Process Monitoring Research Articles

Multimodal Decoupled Representation With Compatibility Learning for Explicit Nonstationary Process Monitoring

Multimodal Decoupled Representation With Compatibility Learning for Explicit Nonstationary Process Monitoring

Drilling Process Monitoring Based on Operation Mode Recognition and Dynamic Feature Extraction

Drilling Process Monitoring Based on Operation Mode Recognition and Dynamic Feature Extraction

Cloud-Based Institutional Quality Assurance Model for a Local Higher Education Institution Using Data Mining Techniques

This study presents a Cloud-Based Institutional Quality Assurance Model for Local Higher Education Institutions (LHEIs). It utilizes advanced data mining techniques and custom data models that enhance the monitoring, assessment, and improvement of quality assurance processes through the utilization of cloud computing infrastructure in a developed system that offers scalability, flexibility, and accessibility, empowering policymakers to make data-driven decisions and to efficiently analyze vast amounts of data collected from various sources and be guided by algorithms. The findings advance quality assurance methodologies and offer valuable insights for practitioners, policymakers, and researchers in higher education.

Determinants of Community Participation in Sustainable Management of Rural Water Supply System: Evidence from District Lahore, Pakistan

Sustainable functionality of rural water supply systems is one of the most imperative aspects to ensure the provision of safe drinking water for communities. In recent decades, a number of projects have been initiated by national and international organizations to meet the desired need for safe drinking water and sanitation, but a large proportion of these development projects fail shortly after their execution. The majority of water supply projects fail in rural communities within three years after development due to the least level of community participation regarding decision-making, site identification, planning, execution, monitoring, evaluation and management process and, above all, unawareness, lack of basic technical and management skills. This study presents the most influencing determinants of community participation that are compulsory for the sustainable functionality of rural water supply systems. This study was conducted in the district of Lahore, Pakistan and simple random sampling methods and techniques were applied to draw the sample from the population. A semi-structured questionnaire was used as a tool for data collection. The result of the study indicated that a significant association existed among community participation in planning, execution, operation, maintenance & evaluation processes and sustainability functionality of rural water supply systems.

Mussel‐inspired self‐assembly of silver nanoclusters into multifunctional silver aerogels for enhanced catalytic and bactericidal applications

AbstractSilver nanoclusters (AgNCs) have shown broad application prospects in catalysis, sensing, and biological fields. However, the limited stability of AgNCs has become the main challenge restricting their practical application in complex environments. Herein, a mussel‐inspired, dopamine‐assisted self‐assembly approach is reported to fabricate 3D AgNC aerogels (PDA/AgNCs), which possess significantly enhanced structural stability and synergistic functional properties. The prepared AgNC aerogels display a hierarchical network structure with an ultrafine ligament size of 10.3 ± 1.2 nm and a high specific surface area of 50.7 m2 g−1. The gelation mechanism is elucidated by in‐depth characterization and time‐lapse monitoring of the gelation process vis spectroscopic and microscopic approaches. Owing to the distinct features of aerogels and the synergistic effect of AgNCs and PDA, the fabricated aerogels can not only efficiently decolorize dyes with a faster kinetic than individual AgNCs, but also exhibit remarkable broad‐spectrum antimicrobial activity. Consequently, a conceptual water‐treatment device is established by depositing PDA/AgNC aerogels on the cotton substrate, which shows good performance in both catalytic dye degradation and bacterial killing in the flowing system. This mussel‐inspired self‐assembly strategy has great potential in developing robust AgNC‐based functional materials, which also provides a new guideline for designing sophisticated materials with integrated functions and synergistic properties.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.

Biocalorimetry-aided monitoring of fungal pretreatment of lignocellulosic agricultural residues

The present study aimed to investigate whether and how non-invasive biocalorimetric measurements could serve for process monitoring of fungal pretreatment during solid-state fermentation (SSF) of lignocellulosic agricultural residues such as wheat straw. Seven filamentous fungi representing different lignocellulose decay types were employed. Water-soluble sugars being immediately available after fungal pretreatment and those becoming water-extractable after enzymatic digestion of pretreated wheat straw with hydrolysing (hemi)cellulases were considered to constitute the total bioaccessible sugar fraction. The latter was used to indicate the success of pretreatments and linked to corresponding species-specific metabolic heat yield coefficients (YQ/X) derived from metabolic heat flux measurements during fungal wheat straw colonisation. An YQ/X range of about 120 to 140 kJ/g was seemingly optimal for pretreatment upon consideration of all investigated fungi and application of a non-linear Gaussian fitting model. Upon exclusion from analysis of the brown-rot basidiomycete Gloeophyllum trabeum, which differs from all other here investigated fungi in employing extracellular Fenton chemistry for lignocellulose decomposition, a linear relationship where amounts of total bioaccessible sugars were suggested to increase with increasing YQ/X values was obtained. It remains to be elucidated whether an YQ/X range being optimal for fungal pretreatment could firmly be established, or if the sugar accessibility for post-treatment generally increases with increasing YQ/X values as long as “conventional” enzymatic, i.e. (hemi)cellulase-based, lignocellulose decomposition mechanisms are operative. In any case, metabolic heat measurement–derived parameters such as YQ/X values may become very valuable tools supporting the assessment of the suitability of different fungal species for pretreatment of lignocellulosic substrates.Key points• Biocalorimetry was used to monitor wheat straw pretreatment with seven filamentous fungi.• Metabolic heat yield coefficients (YQ/X) seem to indicate pretreatment success.• YQ/X values may support the selection of suitable fungal strains for pretreatment.

Youth migration during war: Triggers of positive aspirations and preservation of human resources in Ukraine

Russia’s full-scale invasion of Ukraine has become an existential challenge and a trigger of the migration crisis. The study aims to identify migration intentions of youth and factors for the preservation of the young population in Ukraine (a case study of university youth). The study conducts a sociological survey using a Google Forms questionnaire. The sample was formed by the method of three-stage selection: (1) quotas for the share of undergraduate and graduate students; (2) the higher education institutions in Ukraine were selected by the criterion of the number of students and specialties; and (3) field of knowledge. The sample size is calculated based on the resampling method and included 2,200 people from all regions in Ukraine (except Donetsk, Luhansk, Zaporizhzhia, and Dnipropetrovsk oblasts). The study reveals that 10% of students plan to go abroad in the near future before graduation, 30% plan to stay in Ukraine only if the socio-economic situation improves after the end of the war, and 28.3% plan to migrate after graduation. The reasons for positive migration aspirations among students are socio-economic and security issues (14.9% can find a job abroad in the short term, 11.1% see the lack of further prospects in Ukraine even after the end of the war). Monitoring of youth migration processes across two vectors – current volumes and potential aspirations – can serve as an information and analytical basis for the development of a new vision of the country’s migration security strategy to preserve human resources in Ukraine.

A research study of lightweight state perception algorithm based on improved YOLOv5s‐Tiny for fully mechanized top‐coal caving mining

AbstractReal‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining.

Rule based complex event processing for an air quality monitoring system in smart city

Smart city development is accelerating, aiming for systematic urban living, but challenges like air quality and traffic management need addressing for sustainability in cities. The World Health Organization (WHO) links air pollution to severe respiratory diseases, emphasizing the need for real-time air quality monitoring and timely decisions based on varying pollutant concentrations (NH3, O3, SO2, NO2) across locations. This research work proposes a framework combining Siddhi Complex Event Processing (CEP) and SPARQL queries for air quality monitoring. Dataset collected from Central Pollution Control Board (CPCB) India, is preprocessed, passed through Apache Kafka, and transformed into a knowledge graph. The RDF data is integrated with a knowledge graph via Apache Jena to enhance CEP performance. Decision trees generate rules and enrich CEP engines with CPCB parameters to identify complex patterns. SPARQL queries on real-time RDF data categorize air quality conditions for all stakeholders. The model's effectiveness is validated by deploying RDF chunks to the CEP engine and evaluating performance through various query execution times. Our model efficiently processed numerous air pollutant events (200, 400, 600 etc.), filtering out about 80% and demonstrating robust event processing capabilities, which enhances air quality management and supports sustainable urban ecosystems and societies.

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

A Cyber-Physical Production System for the Integrated Operation and Monitoring of a Continuous Manufacturing Train for the Production of Monoclonal Antibodies.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber-physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Real-time determination of combustion degree by laser-induced breakdown spectroscopy

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

A propagation path-based interpretable neural network model for fault detection and diagnosis in chemical process systems

Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial role in maintaining a productive and reliable chemical process system. Developments in AI and machine learning have boosted FDD model performances especially with deep learning methods. However, these neural network models are considered black-boxes where the reasoning behind a diagnosis is unclear, preventing industrial adoption. Therefore, in this study, an interpretable neural network model is proposed for FDD in chemical processes. This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph convolutional networks. A mechanism for interpreting the node activations which represent process variables is developed for decision verification. The proposed method is evaluated on the benchmark Tennessee Eastman Process where it achieves a 93.56% accuracy on selected faults.

Experimental Investigation on the Influence of Water on Rockburst in Rock-like Material with Voids and Multiple Fractures.

To investigate the influence of water content on the rockburst phenomena in tunnels with horizontal joints, experiments were conducted on simulated rock specimens exhibiting five distinct levels of water absorption. Real-time monitoring of the entire blasting process was facilitated through a high-speed camera system, while the microscopic structure of the rockburst debris was analyzed using scanning electron microscopy (SEM) and a particle size analyzer. The experimental findings revealed that under varying degrees of water absorption, the specimens experienced three stages: debris ejection; rockburst; and debris spalling. As water content increased gradually, the intensity of rockburst in the specimens was mitigated. This was substantiated by a decline in peak stress intensity, a decrease in elastic modulus, delayed manifestation of pre-peak stress drop, enhanced amplitude, diminished elastic potential energy, and augmented dissipation energy, resulting in an expanded angle of rockburst debris ejection. With increasing water content, the bond strength between micro-particles was attenuated, resulting in the disintegration of the bonding material. Deformation failure was defined by the expansion of minuscule pores, gradual propagation of micro-cracks, augmentation of fluffy fine particles, exacerbation of structural surface damage akin to a honeycomb structure, diminishment of particle diameter, and a notable increase in quantity. Furthermore, the augmentation of secondary cracks and shear cracks, coupled with the enlargement of spalling areas, signified the escalation of deformation failure. Simultaneously, the total mass of rockburst debris gradually diminished, accompanied by a corresponding decrease in the proportion of micro and fine particles within the debris.

Demonstration of temperature monitoring of the curing process in resin transfer moulding using integrated phase-shifted Bragg grating sensors

Composite materials have increased in use across multiple industries due to their low weight, thermal stability and design flexibility. As applications grow, the importance of accurate process-monitoring techniques grows, with various research teams investigating a variety of in situ cure-monitoring sensors. Photonic integrated circuits (PICs) can be a feasible solution in tracking curing cycles due to their resistance to harsh conditions and ease of integration into the production tool. This paper demonstrates the integration and performance evaluation of an embedded phase-shifted Bragg grating solution, instead of commonly used fiber sensors, based on 220 nm height silicon-on-insulator platform for in-situ monitoring of the RTM-6 resin curing process in a composite tool. The embedded optical sensor presents high sensitivity (75 pm/°C) and linearity (R2=0.995) up to 120 °C, enabling precise temperature measurements throughout the curing cycle. The experimental results show consistency with previously reported simulations, throughout the calibration, dry run characterization and the curing process, up to 120 °C, allowing for accurate monitoring of the curing process.

In Situ Array Microextraction and Metabolic Profiling of Small Extracellular Vesicles to Guide and Monitor Maternal Delivery.

The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large-scale extraction and high-throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on-plate aptamer array (16×24 spots) is developed for continuous scale-up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5h) and sample volume by 95% (only 0.125µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post-administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post-administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real-time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large-scale clinical samples for medicine innovation and advancement.

Vibro stone column ground improvement practice in the United Kingdom

Vibro stone-column (VSC) techniques have been applied in the United Kingdom (UK) since the late 1950s–early 1960s, where they are one of the most commonly adopted ground improvement techniques. While there have been significant advances in VSC equipment development and also in the monitoring of the installation process for the VSC technique over the past 50 years, the design of VSC remains largely empirical and has changed very little since the 1970s. Publication of well-documented case histories and of instrumented field trials has also been limited over the past 25 years in the UK, in a VSC ground improvement market, which is predominantly commerce driven, based on the cheapest price. This has inherent implications for further advancement of design and innovation through funded full-scale field-based research. Current state-of-the-art of VSC in UK ground improvement is reviewed in the above context, together with comments on future challenges.

Passive seismic monitoring of injection-production process in oilfield using reverse time imaging

Abstract Monitoring underground fluid migration caused by injection/production processes is crucial for guiding petroleum exploitation in mature oilfields and ultimately enhancing petroleum production. In this paper, we propose a time-lapse reverse time imaging (RTI) to dynamically monitor the injection/production processes within oilfield. By utilizing RTI to track microseimicities at different time periods, we can analyze the relationship between injection/production activities and the spatiotemporal changes in microseismic distribution. The inferred relationship enables the time-lapse RTI to infer fluid migration patterns within oil reservoirs. To assess the accuracy and spatiotemporal resolution of the time-lapse RTI, we conducted numerical experiments to evaluate the imaging quality under different microseismic distribution scenarios. In addition, we assessed the method's stability under low signal-to-noise ratio conditions. Numerical results indicate that the time-lapse RTI can effectively distinguish the spatiotemporal variations of seismic swarms at depths of 0.5 kilometers within the target layer, even in the presence of strong noise. Practical applications show a significant correlation between changes in swarm distribution surrounding reservoirs and fluctuations in oil production. Utilizing time-lapse RTI enables real-time monitoring of oilfield injection/production processes, thereby offering valuable insights for optimizing oilfield development and fostering future increases in petroleum production.

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

This paper reviews the current state of research in data analytics and machine learning techniques, focusing on their applications in process industrial manufacturing, particularly in control and optimization. Key areas for future research include selection and transfer learning for process monitoring, addressing time-varying characteristics, and enhancing data-driven optimal control with domain-specific knowledge. Additionally, the paper explores reinforcement learning techniques and robust optimization, including distributional robust optimization, for high-level decision-making. Emphasizing the importance of historical knowledge of plants and processes, this paper aims to identify knowledge gaps and pave the way for future research in data-driven strategies for process industries, with a particular emphasis on energy efficiency and optimization.