin Panjapornpon, Santi Bardeeniz and Mohd Azlan Hussain
Processes 2024, 12(4), 661; https://doi.org/10.3390/pr12040661 - 26 Mar 2024
Viewed by 407
Abstract
Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for [...] Read more.
Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond historical practice. The result shows that the proposed model improved prediction performance by 24.22% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified production and product quality improvements for enhancing the glycerin purification process. Full article
Show Figures

Figure 1

22 pages, 1479 KiB  
Article
Robust Observer-Based Proportional Derivative Fuzzy Control Approach for Discrete-Time Nonlinear Descriptor Systems with Transient Response Requirements
by Ting-An Lin, Yi-Chen Lee, Wen-Jer Chang and Yann-Horng Lin
Processes 2024, 12(3), 540; https://doi.org/10.3390/pr12030540 - 09 Mar 2024
Viewed by 414
Abstract
This paper proposes an observer-based proportional Derivative (O-BPD) fuzzy controller for uncertain discrete-time nonlinear descriptor systems (NDSs). Representing NDSs with the Takagi–Sugeno fuzzy model (T-SFM), the proportional derivative (PD) feedback method can be utilized in the fuzzy controller design via the Parallel Distributed [...] Read more.
This paper proposes an observer-based proportional Derivative (O-BPD) fuzzy controller for uncertain discrete-time nonlinear descriptor systems (NDSs). Representing NDSs with the Takagi–Sugeno fuzzy model (T-SFM), the proportional derivative (PD) feedback method can be utilized in the fuzzy controller design via the Parallel Distributed Compensation (PDC) concept, such that the noncausal problem and impulse behavior are avoided. A fuzzy observer is proposed to obtain unmeasured states to fulfill the PD fuzzy controller. Moreover, uncertainties and transient response performances are taken into account for the NDSs. Then, a stability analysis process and corresponding stability conditions are derived from the Lyapunov theory with the robust control method and the pole constraint. Different from existing research, the Singular Value Decomposition (SVD) and the projection lemma are utilized to transfer the stability conditions into the Linear Matrix Inequation (LMI) form. Because of this reason, the conservatism of the analysis process can be reduced by eliminating the restriction on the positive definite matrix in the Lyapunov function. By giving the proper center and radius parameters of the pole constraint in the O-BPD fuzzy controller design process, the expected transient responses can be obtained for different designers and different practical applications. Finally, the effectiveness and applicability of the proposed O-BPD fuzzy controller are demonstrated by two examples of the simulation. Full article
Show Figures

Figure 1

18 pages, 6948 KiB  
Article
A Fault Detection and Isolation Method via Shared Nearest Neighbor for Circulating Fluidized Bed Boiler
by Minseok Kim, Seunghwan Jung, Eunkyeong Kim, Baekcheon Kim, Jinyong Kim and Sungshin Kim
Processes 2023, 11(12), 3433; https://doi.org/10.3390/pr11123433 - 15 Dec 2023
Viewed by 657
Abstract
Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. [...] Read more.
Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods. Full article
Show Figures

Figure 1

14 pages, 11437 KiB  
Article
Digital Twin of a Hydraulic System with Leak Diagnosis Applications
by Leonardo Gómez-Coronel, Ildeberto Santos-Ruiz, Lizeth Torres, Francisco-Ronay López-Estrada, Samuel Gómez-Peñate and Elías Escobar-Gómez
Processes 2023, 11(10), 3009; https://doi.org/10.3390/pr11103009 - 19 Oct 2023
Viewed by 1246
Abstract
This paper presents the design and development of a digital twin to diagnose leaks in water distribution networks. The digital twin allows for the remote operation of the hydraulic system’s actuators using embedded microcontrollers integrated with Internet of Things (IoT) capabilities. Pressure head [...] Read more.
This paper presents the design and development of a digital twin to diagnose leaks in water distribution networks. The digital twin allows for the remote operation of the hydraulic system’s actuators using embedded microcontrollers integrated with Internet of Things (IoT) capabilities. Pressure head and flow rate measurements are received online in the operator interface, and hydraulic simulations are performed with a well-calibrated EPANET model of the hydraulic system to estimate the pressure head at nodes without sensors. A genetic algorithm was designed to detect and estimate the size of the leaks online. Different experiments were carried out to validate the online application of the method based on the digital twin and under a multi-leak event. Full article
Show Figures

Figure 1

18 pages, 707 KiB  
Article
Robust Design and Operation of a Multistage Reactor for Methanol Synthesis from Renewable Resources
by Tobias Keßler and Achim Kienle
Processes 2023, 11(10), 2928; https://doi.org/10.3390/pr11102928 - 07 Oct 2023
Viewed by 1088
Abstract
Methanol is an important raw material widely used in the chemical industry. This article addresses the challenge of fluctuations in green hydrogen as a feed stream for methanol production from renewable feedstock. For a staged reactor design, robust operating conditions are generated through [...] Read more.
Methanol is an important raw material widely used in the chemical industry. This article addresses the challenge of fluctuations in green hydrogen as a feed stream for methanol production from renewable feedstock. For a staged reactor design, robust operating conditions are generated through the simultaneous steady-state optimization of 50 process scenarios. The feed can be split and fed separately to the different reactor stages. However, neglected transient effects may render this design infeasible under dynamic conditions concerning carbon conversion and reactor temperature constraints. To overcome this, an additional dynamic optimization is conducted to ensure a feasible operation by an optimized feed-forward control of feed distribution and shell temperatures. In practice, this is possible because the disturbance, i.e., fluctuation, is measurable and predictable in a short time frame. The optimization yields optimal operating conditions, resulting in a reactor that is dynamically feasible for measurable fluctuating inlet conditions. Full article
Show Figures

Figure 1

Back to TopTop