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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 248

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)

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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 390

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)

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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 371

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)

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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

Viewed by 361

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)

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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 436

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)

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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

Viewed by 812

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)

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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 589

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 kee** 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)

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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 762

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)

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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 971

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)

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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 582

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)

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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, Zhou**g Ye, **aohua Zhou and Linbing Wang

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

Viewed by 632

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)

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Review

Jump to: Research

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 219

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)

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