Processes

Research

Jump to: Review

13 pages, 4243 KiB

Open AccessArticle

A Novel Nonsingular Fast Terminal Sliding Mode Control with Sliding Mode Disturbance Observer for Permanent Magnet Synchronous Motor Servo Control

by Difen Shi, Kai Bodemann, Yao Wang, Changliang Xu, Lulu Liu and Chungui Feng

Processes 2024, 12(9), 1986; https://doi.org/10.3390/pr12091986 - 14 Sep 2024

Viewed by 305

Abstract

This article proposes a novel nonsingular fast terminal sliding mode control (N-NFTSMC) with a sliding mode disturbance observer (SDOB) for permanent magnet synchronous motor (PMSM) servo control. Firstly, to reduce the chattering issue, a new sliding mode reaching law (NSRL) is proposed for [...] Read more.

This article proposes a novel nonsingular fast terminal sliding mode control (N-NFTSMC) with a sliding mode disturbance observer (SDOB) for permanent magnet synchronous motor (PMSM) servo control. Firstly, to reduce the chattering issue, a new sliding mode reaching law (NSRL) is proposed for the N-NFTSMC. Secondly, to further improve the dynamic tracking accuracy, we introduce a sliding disturbance observer to estimate unknown disturbances for feedforward compensation. Comparative simulations via Matlab/Simulink 2018 are conducted using the traditional NFTSMC and N-NFTSMC; the step simulation results show that the chattering phenomenon was suppressed well via the N-NFTSMC scheme. The sine wave tracking simulation proves that the N-NFTSMC has better dynamic tracking performance when compared with traditional NFTSMC. Finally, we carry out experiments to validate that the N-NFTSMC adequately suppresses the chattering issue and possesses better anti-disturbance performance. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

14 pages, 5742 KiB

Open AccessFeature PaperArticle

The Physical Properties and Crystal Structure Changes of Stabilized Ice Cream Affected by Ultrasound-Assisted Freezing

by Anna Kamińska-Dwórznicka and Anna Kot

Processes 2024, 12(9), 1957; https://doi.org/10.3390/pr12091957 - 12 Sep 2024

Viewed by 291

Abstract

In this study, the effect of ultrasound-assisted freezing with frequencies of 21.5 and 40 kHz, and a power of 2.4 kW in the chopped mode of milk ice cream in comparison to a standard freezer on the freezing course and formed crystal structure [...] Read more.

In this study, the effect of ultrasound-assisted freezing with frequencies of 21.5 and 40 kHz, and a power of 2.4 kW in the chopped mode of milk ice cream in comparison to a standard freezer on the freezing course and formed crystal structure was examined. The first part of the research included the preparation of an ice cream mixture on the basis of skimmed milk with the addition of an emulsifier, locust bean gum, xanthan gum, ι-carrageenan and a reference mixture without stabilizer addition. Ultrasound-assisted freezing shortened the processing time of both stabilized and non-stabilized ice cream. Stabilized samples of milk ice cream exposed to ultrasound (US) at a frequency of 21.5 kHz were characterized by the most homogeneous structure, consisting of crystals with the smallest diameters among all of the tested samples, the size of which, after 3 months of storage at −18 °C, was 7.8 µm (for the reference sample, it was 14.9 µm). The ice recrystallization inhibition (IRI effect) in the samples after US treatment with a frequency of 40 kHz was also observed, regardless of the addition of stabilizers, which may suggest that sonication with these parameters could replace or limit the addition of these substances. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

19 pages, 21153 KiB

Open AccessFeature PaperArticle

AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

by Inga Burke, Sven Salzer, Sebastian Stein, Tom Olatomiwa Olakunle Olusanya, Ole Fabian Thiel and Norbert Kockmann

Processes 2024, 12(9), 1821; https://doi.org/10.3390/pr12091821 - 27 Aug 2024

Viewed by 556

Abstract

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, [...] Read more.

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

20 pages, 1455 KiB

Open AccessFeature PaperArticle

An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection

by Ngoc-Tien Tran, Van-Long Trinh and Chen-Kuei Chung

Processes 2024, 12(8), 1723; https://doi.org/10.3390/pr12081723 - 16 Aug 2024

Cited by 1 | Viewed by 580

Abstract

In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific [...] Read more.

In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

20 pages, 2351 KiB

Open AccessArticle

Zone Model Predictive Control with Ellipsoid Softing Target in Chemical Processes

by Xin Wan

Processes 2024, 12(8), 1611; https://doi.org/10.3390/pr12081611 - 31 Jul 2024

Viewed by 627

Abstract

A zone control algorithm is proposed that considers both economic performance indicators and control performance indicators. Unlike classic set point control, zone control expands the control target into a convex set. In this study, an ellipsoid is used as the control target, and [...] Read more.

A zone control algorithm is proposed that considers both economic performance indicators and control performance indicators. Unlike classic set point control, zone control expands the control target into a convex set. In this study, an ellipsoid is used as the control target, and the advantages of the ellipsoid target are explained in terms of overall stability and computational load. After defining the distance measurement function and appropriate terminal constraints, an objective function that considers both control performance and optimization performance is constructed. A theoretical analysis shows that the proposed control algorithm satisfies the Lyapunov stability criterion. The superiority of the ellipsoid control target in handling complex multivariable control tasks is also demonstrated. This method has significant potential value in practical industrial applications, helping to unleash the potential control performance and economic benefits of zone control systems. Finally, the feasibility and stability of the algorithm are verified through a typical chemical process simulation. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

14 pages, 3142 KiB

Open AccessFeature PaperArticle

Leakage Quantification in Metallic Pipes under Different Corrosion Exposure Times

by Alaa Agala, Muhammad A. Khan, Feiyang He and Abdulaaziz Alnuman

Processes 2024, 12(7), 1529; https://doi.org/10.3390/pr12071529 - 20 Jul 2024

Viewed by 591

Abstract

The combined effects of aqueous corrosion, stress factors, and seeded cracks on leakage in cast iron pipes have not been thoroughly examined due to the complexity and difficulty in predicting their interactions. This study seeks to address this gap by investigating the interdependencies [...] Read more.

The combined effects of aqueous corrosion, stress factors, and seeded cracks on leakage in cast iron pipes have not been thoroughly examined due to the complexity and difficulty in predicting their interactions. This study seeks to address this gap by investigating the interdependencies between corrosion, stress, and cracks in cast iron pipes to optimise the material selection and design in corrosive environments. Leakage experiments were conducted under simulated localised corrosive conditions and internal pressure, revealing that leakage increased from 0 to 25 mL with crack sizes of 0.5 mm, 0.8 mm, 1 mm, and 1.2 mm, along with corrosion times of 0, 120, 160, and 200 h, and varying stress levels. An empirical model was developed using a curve-fitting approach to map the relationships among corrosion time, crack propagation, and leakage amount. The results demonstrate that the interaction between corrosion, stress, and crack propagation was complex and nonlinear, and the leakage amount increased from 0.7 to 0.10 mm every 15 min, as evidenced by SEM microstructure images and empirical data. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

19 pages, 4042 KiB

Open AccessFeature PaperArticle

A Novel Data Mining Framework to Investigate Causes of Boiler Failures in Waste-to-Energy Plants

by Dong Wang, Lili Jiang, Måns Kjellander, Eva Weidemann, Johan Trygg and Mats Tysklind

Processes 2024, 12(7), 1346; https://doi.org/10.3390/pr12071346 - 28 Jun 2024

Viewed by 737

Abstract

Examining boiler failure causes is crucial for thermal power plant safety and profitability. However, traditional approaches are complex and expensive, lacking precise operational insights. Although data-driven approaches hold substantial potential in addressing these challenges, there is a gap in systematic approaches for investigating [...] Read more.

Examining boiler failure causes is crucial for thermal power plant safety and profitability. However, traditional approaches are complex and expensive, lacking precise operational insights. Although data-driven approaches hold substantial potential in addressing these challenges, there is a gap in systematic approaches for investigating failure root causes with unlabeled data. Therefore, we proffered a novel framework rooted in data mining methodologies to probe the accountable operational variables for boiler failures. The primary objective was to furnish precise guidance for future operations to proactively prevent similar failures. The framework was centered on two data mining approaches, Principal Component Analysis (PCA) + K-means and Deep Embedded Clustering (DEC), with PCA + K-means serving as the baseline against which the performance of DEC was evaluated. To demonstrate the framework’s specifics, a case study was performed using datasets obtained from a waste-to-energy plant in Sweden. The results showed the following: (1) The clustering outcomes of DEC consistently surpass those of PCA + K-means across nearly every dimension. (2) The operational temperature variables T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r emerged as the most significant contributors to the failures. It is advisable to maintain the operational levels of T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r around 527 °C, 432 °C, 482 °C, 338 °C, 313 °C, and 343 °C respectively. Moreover, it is crucial to prevent these values from reaching or exceeding 594 °C, 471 °C, 537 °C, 355 °C, 340 °C, and 359 °C for prolonged durations. The findings offer the opportunity to improve future operational conditions, thereby extending the overall service life of the boiler. Consequently, operators can address faulty tubes during scheduled annual maintenance without encountering failures and disrupting production. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

15 pages, 5302 KiB

Open AccessArticle

Image Analysis Techniques Applied in the Drilling of a Carbon Fibre Reinforced Polymer and Aluminium Multi-Material to Assess the Delamination Damage

by Rúben D. F. Sousa Costa, Marta L. S. Barbosa, Filipe G. A. Silva, Tiago E. F. Silva, Abílio M. P. de Jesus, Francisco J. G. Silva, Luís M. P. Durão and João Manuel R. S. Tavares

Processes 2024, 12(6), 1258; https://doi.org/10.3390/pr12061258 - 19 Jun 2024

Viewed by 764

Abstract

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality [...] Read more.

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality and reliability. Image processing and analysis algorithms can be developed to effectively assess such damage, allowing for the calculation of delamination factors essential to the quality control of hole inspection in composite materials. In this study, a digital image processing and analysis algorithm was developed in Python to perform the delamination evaluation of drilled holes on a carbon fibre reinforced polymer (CFRP) and aluminium (Al) multi-material. This algorithm was designed to overcome several limitations often found in other algorithms developed with similar purposes, which frequently lead to user mistakes and incorrect results. The new algorithm is easy to use and, without requiring manual pre-editing of the input images, is fully automatic, provides more complete and reliable results (such as the delamination factor), and is a free-of-charge software. For example, the delamination factors of two drilled holes were calculated using the new algorithm and one previously developed in Matlab. Using the previous Matlab algorithm, the delamination factors of the two holes were 1.380 and 2.563, respectively, and using the new Python algorithm, the results were equal to 3.957 and 3.383, respectively. The Python results were more trustworthy, as the first hole had a higher delamination area, so its factor should be higher than that of the second one. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

11 pages, 3663 KiB

Open AccessFeature PaperArticle

Acoustic Effects of Uneven Polymeric Layers on Tunable SAW Oscillators

by Ionut Nicolae, Mihaela Bojan and Cristian Viespe

Processes 2024, 12(6), 1217; https://doi.org/10.3390/pr12061217 - 13 Jun 2024

Viewed by 653

Abstract

Surface acoustic wave (SAW) sensors in tunable oscillator configuration, with a deposited polymeric layer, were used to investigate the layer’s impact on the oscillator’s resonant frequency. The SAW oscillators were tuned by means of variable loop amplification. Full-range amplification variation led to a [...] Read more.

Surface acoustic wave (SAW) sensors in tunable oscillator configuration, with a deposited polymeric layer, were used to investigate the layer’s impact on the oscillator’s resonant frequency. The SAW oscillators were tuned by means of variable loop amplification. Full-range amplification variation led to a resonant frequency increase of ~1.7 MHz due to the layer’s nonlinear reaction. The layer’s morphology and location resulted in a specific resonant frequency–amplitude dependence. Five types of layers were used to test the causal linkage between the layers’ morphological parameters or positioning and the SAW oscillator’s resonant frequency. The frequency variation trend is almost linear, with a complex minute variation. Small amplitude sigmoids occur at certain attenuation values, due to layer acoustic resonances. Multiple sigmoids were linked with layer resonances of different orders. A good correlation between the layer’s thickness and resonance position was found. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

16 pages, 2668 KiB

Open AccessFeature PaperArticle

Controlling the Friction Coefficient and Adhesive Properties of a Contact by Varying the Indenter Geometry

by Iakov A. Lyashenko, Thao H. Pham and Valentin L. Popov

Processes 2024, 12(6), 1209; https://doi.org/10.3390/pr12061209 - 13 Jun 2024

Cited by 1 | Viewed by 889

Abstract

In the present paper, we describe a series of laboratory experiments on the friction between rigid indenters with different geometrical forms and an elastic sheet of elastomer as a function of the normal load. We show that the law of friction can be [...] Read more.

In the present paper, we describe a series of laboratory experiments on the friction between rigid indenters with different geometrical forms and an elastic sheet of elastomer as a function of the normal load. We show that the law of friction can be controlled by the shape of the surface profile. Since the formulation of the adhesive theory of friction by Bowden and Tabor, it is widely accepted and confirmed by experimental evidence that the friction force is roughly proportional to the real contact area. This means that producing surfaces with a desired dependence of the real contact area on the normal force will allow to “design the law of friction”. However, the real contact area in question is that during sliding and differs from that at the pure normal contact. Our experimental studies show that for indenters having a power law profile f(r) = c_nrⁿ with an index n < 1, the system exhibits a constant friction coefficient, which, however, is different for different values of n. This opens possibilities for creating surfaces with a predefined coefficient of friction. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Graphical abstract

24 pages, 1295 KiB

Open AccessArticle

CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions

by Georgina Elizabeth Riosvelasco-Monroy, Iván Juan Carlos Pérez-Olguín, Salvador Noriega-Morales, Luis Asunción Pérez-Domínguez, Luis Carlos Méndez-González and Luis Alberto Rodríguez-Picón

Processes 2024, 12(6), 1070; https://doi.org/10.3390/pr12061070 - 23 May 2024

Viewed by 712

Abstract

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate [...] Read more.

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

18 pages, 10994 KiB

Open AccessFeature PaperArticle

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version 8 and Transfer Learning Approaches

by Nodir Yunusov, Bappy MD Siful Islam, Akmalbek Abdusalomov and Wooseong Kim

Processes 2024, 12(5), 1039; https://doi.org/10.3390/pr12051039 - 20 May 2024

Cited by 2 | Viewed by 1421

Abstract

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial [...] Read more.

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial resources, the natural environment, and global warming. Therefore, timely fire detection is essential for quick and effective response and not to endanger forest resources, animal life, and the human economy. This study introduces a forest fire detection approach utilizing transfer learning with the YOLOv8 (You Only Look Once version 8) pretraining model and the TranSDet model, which integrates an improved deep learning algorithm. Transfer Learning based on pre-trained YoloV8 enhances a fast and accurate object detection aggregate with the TranSDet structure to detect small fires. Furthermore, to train the model, we collected 5200 images and performed augmentation techniques for data, such as rotation, scaling, and changing due and saturation. Small fires can be detected from a distance by our suggested model both during the day and at night. Objects with similarities can lead to false predictions. However, the dataset augmentation technique reduces the feasibility. The experimental results prove that our proposed model can successfully achieve 98% accuracy to minimize catastrophic incidents. In recent years, the advancement of deep learning techniques has enhanced safety and secure environments. Lastly, we conducted a comparative analysis of our method’s performance based on widely used evaluation metrics to validate the achieved results. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

28 pages, 5233 KiB

Open AccessArticle

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

by Viorel Mînzu, Iulian Arama and Eugen Rusu

Processes 2024, 12(5), 991; https://doi.org/10.3390/pr12050991 - 13 May 2024

Viewed by 792

Abstract

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that [...] Read more.

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

20 pages, 13316 KiB

Open AccessArticle

Confluence Effect of Debris-Filled Damage and Temperature Variations on Guided-Wave Ultrasonic Testing (GWUT)

by Samuel C. Olisa and Muhammad A. Khan

Processes 2024, 12(5), 957; https://doi.org/10.3390/pr12050957 - 8 May 2024

Viewed by 1012

Abstract

Continuous monitoring of structural health is essential for the timely detection of damage and avoidance of structural failure. Guided-wave ultrasonic testing (GWUT) assesses structural damages by correlating its sensitive features with the damage parameter of interest. However, few or no studies have been [...] Read more.

Continuous monitoring of structural health is essential for the timely detection of damage and avoidance of structural failure. Guided-wave ultrasonic testing (GWUT) assesses structural damages by correlating its sensitive features with the damage parameter of interest. However, few or no studies have been performed on the detection and influence of debris-filled damage on GWUT under environmental conditions. This paper used the pitch–catch technique of GWUT, signal cross-correlation, statistical root mean square (RMS) and root mean square deviation (RMSD) to study the combined influence of varying debris-filled damage percentages and temperatures on damage detection. Through experimental result analysis, a predictive model with an R2 of about 78% and RMSE values of about

7.5 \times 10^{- 5}

was established. When validated, the model proved effective, with a comparable relative error of less than 10%. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

19 pages, 7263 KiB

Open AccessArticle

SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion

by Hongli Li, Zhiqi Yi, Liye Mei, Jia Duan, Kaimin Sun, Mengcheng Li, Wei Yang and Ying Wang

Processes 2024, 12(5), 931; https://doi.org/10.3390/pr12050931 - 2 May 2024

Viewed by 1353

Abstract

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited [...] Read more.

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

15 pages, 2955 KiB

Open AccessArticle

Comparison between Conventional Ageing Process in Barrels and a New Rapid Aging Process Based on RSLDE: Analysis of Bioactive Compounds in Spirit Drinks

by Daniele Naviglio, Paolo Trucillo, Angela Perrone, Domenico Montesano and Monica Gallo

Processes 2024, 12(4), 829; https://doi.org/10.3390/pr12040829 - 19 Apr 2024

Viewed by 848

Abstract

“Aging” is a practice that allows alcoholic beverages to mature and gives them particular flavors and colors. In this context, oak or durmast wooden barrels are used in this process, thus providing different types of aging. This conventional process produces a slow enrichment [...] Read more.

“Aging” is a practice that allows alcoholic beverages to mature and gives them particular flavors and colors. In this context, oak or durmast wooden barrels are used in this process, thus providing different types of aging. This conventional process produces a slow enrichment of organic compounds in the spirit inside the barrels. Organic substances present in the internal part of the barrels slowly undergo the phenomenon of extraction by the liquid phase (solid–liquid extraction). In this work, a new procedure based on rapid solid–liquid dynamic extraction (RSLDE) was used to evaluate the potential of obtaining the effects of aging in spirits in shorter times than conventional methods. For this purpose, a comparison between two solid–liquid extraction techniques, RSLDE and conventional maceration, was made. Four water/ethanol 60:40 (v/v) model solutions were prepared and put in contact with medium-toasted chips using the two extraction procedures (conventional and non-conventional) and determining dry residue and total polyphenol content. Reversed phase high-performance liquid chromatography (RP-HPLC) analyses allowed the identification and quantification of furfural, ellagic acid and phenolic aldehydes (vanillin, syringaldehyde, coniferaldehyde and sinapaldehyde). The aging procedure with medium-toasted chips was tested on a young commercial grappa using maceration and RLSDE. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Graphical abstract

19 pages, 5991 KiB

Open AccessArticle

Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures

by Biyu Yang, Songli Yang, Zhoujing Ye, Xiaohua Zhou and Linbing Wang

Processes 2024, 12(4), 800; https://doi.org/10.3390/pr12040800 - 16 Apr 2024

Viewed by 829

Abstract

Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. [...] Read more.

Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, through its micro- and macro-texture adjustments, effectively reduced vibration frequencies between the tire and pavement, thus mitigating noise. Utilizing the TOPSIS multi-objective optimization framework, an optimization model for pavement textures was developed to augment skid resistance and noise reduction at varying speeds. The results indicated that at 60 km/h, an optimal balance of groove width, depth, and spacing yielded superior skid resistance with a minimal noise increase. At 80 km/h, increased groove spacing and depth were shown to effectively decrease noise while maintaining efficient water evacuation. The optimal pavement texture design must consider the specific context, including traffic volume, vehicle types, and operating speeds. This study provides essential guidance for optimizing urban cement concrete pavement textures, aiming to diminish traffic noise and bolster road safety. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

Review

Jump to: Research

35 pages, 7848 KiB

Open AccessFeature PaperReview

Robots for the Energy Transition: A Review

by Sergio Taraglio, Stefano Chiesa, Saverio De Vito, Marco Paoloni, Gabriele Piantadosi, Andrea Zanela and Girolamo Di Francia

Processes 2024, 12(9), 1982; https://doi.org/10.3390/pr12091982 (registering DOI) - 14 Sep 2024

Viewed by 327

Abstract

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under [...] Read more.

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

31 pages, 3231 KiB

Open AccessReview

Use of Triboelectric Nanogenerators in Advanced Hybrid Renewable Energy Systems for High Efficiency in Sustainable Energy Production: A Review

by Van-Long Trinh and Chen-Kuei Chung

Processes 2024, 12(9), 1964; https://doi.org/10.3390/pr12091964 - 12 Sep 2024

Viewed by 356

Abstract

Renewable energy is the best choice for clean and sustainable energy development. A single renewable energy system reveals an intermittent disadvantage during the energy production process due to the effects of weather, season, day/night, and working environment. A generally hybrid renewable energy system [...] Read more.

Renewable energy is the best choice for clean and sustainable energy development. A single renewable energy system reveals an intermittent disadvantage during the energy production process due to the effects of weather, season, day/night, and working environment. A generally hybrid renewable energy system (HRES) is an energy production scheme that is built based on a combination of two or more single renewable energy sources (such as solar energy, wind power, hydropower, thermal energy, and ocean energy) to produce electrical energy for energy consumption, energy storage, or a power transmission line. HRESs feature the outstanding characteristics of enhancing energy conversion efficiency and reducing fluctuations during the energy production process. Triboelectric nanogenerator (TENG) technology transduces wasted mechanical energies into electrical energy. The TENG can harvest renewable energy sources (such as wind, water flow, and ocean energy) into electricity with a sustainable working ability that can be integrated into an HRES for high power efficiency in sustainable renewable energy production. This article reviews the recent techniques and methods using HRESs and triboelectric nanogenerators (TENGs) in advanced hybrid renewable energy systems for improvements in the efficiency of harvesting energy, sustainable energy production, and practical applications. The paper mentions the benefits, challenges, and specific solutions related to the development and utilization of HRESs. The results show that the TENG is a highly potential power source for harvesting energy, renewable energy integration, application, and sustainable energy development. The results are a useful reference source for developing HRES models for practical applications and robust development in the near future. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

21 pages, 6889 KiB

Open AccessReview

Advanced-Functional-Material-Modified Electrodes for the Monitoring of Nitrobenzene: Progress in Nitrobenzene Electrochemical Sensing

by Khursheed Ahmad and Tae Hwan Oh

Processes 2024, 12(9), 1884; https://doi.org/10.3390/pr12091884 - 2 Sep 2024

Viewed by 627

Abstract

Nitrobenzene (NB) is one of the nitro-aromatic compounds that is extensively used in various chemical industries. Despite its potential applications, NB is considered to be a toxic compound that has significant hazardous effects on human health and the environment. Thus, it can be [...] Read more.

Nitrobenzene (NB) is one of the nitro-aromatic compounds that is extensively used in various chemical industries. Despite its potential applications, NB is considered to be a toxic compound that has significant hazardous effects on human health and the environment. Thus, it can be said that the NB level should be monitored to avoid its negative impacts on human health. In this vein, the electrochemical method has emerged as one of the most efficient sensing techniques for the determination of NB. The sensing performance of the electrochemical techniques depends on the electro-catalytic properties and conductivity of the electrode materials. In the past few years, various electrode materials, such as conductive metal ions, semiconducting metal oxides, metal–organic frameworks, and two-dimensional (2D) materials, have been used as the electrode material for the construction of the NB sensor. Thus, it is worth summarizing previous studies on the design and synthesis of electrode materials for the construction of the NB sensor. In this mini-review article, we summarize the previous reports on the synthesis of various advanced electrode materials, such as platinum (Pt) nanoparticles (NPs), silver (Ag) NPs, carbon dots (CDs), graphene, graphitic carbon nitride (g-C₃N₄), zinc stannate (ZnSnO₃), cerium oxide (CeO₂), zinc oxide (ZnO), and so on. Furthermore, the impacts of different electrode materials are systematically discussed for the sensing of NB. The advantages of, limitations of, and future perspectives on the construction of NB sensors are discussed. The aim of the present mini-review article is to enhance the knowledge and overall literature, working towards the construction of NB sensors. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

22 pages, 1452 KiB

Open AccessFeature PaperReview

Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems

by Paul Arévalo and Danny Ochoa-Correa

Processes 2024, 12(7), 1365; https://doi.org/10.3390/pr12071365 - 30 Jun 2024

Viewed by 773

Abstract

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of [...] Read more.

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety. Full article

(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Industrial Process Operation State Sensing and Performance Optimization

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (21 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI