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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 2025 | Viewed by 17487

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Special Issue Editors

Prof. Dr. Jose Carlos Pinto

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Programa de Engenharia Quimica / COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21921-972, Brazil
Interests: modeling, simulation and control of chemical reactors; in line monitoring and control of chemical processes; real time optimization of chemical processes; numerical techniques and procedures for real time applications
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Dr. Tahyná Barbalho Fontoura

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Programa de Engenharia Quimica / COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21921-972, Brazil
Interests: modeling and simulation of chemical processes, focused on industrial applications; membrane permeation; gas separation; optimization; chemical reactors; polymerization reactors; polymer science; computational fluid dynamics; scale-up

Dr. Tiago Lemos

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Programa de Engenharia Quimica / COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21921-972, Brazil
Interests: applied thermodynamics; molecular dynamics; Monte Carlo; polymer science; process modeling and simulation; process monitoring; statistical process control

Prof. Dr. André Domingues Quelhas

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Petrobras Address, Henrique Valadares Avenue, 28, Rio de Janeiro 20231-030, Brazil
Interests: modeling, control, and optimization of industrial processes

Special Issue Information

Dear Colleagues,

Industrial operations are characterized by the continuous search for more efficient process operations, more reliable and robust equipment designs, maximization of benefits, and minimization of deleterious environmental and social impacts. Simultaneously, the digital revolution provides increasingly cheaper computation platforms, with increasingly larger storage, memory, and computational capabilities. The combination of these has encouraged the continuous development of new modeling and numerical tools that are expected to be applied in actual industrial sites throughout the world and in different process engineering fields, for both offline and online analyses. Typical examples include the use of complex phenomenological models for the design of actual industrial equipment and process flowsheets; implementation of empirical models based on machine learning procedures for process monitoring, fault identification, and diagnosis; and utilization of identification techniques for the construction of virtual sensors and process twins, for purposes of process monitoring and control.

Based on these, this Special Issue on “Industrial Applications of Modeling Tools” aims to present new mathematical and numerical tools that have been implemented to solve real industrial problems at different production stages and that have been validated with actual industrial data, from process design to process monitoring and control. Some typical topics and applications include:

Use of detailed phenomenological models for the design of actual process equipment and process flowsheets;
Implementation of novel numerical schemes for faster design of actual process equipment and process flowsheets;
Use of phenomenological and empirical models for process scale-up and optimization;
Development and implementation of data-driven models for process monitoring (soft sensors), control, fault detection, and fault diagnosis;
Online and real time implementation of phenomenological models for process identification (digital twins) and control;
Novel numerical procedures for enhancement of numerical performance in actual industrial real-time applications;
Use of empirical and phenomenological models for just-in-time maintenance.

Prof. Dr. Jose Carlos Pinto
Dr. Tahyná Barbalho Fontoura
Dr. Tiago Lemos
Prof. Dr. André Domingues Quelhas
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

mathematical modeling
process simulation and control
process monitoring
process intelligence and machine learning
industrial applications

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Published Papers (8 papers)

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Research

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25 pages, 7509 KiB

Open AccessArticle

Optimizing Brown Stock Washing in the Pulp and Paper Industry: A System Dynamics Approach

by Bassam Kayal, Yara Nasr, Henri El Zakhem and Makram El Bachawati

Processes 2025, 13(2), 368; https://doi.org/10.3390/pr13020368 - 28 Jan 2025

Viewed by 1423

Abstract

The process of pulping and papermaking is a complicated, resource-demanding operation that requires energy, water, and chemicals. When not managed properly, the process can also contribute significantly to pollution. The washing process is one critical operation that impacts the process’s economics and environmental footprint. Most mills utilize rotary vacuum washers to separate black liquor from pulp, ensuring clean pulp for further processing downstream. Numerous factors influence the efficiency of a brown stock washer, and the washing operation itself is intricate. This study employs the system dynamics modeling approach to examine the critical role of brown stock washing in the pulp and paper industry, emphasizing optimizing process parameters for improved efficiency and sustainability. In the first part of the paper, a single stage of the washer system is modeled by establishing mass balance equations for key streams, including pulp, liquor, and dissolved solids. Within the system dynamics environment, separate models are developed for each stream, allowing for a detailed analysis of their behavior. To enhance modeling efficiency, the brown stock washing process is divided into four distinct operations: dilution, pulp formation, washing, and filtration. Breaking down the process into these operations makes it possible to focus on optimizing each step for improved overall performance. Furthermore, a control strategy is implemented to ensure stability in critical areas such as dilution vat level, discharged pulp consistency, and filtration tank level. In the final phase of the research, a multistage countercurrent brown stock washing line comprising three washers is modeled. Researchers can gain insights into how different components interact and influence overall performance by evaluating various parameters and analyzing the line’s efficiency. This comprehensive analysis enables them to identify potential improvements and optimize the washing process for enhanced productivity and quality output. The conclusions drawn from this work offer valuable guidance for optimizing water management practices in the pulp and paper sector, contributing to the industry’s sustainability goals and regulatory compliance. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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16 pages, 4914 KiB

Open AccessArticle

Stress Prediction Processes of Metal Pressure-Bearing Complex Components in Thermal Power Plants Based on Machine Learning

by Shutao Wang, Renqiang Shi, Jian Wu, Yunfei Ma, Chao Yang and Huan Liu

Processes 2025, 13(2), 358; https://doi.org/10.3390/pr13020358 - 27 Jan 2025

Viewed by 712

Abstract

The real-time stress assessment of metal pressure components is one of the key factors in ensuring the safe operation of thermal power plants. To address the challenge of real-time prediction of stress in the key areas of complex special-shaped metal pressure-bearing components in a certain domestic 300 MW thermal power plant, three typical complex metal pressure-bearing components, the main steam pipe tee (MSPT), the steam drum downcomer joint (DDJ) and the header ligament (HL), were taken as research objects. The stress distribution of the three complex metal pressure-bearing components under different conditions was analyzed through the finite element method, and the stress results at the dangerous points were used as samples to establish training sample data. Subsequently, different machine learning methods were employed to train the sample data. The training results indicate that neural networks (NNs) and the Auto-Sklearn Regression (ASR) models can accurately predict the stress of the key parts of complex metal pressure-bearing components in real time. The ASR method demonstrates better performance in stress prediction of the main steam pipe tee, with a prediction accuracy of ≥96%. The NN model shows better prediction for the header ligament, with a prediction accuracy of ≥94%. These research findings provide effective support for the high-temperature lifespan assessment and safe operation of thermal power plants. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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26 pages, 5768 KiB

Open AccessArticle

An Optimization Approach to Select Koopman Observables for Data-Based Modeling Using Dynamic Mode Decomposition with Control

by Amanda Martí-Coll, Adrián Rodríguez-Ramos and Orestes Llanes-Santiago

Processes 2025, 13(1), 284; https://doi.org/10.3390/pr13010284 - 20 Jan 2025

Viewed by 1329

Abstract

The advent and evolution of Industry 4.0 have been driven by technologies such as the Industrial Internet of Things, Big Data, and Cloud Computing. Within this framework, digital twins have gained significant popularity and are now employed across a wide range of industries and processes. A crucial step in developing a digital twin is deriving the system model, for which numerous methods are available. Among these, the Koopman operator and Dynamic Mode Decomposition with control have demonstrated their effectiveness and are widely recognized in the scientific community. This paper proposes a procedure for the automatic selection of Koopman observables by solving an optimization problem. The objective is to identify the minimal set of observables, belonging to a predefined dictionary, that minimize the error between actual process observations and predictions made by the estimated linear model—a key requirement for digital twin development. To tackle the optimization challenge, any algorithm available in the literature can be utilized. In this paper, the evolutive algorithms, including Genetic Algorithm and Differential Evolution Algorithm, are applied to evaluate the proposed approach in a benchmark problem. In both cases, the algorithms obtained the minimum set of observable functions from the dictionary used that achieve the lowest error obtained between the real process and the model, confirming the validity of the proposed method. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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19 pages, 6030 KiB

Open AccessArticle

Modeling and Experimental Verification of In-House Built Portable Ultrafiltration (PUF) System to Maintain Water Quality

by Azman Ariffin, Ahmad Khairi Abdul Wahab and Mohd Azlan Hussain

Processes 2024, 12(12), 2926; https://doi.org/10.3390/pr12122926 - 20 Dec 2024

Viewed by 808

Abstract

At present, over 2.6 billion people live without access to a continuous water supply, and nearly 900 million people do not obtain drinking water from reliable sources. To solve these problems, one of this study’s goals is to come up with a water-supply system that uses a simple, inexpensive, portable ultrafiltration (PUF) unit. To determine the effectiveness of the portable system, water-quality analysis has been carried out to determine if the system produces filtered water from various sources of water, reaching drinking-water standards. A simple model of the system using Darcy’s Law was also obtained to predict permeate flux and transmembrane pressure (TMP). Initially, simulation was performed using nominal values taken from the literature for four (4) parameters, i.e., membrane hydraulic resistance, initial rapid fouling constant, mass transfer coefficient, and foulant bulk concentration. By minimizing the error between the model with these nominal values and experimental values, an improved model with updated parameters was obtained using the Evolutionary Programming (EP) approach. With the updated model, the average error between the model and the experiment was reduced from 32% to 9%. This was further validated with new data taken from the experiment. This improved model with the updated parameter was then used to predict the TMP and compared with the experimental value. Contrasting the optimized model with the existing model indicates that the optimized model predicts membrane performance better, leading to a competent and reliable model for the purification of water using a PUF system built in-house. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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22 pages, 10398 KiB

Open AccessArticle

Modeling and Optimization of High-Capacity Experimental Reclaimers to Minimize the Seed and Lint Loss during Roller Ginning of Upland and Pima Cotton

by Jaya Shankar Tumuluru, Carlos B. Armijo, Derek P. Whitelock and Paul A. Funk

Processes 2023, 11(10), 2868; https://doi.org/10.3390/pr11102868 - 29 Sep 2023

Cited by 1 | Viewed by 1516

Abstract

In the present study, two high-capacity experimental roller gin reclaimers, (a) a modified 3-saw cylinder stick machine (three-saw) and (b) a modified 2-saw cylinder gin stand feeder (700), were optimized with respect to reclaimer saw cylinder speed and carryover/seed ratio to minimize the seed and lint loss for both Pima and Upland cotton varieties and were compared to a conventional roller gin reclaimer operated by the ginning industry under standard conditions. Developed regression models adequately described the seed and lint loss phenomena during the reclaiming process. Surface plots indicated that the reclaimer saw cylinder speed and carryover/seed ratio impacted the seed and lint loss for both the 3-saw and 700 reclaimers. Under optimized conditions, the 700 reclaimer resulted in lower lint and seed loss compared to the 3-saw reclaimer when using Upland cotton. In the case of Pima cotton, under optimized conditions, the 3-saw reclaimer had 38% lower lint loss and 24% higher seed loss compared to the 700 reclaimer. The regression equations of both 3-saw and 700 reclaimers were further used to optimize the reclaimers in parallel arrangement to minimize the seed and lint loss. With Upland cotton, the economic loss was about 2.5 times greater with the conventional reclaimer compared to the 3-saw and 700 reclaimers ($15.97/bale loss for the conventional, $8.63 for the 3-saw, and $6.44 for the 700 reclaimers). With Pima cotton, the conventional reclaimer resulted in a lower economic loss ($3.44/bale) compared to the 700 reclaimer which had a loss of about $3.59/bale. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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30 pages, 10431 KiB

Open AccessFeature PaperArticle

Process Hazard Analysis Based on Modeling and Simulation Tools

by Júlia Pinto Athanázio de Azevedo, Maurício Bezerra de Souza, Jr. and José Carlos Pinto

Processes 2022, 10(2), 386; https://doi.org/10.3390/pr10020386 - 17 Feb 2022

Cited by 5 | Viewed by 3310

Abstract

Chemical and oil processes are intrinsically sources of potential hazards. Although traditional qualitative hazard identification methods are simple, systematic, and flexible, such methodologies present limitations related to the inherent subjectivity, dependence on the team’s level of experience, and widespread time consumption of the members involved. In this context, the present work aims to develop a systematic way to use computational modeling and simulation tools for hazard identification. After extensive literature review, the present work proposes a methodology based on the association of the main points of previous works, with new contributions regarding the preparation for the simulations and the characterization of the minimum set of process variables that can enable appropriate interpretation of the results. The propene polymerization process (LIPP-SHAC process) was used as a case study to illustrate the proposed procedure. The paper explores how the model can be adapted for safety analyses and simulations for different hazard scenarios. The results obtained with different models are discussed and compared to those obtained with a traditional hazard identification approach to discuss how computational process modeling and simulation tools can sum to heuristic analysis. In conclusion, the use of simulations complementing the human-based approach can indeed enhance the understanding of mechanisms of hazardous scenarios, lessen conservative decision-making, and avoid overlooking device failures that can pose a severe hazard to the process. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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18 pages, 10051 KiB

Open AccessArticle

Research on the End-Face Distribution of Rotational Molding Heating Gun Based on Numerical Simulation Method

by Yongchun Yan, Lixin Zhang, Xiao Ma, Huan Wang, Wendong Wang and Yan Zhang

Processes 2022, 10(1), 97; https://doi.org/10.3390/pr10010097 - 4 Jan 2022

Viewed by 1690

Abstract

The distribution of heating gun ends plays a decisive role in the sidewall properties of finished rotomolded products. To obtain the optimal distribution of the end face of a rotational molding heating gun, the temperature response of the end-face mold under heating gun heating was investigated, and an analysis method based on numerical simulation is proposed. The FDS (fire dynamics simulator) was used to construct a heating model of the heating gun, simulate and obtain a heatmap of the temperature field distribution of a heating gun of Φ30–70 mm, and determine the optimal diameter and heating distance of the heating gun. ANSYS was used to establish the thermal response model of the heat-affected mold, which was combined with the mold structure and thermophysical properties of steel. A temperature field distribution on the inner wall surface of Φ30, Φ50, and Φ70 mm heating guns when heating at each diameter of the end face was obtained and the distribution position of the end face of each diameter heating gun was determined. ANSYS was used to establish the thermal response model of the end-face mold and obtain the temperature field distribution of the inner wall surface of the end-face mold. The size of the heat-affected area of each diameter heating gun was combined, the end-face heating gun distribution was optimized, and the optimal heating gun end-face distribution was obtained. An experimental platform was built, and a validation experiment was set up. Through the analysis and processing of the data of three experiments, the temperature variation curve of each diameter on the inner surface of the end-face mold was obtained. We compare and analyze the simulation and experimental results to determine the feasibility of the FDS + ANSYS method and the correctness and accuracy of the simulation model and the results. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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48 pages, 8785 KiB

Open AccessFeature PaperReview

Oxidative Coupling of Methane for Ethylene Production: Reviewing Kinetic Modelling Approaches, Thermodynamics and Catalysts

by Simoní Da Ros, Tahyná Barbalho Fontoura, Marcio Schwaab, Normando José Castro de Jesus and José Carlos Pinto

Processes 2021, 9(12), 2196; https://doi.org/10.3390/pr9122196 - 6 Dec 2021

Cited by 12 | Viewed by 5355

Abstract

Ethylene production via oxidative coupling of methane (OCM) represents an interesting route for natural gas upscaling, being the focus of intensive research worldwide. Here, OCM developments are analysed in terms of kinetic mechanisms and respective applications in chemical reactor models, discussing current challenges and directions for further developments. Furthermore, some thermodynamic aspects of the OCM reactions are also revised, providing achievable olefins yields in a wide range of operational reaction conditions. Finally, OCM catalysts are reviewed in terms of respective catalytic performances and thermal stability, providing an executive summary for future studies on OCM economic feasibility. Full article

(This article belongs to the Special Issue Industrial Applications of Modeling Tools)

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