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Editorial

Special Issue: “Modeling, Simulation, Control, and Optimization of Processes”

by
Marco S. Reis
1,*,
Argimiro R. Secchi
2 and
Simone C. Miyoshi
2
1
CERES, Department of Chemical Engineering, University of Coimbra, 3030-790 Coimbra, Portugal
2
Chemical Engineering Program, COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, Rio de Janeiro 21941-972, Brazil
*
Author to whom correspondence should be addressed.
Processes 2025, 13(5), 1388; https://doi.org/10.3390/pr13051388
Submission received: 21 March 2025 / Revised: 13 April 2025 / Accepted: 17 April 2025 / Published: 2 May 2025
(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)
To address the global challenges facing Earth, the field of process systems engineering [1] must focus more than ever on finding sustainable solutions for the new products that fulfill the various sorts of societal needs and the industrial processes that produce them. While it is essential to continue efforts towards achieving improvements in efficiency, safety, mass and energy integration, and process intensification, PSE must also go beyond them. New developments in artificial intelligence, machine learning, and data science are being added to the extensive modeling, simulation, and optimization toolkit of PSE [2,3], making the field more resourceful and prepared to face more demanding, challenging, and multiscale goals [4]. Guided by key sustainability criteria, new industrial processes, particularly biorefineries, and innovative products present numerous opportunities for computer-aided process engineering (CAPE) to assume a significant role and lead the way towards a better future for mankind.
This Special Issue on “Modeling, Simulation, Control, and Optimization of Processes” features novel advances and applications from the broad field of process systems engineering (PSE), focusing on sustainable processes. It gathers contributions from 74 authors representing institutions from 13 countries: Brazil, China, Ethiopia, Ecuador, Germany, India, Malaysia, Norway, Romania, The Republic of Korea, Saudi Arabia, South Africa, and Spain.
The PSE activities covered in the Special Issue include quality prediction [5], system identification, control [6], simulation, optimization [7], quality by design, life cycle assessment [8], process and equipment design, and maintenance [9].
Relevant practical contexts are covered, such as those relevant to the petrochemical industry, energy, bioenergy, the pharmaceutical industry, the agroindustry, metallurgy, mining, and wastewater treatment.
This Special Issue is primarily aimed at practitioners and researchers from industry and academia, as well as graduate and postgraduate students.

Conflicts of Interest

The authors declare no conflicts of interest.

List of Contributions

  • Martínez-Gómez, J.; Portilla, J. Coil High Voltage Spark Plug Boots Insulators Material Selection Using MCDM, Simulation, and Experimental Validation. Processes 2023, 11, 1292. https://doi.org/10.3390/pr11041292.
  • Qu, N.; Han, S.; You, W.; Wang, Y. Study on Adaptive Parameter Internal Mode Control Method for Argon–Oxygen Refining Ferrochrome Alloy. Processes 2023, 11, 1461. https://doi.org/10.3390/pr11051461.
  • Wondim, T.; Dzwairo, R.; Aklog, D.; Janka, E.; Samarakoon, G. Enhancing Textile Wastewater Treatment Performance: Optimization and Troubleshooting (Decision Support) via GPS-X Model. Processes 2023, 11, 2995. https://doi.org/10.3390/pr11102995.
  • Cai, C.; Zhao, M.; Shen, M.; Pan, Y.; Deng, X.; Shi, C. Physical and Numerical Simulation Study on Structure Optimization of the Inner Wall of Submerged Entry Nozzle for Continuous Casting of Molten Steel. Processes 2023, 11, 3237. https://doi.org/10.3390/pr11113237.
  • Dai, C.; Gao, Z.; Chen, Y.; Li, D. Generalized Conditional Feedback System with Model Uncertainty. Processes 2024, 12, 65. https://doi.org/10.3390/pr12010065.
  • Gerschütz, B.; Consten, Y.; Goetz, S.; Wartzack, S. PADDME—Process Analysis for Digital Development in Mechanical Engineering. Processes 2024, 12, 173. https://doi.org/10.3390/pr12010173.
  • Gao, W.; Song, S.; Yang, G.; Wang, C.; Wang, Y.; Chen, L.; Xu, W.; Ai, C. Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process. Processes 2024, 12, 250. https://doi.org/10.3390/pr12020250.
  • Zhou, W.; Guan, S.; Liu, S.; Wang, Y.; Cheng, Y.; Zhao, T.; Cheng, L.; Qin, T. The Distribution Pattern of Calcium Carbonate Crystallization in Tunnel Drainage Pipes. Processes 2024, 12, 1058. https://doi.org/10.3390/pr12061058.
  • Patil, T.; Siddique, M.; Shelke, M.; Ramzan, M.; Patil, M.; Shahid, M. Development and Validation of HSPiP- and Optimization-Assisted Method to Analyze Tolterodine Tartrate in Pharmacokinetic Study. Processes 2024, 12, 2164. https://doi.org/10.3390/pr12102164.
  • Wang, T.; Sun, S.; She, B. Optimization of Synchronous Control Parameters Based on Improved Sinusoidal Gray Wolf Algorithm. Processes 2024, 12, 2171. https://doi.org/10.3390/pr12102171.

References

  1. Stephanopoulos, G.; Reklaitis, G.V. Process systems engineering: From Solvay to modern bio- and nanotechnology. A history of development, successes and prospects for the future. Chem. Eng. Sci. 2011, 66, 4272–4306. [Google Scholar] [CrossRef]
  2. Christofides, P.D.; Armaou, A.; Lou, Y.; Varshney, A. Control and Optimization of Multiscale Process Systems; Birkhäuser: Basel, Switzerland, 2009. [Google Scholar]
  3. Grossmann, I.; Westerberg, A.W. Research Challenges in Process Systems Engineering. Aiche J. 2000, 46, 1700–1703. [Google Scholar] [CrossRef]
  4. Reis, M.S. A multiscale empirical modeling framework for system identification. J. Process Control 2009, 19, 1546–1557. [Google Scholar] [CrossRef]
  5. Fan, C.; Wang, W.; Cui, T.; Liu, Y.; Qiao, M. Research on Impact Prediction Model for Corn Ears by Integrating Motion Features Using Machine Learning Algorithms. Processes 2024, 12, 2362. [Google Scholar] [CrossRef]
  6. Andrei, A.M.; Bildea, C.S. Linear Model Predictive Control of Olefin Metathesis Process. Processes 2023, 11, 2216. [Google Scholar] [CrossRef]
  7. Yong, G.T.; Chan, Y.J.; Lau, P.L.; Ethiraj, B.; Ghfar, A.A.; Mohammed, A.A.A.; Shahid, M.K.; Lim, J.W. Optimization of the Performances of Palm Oil Mill Effluent (POME)-Based Biogas Plants Using Comparative Analysis and Response Surface Methodology. Processes 2023, 11, 1603. [Google Scholar] [CrossRef]
  8. Miyoshi, S.C.; Secchi, A.R. Simultaneous Life Cycle Assessment and Process Simulation for Sustainable Process Design. Processes 2024, 12, 1285. [Google Scholar] [CrossRef]
  9. Liu, J.; Zhan, C.; Liu, Z.; Zheng, S.; Wang, H.; Meng, Z.; Xu, R. Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm. Processes 2023, 11, 2462. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Reis, M.S.; Secchi, A.R.; Miyoshi, S.C. Special Issue: “Modeling, Simulation, Control, and Optimization of Processes”. Processes 2025, 13, 1388. https://doi.org/10.3390/pr13051388

AMA Style

Reis MS, Secchi AR, Miyoshi SC. Special Issue: “Modeling, Simulation, Control, and Optimization of Processes”. Processes. 2025; 13(5):1388. https://doi.org/10.3390/pr13051388

Chicago/Turabian Style

Reis, Marco S., Argimiro R. Secchi, and Simone C. Miyoshi. 2025. "Special Issue: “Modeling, Simulation, Control, and Optimization of Processes”" Processes 13, no. 5: 1388. https://doi.org/10.3390/pr13051388

APA Style

Reis, M. S., Secchi, A. R., & Miyoshi, S. C. (2025). Special Issue: “Modeling, Simulation, Control, and Optimization of Processes”. Processes, 13(5), 1388. https://doi.org/10.3390/pr13051388

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