Process Monitoring Research Articles

Dielectric Monitoring of Microwave Extraction Processes

Dielectric Monitoring of Microwave Extraction Processes

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

In civil aviation systems, the inertial measurement unit (IMU)/Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, redundant IMUs/GNSS integrated navigation systems are susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU faults. Additionally, dealing with correlated measurements between redundant constructions presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation systems. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace covariance of the primary filter. An optimal allocation scheme for continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

Tensile testing data of additive manufactured ASTM D638 standard specimens with embedded internal geometrical features.

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

As a highly promising technology in additive manufacturing, the laser powder bed fusion has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a complementary metal–oxide–semiconductor industrial camera. The important features were selected, extracted and quantificated by analyzing the tensile test results. Finally, combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimization algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination (R 2) is improved from 63% to 90.57% compared to using only the laser power as input.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

An ecologically sound and participatory monitoring network for pan-Arctic seabirds.

In a warming Arctic, circumpolar long-term monitoring programs are key to advancing ecological knowledge and informing environmental policies. Calls for better involvement of Arctic peoples in all stages of the monitoring process are widespread, although such transformation of Arctic science is still in its infancy. Seabirds stand out as ecological sentinels of environmental changes, and priority has been given to implement the Circumpolar Seabird Monitoring Plan (CSMP). We assessed the representativeness of a pan-Arctic seabird monitoring network focused on the black-legged kittiwake (Rissa tridactyla) by comparing the distribution of environmental variables for all known versus monitored colonies. We found that with respect to its spatiotemporal coverage, this monitoring network does not fully embrace current and future environmental gradients. To improve the current scheme, we designed a method to identify colonies whose inclusion in the monitoring network will improve its ecological representativeness, limit logistical constraints, and improve involvement of Arctic peoples. We thereby highlight that inclusion of study sites in the Bering Sea, Siberia, western Russia, northern Norway, and southeastern Greenland could improve the current monitoring network and that their proximity to local populations might allow increased involvement of local communities. Our framework can be applied to improve existing monitoring networks in other ecoregions and sociological contexts.

In situ Electrical Impedance Tomography for Visualizing Water Transportation in Hygroscopic Aerogels.

The global water crisis demands immediate attention, and atmospheric water harvesting (AWH) provides a viable alternative. However, studying the real-time subtle relationship between water absorption, diffusion, and internal structure for hygroscopic materials is challenging. Herein, a dynamic visualization technique is proposed that utilizes an in situ electrical impedance tomography (EIT) system and a precise reconstruction algorithm to achieve real-time monitoring of the water sorption process within aerogels from an internal microstructural perspective. These results can be inferred that composites' pore sizes affecting the kinetics of their moisture absorption. In addition, the diffusion path of moisture absorption and the distribution of stored moisture inside aerogels exhibit intrinsic self-selective behavior, where the fiber skeleton of the aerogel plays a crucial role. In summary, this work proposes a generic EIT-based technique for the in situ and dynamic monitoring of the hygroscopic process, pointing to an entirely new approach regarding research on AWH materials.

In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing

In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing

Design of Control Unit for Fertilizer Decomposition

The design and optimisation of a control device for tracking the decomposition of organic material into fertiliser is the main goal of this research study. The system uses temperature, moisture, and humidity sensors to control important variables in real-time with the goal of producing high-quality fertiliser, accelerating decomposition, and using less energy. By integrating sensors, developing algorithms, testing, and analysing theoretical data, the control unit is designed to improve agricultural sustainability by offering an advanced tool for composting process monitoring and control

Economic modelling and optimisation of SPRT-based quality control schemes for individual observations

In many industrial processes, it may not be practical to obtain sample sizes larger than one due to various reasons. As a result, practitioners must rely on control charts based on individual observations for statistical process monitoring and control. The sequential probability ratio test (SPRT) chart is an appropriate solution for such situations. Existing literature predominantly delves into the statistical aspects of designing or optimising an SPRT chart, neglecting the economic and associated design aspects. However, past experience suggests that statistical design may not always be optimal from an economic perspective. This paper aims to bridge this research gap by proposing a two-stage Markov chain approach to develop an economic model for the optimal design of the SPRT chart with individual observations. Following the principles of economic-statistical designs, optimal parameters are determined by solving a mixed-integer non-linear programming problem that minimises the long-run cost per item while ensuring compliance with practical constraints. Through an extensive study, we find that the economic performance of the SPRT chart surpasses that of competing individual control charts in many cases. This indicates that the economic-statistical design of the SPRT chart can yield significant economic benefits while maintaining desirable statistical properties. Moreover, enhancements have been introduced to refine the optimal design of the SPRT chart, retaining excellent overall performance across a wide range of shifts. Finally, we provide two industrial examples to illustrate the effectiveness of the proposed SPRT chart.

Demonstration of temperature monitoring of the curing process in Resin Transfer Moulding using integrated phase-shifted Bragg grating sensors

Demonstration of temperature monitoring of the curing process in Resin Transfer Moulding using integrated phase-shifted Bragg grating sensors

Increasing coverage and uptake of voluntary family planning in Uganda's emerging municipalities and secondary cities: An implementation research study protocol.

While urban areas are often perceived to have better access to healthcare services, including modern family planning (FP) services, urban dwellers including those with better socioeconomic status are faced with multidimensional challenges that shape their access to appropriate FP services. In Uganda's urban spaces, there is currently a lack of understanding among service providers, civil society organizations, and individuals/communities regarding the implementation of interventions that promote informed choice and voluntary use of family planning services. This knowledge gap has profound implications for reproductive rights. This study seeks to enhance existing efforts towards increasing coverage and uptake of Voluntary Family Planning (VFP) in Jinja City and Iganga Municipality, central eastern Uganda. Our primary question is, "What interventions can effectively be packaged and delivered to increase the uptake of VFP among different segments of urban residents?" We propose to use the Human-Centered Design (HCD) approach to understand the needs and challenges of users and community capabilities in ensuring access to VFP services. Co-creating with stakeholders' engagement and a data-driven-centric approach will steer design and adaptation that respond to the different population segments within the urban space. As such, the study will be implemented in three phases: formative assessment, design and implementation, and implementation monitoring and evaluation. The implementation process will incorporate robust monitoring, learning, and adaptation mechanisms. The primary focus of these mechanisms will be to utilize gathered information effectively to inform the design of the implementation and facilitate continuous learning throughout the process. The study will apply a process monitoring and evaluation approach to address questions related to what package of FP interventions work, for whom, under what circumstances and why. Guided by strong learning and implementation flexibility, we hypothesize that our implementation will provide segmentation-specific high-impact interventions in an urban context. This implementation research protocol has been registered on the Open Science Framework (OSF) repository Registries (https://osf.io/vqxu9; DOI: 10.17605/OSF.IO/VQXU9).

The Influence of Project Monitoring Process on the Performance of Technical Vocational Educational and Training Institution’s Construction in Kenya

The Influence of Project Monitoring Process on the Performance of Technical Vocational Educational and Training Institution’s Construction in Kenya

Dimensionality reducing Gaussian mixture‐based reconstruction for fault detection in multimode processes

AbstractModern industrial processes increasingly prioritize demands for safety and reliability, spurring substantial research on process monitoring models. Among existing research subjects, concurrent multimode operating conditions are vital for effective process monitoring. This work proposes an efficient dimensionality‐reducing Gaussian mixture‐based reconstruction approach for multimode industrial process monitoring. The t‐SNE method is first employed to transform high‐dimensional data into a lower‐dimensional space that retains critical operational information. Using these reduced dimensions, a robust Gaussian mixture model is established to partition the operation data into different modes. Furthermore, the original data are assigned to the corresponding operating modes, and local variational autoencoder (VAE) reconstruction models are established, respectively. For each VAE model, two statistics are designed, termed and , to detect abnormalities. The proposed method is applied to a three‐phase flow facility, and the superiority over the comparison methods is proved.

Generative AI and process systems engineering: The next frontier

This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Institutional governance and state-building in Somaliland

Governance is the combined principles of the effective rule of law within a framework of institutional capacity for legal entity, Institutional governance encompassed both administrative aspect and legal framework of institutional development linked with the state-building development. Institutional governance entails accountability, transparency to the demands of effective monitoring process, the aim of the paper is to know how institutional governance affect state-building in Somaliland, institution governance framework and the role of institutions to stat-building follow to accountability, transparency, rule of law and participation. The paper exams to understand the dynamics of institutional governance and state-building idea of governance in state-progress, state-building determinate, institutional Legal frameworks, discrepancies affecting institutions and the consequence. Somaliland is post-conflict country, the consequence from the destruction of infrastructural development brought some basic facilities including, peace, schools and institutional governance has destroyed, the paper presents the role of government bureaucrats (Civil Service)[1] in institutional building, peace restoration and institutional development, finally one of the ideas of paper discussed is the how citizens involved state-building and state-development. The Somaliland Civil Service Commission is a Somaliland government agency established in 1993 that oversees the employment of civil servants and lead reforms in Somaliland's decentralization process. The head of the agency is chairman and is appointed by the President of Somaliland.

The Directions for Improving the Surface Wastewater Treatment Facilities

Prospective directions for increasing efficiency of the drains by improving the systems for collection, disposal and treatment of surface wastewater have been considered. The design and technological features of treatment facilities in operation have been analyzed. The significant shortcomings have been identified, as a result of which the quality of purified water discharged into water bodies, declared by manufacturers, is reduced and the operation is complicated. Promising directions for resolving the identified problems have been recommended: targeted formation of surface wastewater flows, increasing the engineering and technological level of mono-block structures, the use of modern structural materials, providing the possibility of remote monitoring and control of the technological process, as well as timely routine maintenance.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.MethodsThis prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source “Pothos” air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.ResultsFollowing initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.ConclusionThis study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Extracting powder bed features via electron optical images during electron beam powder bed fusion

Electron beam powder bed fusion offers the unique opportunity to observe the process by measuring scattered electrons on a metal detector. This technique is the state of the art in generating electron optical images of the build area after melting using single- or multi-detector setups. The images enable the detection of surface defects like porosity or material transport by reconstructing the surface topography. Internal defects such as layer-bonding defects cannot be identified. Many of these defects, particularly layer-bonding defects, often originate from an irregular distribution of the powder bed.This work introduces an additional process step by recording an electron optical image after the distribution of the powder bed. Combining this with an electron optical image after melting the previous layer enables extraction of powder bed features such as the current powder bed height. The underlying method bases on the correlation of experimental measurements and numerical simulations of the intensity of the electron optical signal for different powder bed heights. With this approach, it is possible to identify irregular powder distributions, such as uncovered areas of previously molten material or locally varying powder bed heights. This information is crucial for online monitoring and real time process control. Exemplary, this opens the opportunity of healing the powder bed by an additional raking step.

Parameter free AEWMA control chart for dispersion in semiconductor manufacturing

The study presents a new parameter free adaptive exponentially weighted moving average (AEWMA) control chart tailored for monitoring process dispersion, utilizing an adaptive approach for determining the smoothing constant. This chart is crafted to adeptly detect shifts within anticipated ranges in process dispersion by dynamically computing the smoothing constant. To assess its effectiveness, the chart’s performance is measured through concise run-length profiles generated from Monte Carlo simulations. A notable aspect is the incorporation of an unbiased estimator in computing the smoothing constant through the suggested function, thereby improving the chart’s capability to identify different levels of increasing and decreasing shifts in process dispersion. The comparison with an established adaptive EWMA-S2 dispersion chart highlights the considerable efficiency of the proposed chart in addressing diverse magnitudes of process dispersion shifts. Additionally, the study includes an application to a real-life dataset, showcasing the practicality and user-friendly nature of the proposed chart in real-world situations.