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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: 30 July 2024 | Viewed by 11497

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Special Issue Editors

Prof. Dr. Argimiro Resende Secchi

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Chemical Engineering Program, COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-914, Brazil
Interests: process modeling and simulation; process control and optimization; real-time process monitoring and optimization; numerical methods
Special Issues, Collections and Topics in MDPI journals

Dr. Marco S. Reis

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Department of Chemical Engineering, University of Coimbra, Polo II, Rua Sílvio Lima, 3030-790 Coimbra, Portugal
Interests: process analytics; process systems engineering; fault detection, diagnosis and prognosis; industrial data science; chemometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Simone De Carvalho Miyoshi

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Chemical Engineering Program, COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-914, Brazil
Interests: life cicle analysis; techno-economic-environmental analysis; process modeling and simulation; process optimization; biorrefineries

Special Issue Information

Dear Colleagues,

In the current climate emergency on Earth, more than ever, process systems engineers need to focus on finding more sustainable solutions. It is necessary to go beyond a greater efficiency of industrial plants, high mass and energy integration, and process intensification. Based on the main sustainability criteria, new industrial processes, specially biorefineries, and new products open plenty of opportunities for computer-aided process engineering (CAPE).

This Special Issue on “Modeling, Simulation, Control, and Optimization of Processes” aims to curate novel advances in process systems engineering (PSE) focusing on sustainable processes. Topics include, but are not limited to, methods and/or applications in the following areas:

Process Modeling and Simulation;
Process Optimization;
Process Synthesis and Design;
Process Integration and Intensification;
Process Control and Instrumentation;
Data Mining and Machine Learning;
Production Planning and Scheduling;
Molecular Modeling and Product Design;
Fault Detection and Diagnostics;
Digital Twins and other Industrial Applications of PSE Tools.

Prof. Dr. Argimiro Resende Secchi
Dr. Marco S. Reis
Dr. Simone De Carvalho Miyoshi
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

sustainable process
process systems engineering
carbon negative
energy transition
product and process design
process intensification
hydrogen
systems analysis
global optimization

Published Papers (10 papers)

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Research

21 pages, 6222 KiB

Open AccessArticle

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

by Wei Gao, Shiheng Song, Guisheng Yang, Chunyi Wang, Yong Wang, Lijuan Chen, Wenqiang Xu and Chao Ai

Processes 2024, 12(2), 250; https://doi.org/10.3390/pr12020250 - 24 Jan 2024

Viewed by 482

Abstract

Due to the large inertia and strong impact accompanying the free-falling hook process of crawler cranes, it is difficult to meet the demand for flexible and smooth braking control under different weight load conditions. Therefore, this paper takes the free-fall hook system as the research object and combines system operation characteristics and control theory to carry out research on flexible braking control of the free-fall hook system. Firstly, a joint simulation platform of MATLAB (version 2018b) and AMESim (version 2019.1) software is built to theoretically analyze the key components of the free-fall hook system (proportional pressure-reducing valve, winch reducer, and wet clutch). Secondly, a mathematical model of the braking process is established, and the pressure control demand is clarified to analyze the reasons for the existence of dead zones and hysteresis loops in the system. Meanwhile, it is found that the dead zones and hysteresis loops existing in the pressure output of the pressure-reducing valve are the main factors of flexibility with load braking. Then, in this paper, a closed-loop control strategy is formulated based on the automatic adaptation of the braking gear in combination with the fuzzy PID pressure. Finally, the effectiveness of the control strategy proposed in this paper is verified with simulation and experimental testing using the pressure hysteresis loop of the free-fall hook process and the load-braking acceleration as the judging criteria. The results show that the system pressure hysteresis loop is reduced by 50%–60% and the maximum braking acceleration is reduced by 24%–30% under the conditions of 6.44 tonnes and 10.44 tonnes, which improves the accuracy of pressure control and achieves flexible and smooth braking with loads for different tonnages of free-fall hooks. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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33 pages, 1751 KiB

Open AccessArticle

PADDME—Process Analysis for Digital Development in Mechanical Engineering

by Benjamin Gerschütz, Yvonne Consten, Stefan Goetz and Sandro Wartzack

Processes 2024, 12(1), 173; https://doi.org/10.3390/pr12010173 - 11 Jan 2024

Viewed by 780

Abstract

Design processes are always in motion, since more and more data-driven methods are used for various design and validation tasks. However, small and medium enterprises especially struggle with enhancing their processes with data-driven methods due to a lack of practical and easy-to-use analysis and redesign methods which can handle design process characteristics. In this paper, we present PADDME, which stands for process analysis for digital development in mechanical engineering, as a novel method that, in contrast to currently available analysis methods, considers those design process characteristics with respect to the integration of data-driven methods. Furthermore, a novel technology-readiness framework for digital engineering is introduced. Using the PADDME method, an industrial case study on introducing data-driven methods into the design and evaluation process chain is presented. The usability and novelty of the method are shown by the case study. Thus, PADDME allows a detailed capturing of current design processes and paves the way for process optimisation through data-driven methods. PADDME is a valuable method for advancing digital mechanical engineering processes in small and medium enterprises, and future work will focus on refining and expanding its application and evaluation. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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15 pages, 81013 KiB

Open AccessArticle

Generalized Conditional Feedback System with Model Uncertainty

by Chengbo Dai, Zhiqiang Gao, Yangquan Chen and Donghai Li

Processes 2024, 12(1), 65; https://doi.org/10.3390/pr12010065 - 27 Dec 2023

Viewed by 542

Abstract

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a half-quadrotor system control test. Furthermore, the GCF scheme is open to existing optimal control and robust control theories. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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14 pages, 4497 KiB

Open AccessArticle

Physical and Numerical Simulation Study on Structure Optimization of the Inner Wall of Submerged Entry Nozzle for Continuous Casting of Molten Steel

by Changyou Cai, Ming Zhao, Minggang Shen, Yuhua Pan, Xin Deng and Chunyang Shi

Processes 2023, 11(11), 3237; https://doi.org/10.3390/pr11113237 - 16 Nov 2023

Viewed by 719

Abstract

The submerged entry nozzle (SEN) plays an important role in the continuous casting production process. It is a cast refractory pipe fitting installed in the lower part of the tundish and inserted below the molten steel level of the mold. It not only affects the speed of molten steel flow, but is also prone to nodules and affects production. In the present work, the flow behavior of molten steel in a traditional nozzle and that in a new type of nozzle whose inner wall was distributed with arrays of hemispherical crowns were studied by means of both physical simulation (using a water model) and numerical simulation (using ANSYS CFX) based on the prototype of a production continuous casting slab mold. Both experimental and numerical simulation results show that, compared with the traditional nozzle, the impact depth generated by the new-type nozzle in the mold is reduced by 21.06–26.03 cm, the impact angle is reduced by 14–17 degrees, and swirl flow was generated inside the new-type nozzle, which not only improves the flow characteristics inside the submerged entry nozzle and changes the dead zone size in the submerged entry nozzle, but also improves the velocity distribution at the outlet of the nozzle and minimizes the possibility of nodulation. In addition, in contrast to the traditional nozzle that generates flat body-shaped jets of molten steel flow, the new-type nozzle produces baseball glove-shaped jets that penetrate shallower into the molten steel bath in the mold, which significantly reduces the outlet velocity and is conducive to the floating of inclusions. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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19 pages, 7175 KiB

Open AccessArticle

Enhancing Textile Wastewater Treatment Performance: Optimization and Troubleshooting (Decision Support) via GPS-X Model

by Tilik Tena Wondim, Rimuka Bloodless Dzwairo, Dagnachew Aklog, Eshetu Janka and Gamunu Samarakoon

Processes 2023, 11(10), 2995; https://doi.org/10.3390/pr11102995 - 17 Oct 2023

Viewed by 1026

Abstract

Textile factory water consumption could be optimized to minimize the generation of wastewater, reduce treatment costs, and promote resource recovery. However, downstream plant operation and management is a prime concern in the textile industry, particularly bringing treated wastewater effluent to an acceptable discharge limit. The aim of the study was to optimize key process control parameters to the observed operational challenges of existing processes and suggest an operational guide to the operators and decision makers to enhance the treatment performance in GPS-X. The formulated troubleshooting and decision support strategy, and the optimization results of waste-activated sludge in the primary and secondary clarifiers, was within the range of 15 ± 5 m³/d and 83 ± 7 m³/d, respectively, with a recycle-activated sludge flow of 150 ± 10 m³/d. The sludge retention time was 5 ± 1 d and 6.7 ± 0.5 d in the secondary and primary clarifiers, respectively. The addition of a carbon source in the form of molasses had a flow of 0.5 ± 0.05 m³/d, and the variation in the influent due to wastewater characteristics and rainfall was optimized to 600 ± 50 m³/d. The optimum air flow into the aeration tank was 550 ± 5 m³/hr and saved 91.5% of energy in the optimized process. Thus, the study is indispensable for the effective and efficient operation of the plant and serves as a good guide to the plant operators and decision makers for the best course of action. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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17 pages, 2788 KiB

Open AccessArticle

Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm

by Jiang Liu, Changshu Zhan, Zhiyong Liu, Shuangqing Zheng, Haiyang Wang, Zhou Meng and Ruya Xu

Processes 2023, 11(8), 2462; https://doi.org/10.3390/pr11082462 - 16 Aug 2023

Cited by 1 | Viewed by 861

Abstract

Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls’ courtship behavior, the adaptive behavior of female peafowls in proximity, the adaptive search behavior of peafowl chicks, and interactive behavior among male peafowls. These mechanisms enable effective search for optimal or near-optimal solutions. Through comparisons with a real-world industrial case and other advanced algorithms, the superiority of the IPOA in solving DSP problems is demonstrated. This research contributes to improving maintenance efficiency and quality, bringing positive impacts to industrial equipment maintenance. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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18 pages, 2661 KiB

Open AccessFeature PaperArticle

Linear Model Predictive Control of Olefin Metathesis Process

by Andrei Maxim Andrei and Costin Sorin Bildea

Processes 2023, 11(7), 2216; https://doi.org/10.3390/pr11072216 - 23 Jul 2023

Cited by 1 | Viewed by 1052

Abstract

The applicability of linear model predictive control to the 2-butene metathesis process is studied. Similarly to industrial practice, the model predictive controller is configured on a supervisory level, providing set points to basic process controllers. The development of the process model is based on open-loop identification from input–output data extracted from dynamic simulation performed in Aspen Plus Dynamics. The model predictive controller, designed using MATLAB tools, supervises a system consisting of two inputs (feed rate and reaction temperature) and two outputs (ethylene and propylene production rates). The performance of the model-based control strategy is assessed by Aspen Plus Dynamics-Simulink co-simulation and compared to regulatory control through several indexes (mean square error, integral square error, peak error, and integral absolute error). The model predictive controller outperforms the feedback controller. Considerations regarding the workflow for the implementation of model predictive control in an industrial environment are provided. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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23 pages, 5188 KiB

Open AccessArticle

Optimization of the Performances of Palm Oil Mill Effluent (POME)-Based Biogas Plants Using Comparative Analysis and Response Surface Methodology

by Gloria Tung Xin Yong, Yi Jing Chan, Phei Li Lau, Baranitharan Ethiraj, Ayman A. Ghfar, Abdallah A. A. Mohammed, Muhammad Kashif Shahid and Jun Wei Lim

Processes 2023, 11(6), 1603; https://doi.org/10.3390/pr11061603 - 24 May 2023

Cited by 3 | Viewed by 2689

Abstract

The rapid increase in demand for renewable energy has led to a need for more efficient and effective ways to produce biogas from palm oil mill effluent (POME), which is rich in biological and chemical oxygen demand (BOD and COD). Despite its potential as a source of biogas, POME is not always effectively utilized in biogas production due to a lack of optimization of the treatment process. This study aims to address this issue by identifying the critical parameters affecting biogas production from POME and optimizing the process for maximum biogas yield and COD removal. This study employed comparative analysis and response surface methodology to optimize the performance of palm oil mill effluent (POME)-based biogas plants in Malaysia. Historical data from three commercial POME-based biogas plants in Malaysia were analyzed to identify the most critical parameters for biogas yield and COD removal. Response surface methodology, using Box–Behnken design and Design-Expert software, was then used to optimize these parameters. Sensitivity analysis was performed to interpret the impact of parameters on biogas production, with Organic Loading Rate (OLR) found to be the most critical factor for methane yield. The results showed that the optimum conditions for maximum methane production were OLR of 1.23 kg/m³·day, inlet Total Solids (TS) of 46,370 mg/L, pH of 4.5, and temperature of 45.4 °C, resulting in a 39.6% increase in methane yield (0.335 m³ CH₄/kgCOD_removed) and a 1.1% increase in COD removal (93.4%). Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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13 pages, 1881 KiB

Open AccessArticle

Study on Adaptive Parameter Internal Mode Control Method for Argon–Oxygen Refining Ferrochrome Alloy

by Na Qu, Shunjie Han, Wen You and Yifan Wang

Processes 2023, 11(5), 1461; https://doi.org/10.3390/pr11051461 - 11 May 2023

Cited by 1 | Viewed by 808

Abstract

In order to determine a way to be able to shorten the smelting time of low-carbon ferrochrome alloys and to strengthen the temperature control capability of the AOD converter during the smelting process, this article establishes a mechanism model of the rate of oxygen supply and carbon content change in the smelting process as well as the temperature of the reaction fluid in the converter. The physical and chemical reactions of the smelting process and the actual smelting data are used as the basis. The temperature and carbon content in the smelting converter are considered as the output quantity and the rate of oxygen supply is considered as the input quantity. An expert internal model control framework is built. In addition, an adaptive parameter adjustment mechanism is added to the control framework, taking into account data such as the maximum gas supply rate and the maximum limiting temperature of the actual production process. This method improves the smelting speed and smelting accuracy compared to the general internal mode control method. Finally, according to this method, with the use of the built low-carbon ferrochrome alloy centralized control system for smelting, the system shortens the smelting time by 12.79% compared with the general method and controls the converter temperature below 1950 K, which achieves the expected goal according to the simulation and actual smelting results. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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23 pages, 5837 KiB

Open AccessArticle

Coil High Voltage Spark Plug Boots Insulators Material Selection Using MCDM, Simulation, and Experimental Validation

by Javier Martínez-Gómez and Jaime Eduardo Portilla

Processes 2023, 11(4), 1292; https://doi.org/10.3390/pr11041292 - 21 Apr 2023

Cited by 1 | Viewed by 1326

Abstract

The examination and choice of an alternate composite material for the high-voltage circuit of Otto cycle internal combustion engines—more commonly known as gasoline engines—are presented in the research that follows. To do this, multicriteria selection procedures are employed, and the outcomes are validated through the use of thermal character simulation software and standard laboratory tests. Nylon is the recommended material for Coils on Plug (COP) high-voltage insulators. Four of the six multicriteria selection techniques utilized in this study were found to be effective. It was discovered through the virtual simulation process that, even in the same environment with the same edge circumstances, the thermal behavior of the materials differs dramatically because the quadrants exhibit different behavior depending on the material. Given that nylon has a lower elasticity modulus than silicone, it was determined that the dimensions are crucial for the nylon Spark Plug Boot (SPB) to comply with the dielectric isolation process. It must have a minimal clearance in order to be related to the geometry of the spark plug and perform the perfect insulation in this manner. Full article

(This article belongs to the Special Issue Modeling, Simulation, Control, and Optimization of Processes)

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