Search results for: storage device

Authors: Atish Bagchi, Siva Chandrasekaran

Abstract:

Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: M. Agostini, A. Sonato, G. Greco, M. Travagliati, G. Ruffato, E. Gazzola, D. Liuni, F. Romanato, M. Cecchini

Abstract:

Since their first demonstration in the early 1980s, surface plasmon resonance (SPR) sensors have been widely recognized as useful tools for detecting chemical and biological species, and the interest of the scientific community toward this technology has known a rapid growth in the past two decades owing to their high sensitivity, label-free operation and possibility of real-time detection. Recent works have suggested that a turning point in SPR sensor research would be the combination of SPR strategies with other technologies in order to reduce human handling of samples, improve integration and plasmonic sensitivity. In this light, microfluidics has been attracting growing interest. By properly designing microfluidic biochips it is possible to miniaturize the analyte-sensitive areas with an overall reduction of the chip dimension, reduce the liquid reagents and sample volume, improve automation, and increase the number of experiments in a single biochip by multiplexing approaches. However, as the fluidic channel dimensions approach the micron scale, laminar flows become dominant owing to the low Reynolds numbers that typically characterize microfluidics. In these environments mixing times are usually dominated by diffusion, which can be prohibitively long and lead to long-lasting biochemistry experiments. An elegant method to overcome these issues is to actively perturb the liquid laminar flow by exploiting surface acoustic waves (SAWs). With this work, we demonstrate a new approach for SPR biosensing based on the combination of microfluidics, SAW-induced mixing and the real-time phase-interrogation grating-coupling SPR technology. On a single lithium niobate (LN) substrate the nanostructured SPR sensing areas, interdigital transducer (IDT) for SAW generation and polydimethylsiloxane (PDMS) microfluidic chambers were fabricated. SAWs, impinging on the microfluidic chamber, generate acoustic streaming inside the fluid, leading to chaotic advection and thus improved fluid mixing, whilst analytes binding detection is made via SPR method based on SPP excitation via gold metallic grating upon azimuthal orientation and phase interrogation. Our device has been fully characterized in order to separate for the very first time the unwanted SAW heating effect with respect to the fluid stirring inside the microchamber that affect the molecules binding dynamics. Avidin/biotin assay and thiol-polyethylene glycol (bPEG-SH) were exploited as model biological interaction and non-fouling layer respectively. Biosensing kinetics time reduction with SAW-enhanced mixing resulted in a ≈ 82% improvement for bPEG-SH adsorption onto gold and ≈ 24% for avidin/biotin binding—≈ 50% and 18% respectively compared to the heating only condition. These results demonstrate that our biochip can significantly reduce the duration of bioreactions that usually require long times (e.g., PEG-based sensing layer, low concentration analyte detection). The sensing architecture here proposed represents a new promising technology satisfying the major biosensing requirements: scalability and high throughput capabilities. The detection system size and biochip dimension could be further reduced and integrated; in addition, the possibility of reducing biological experiment duration via SAW-driven active mixing and developing multiplexing platforms for parallel real-time sensing could be easily combined. In general, the technology reported in this study can be straightforwardly adapted to a great number of biological system and sensing geometry.

Keywords: biosensor, microfluidics, surface acoustic wave, surface plasmon resonance

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Authors: Istvan Csarnovics, Attila Bonyar, Miklos Veres, Laszlo Himics, Attila Csik, Judit Kaman, Julia Burunkova, Geza Szanto, Laszlo Balazs, Sandor Kokenyesi

Abstract:

In this work, the performance of gold nanoparticles were investigated for stimulation of photosensitive materials for photonic applications. It was widely used for surface plasmon resonance experiments, not in the last place because of the manifestation of optical resonances in the visible spectral region. The localized surface plasmon resonance is rather easily observed in nanometer-sized metallic structures and widely used for measurements, sensing, in semiconductor devices and even in optical data storage. Firstly, gold nanoparticles on silica glass substrate satisfy the conditions for surface plasmon resonance in the green-red spectral range, where the chalcogenide glasses have the highest sensitivity. The gold nanostructures influence and enhance the optical, structural and volume changes and promote the exciton generation in gold nanoparticles/chalcogenide layer structure. The experimental results support the importance of localized electric fields in the photo-induced transformation of chalcogenide glasses as well as suggest new approaches to improve the performance of these optical recording media. Results may be utilized for direct, micrometre- or submicron size geometrical and optical pattern formation and used also for further development of the explanations of these effects in chalcogenide glasses. Besides of that, gold nanoparticles could be added to the organic light-sensitive material. The acrylate-based materials are frequently used for optical, holographic recording of optoelectronic elements due to photo-stimulated structural transformations. The holographic recording process and photo-polymerization effect could be enhanced by the localized plasmon field of the created gold nanostructures. Finally, gold nanoparticles widely used for electrochemical and optical sensor applications. Although these NPs can be synthesized in several ways, perhaps one of the simplest methods is the thermal annealing of pre-deposited thin films on glass or silicon surfaces. With this method, the parameters of the annealing process (time, temperature) and the pre-deposited thin film thickness influence and define the resulting size and distribution of the NPs on the surface. Localized surface plasmon resonance (LSPR) is a very sensitive optical phenomenon and can be utilized for a large variety of sensing purposes (chemical sensors, gas sensors, biosensors, etc.). Surface-enhanced Raman spectroscopy (SERS) is an analytical method which can significantly increase the yield of Raman scattering of target molecules adsorbed on the surface of metallic nanoparticles. The sensitivity of LSPR and SERS based devices is strongly depending on the used material and also on the size and geometry of the metallic nanoparticles. By controlling these parameters the plasmon absorption band can be tuned and the sensitivity can be optimized. The technological parameters of the generated gold nanoparticles were investigated and influence on the SERS and on the LSPR sensitivity was established. The LSPR sensitivity were simulated for gold nanocubes and nanospheres with MNPBEM Matlab toolbox. It was found that the enhancement factor (which characterize the increase in the peak shift for multi-particle arrangements compared to single-particle models) depends on the size of the nanoparticles and on the distance between the particles. This work was supported by GINOP- 2.3.2-15-2016-00041 project, which is co-financed by the European Union and European Social Fund. Istvan Csarnovics is grateful for the support through the New National Excellence Program of the Ministry of Human Capacities, supported by the ÚNKP-17-4 Attila Bonyár and Miklós Veres are grateful for the support of the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Keywords: light sensitive nanocomposites, metallic nanoparticles, photonic application, plasmonic nanostructures

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Authors: Rajkumar Ghosh, Bhabani Prasad Mukhopadhay, Ananya Mukhopadhyay, Harendra Nath Bhattacharya

Abstract:

This study investigates the global issue of electronic waste (e-waste), focusing on its prevalence in India and other regions. E-waste has emerged as a significant worldwide problem, with India contributing a substantial share of annual e-waste generation. The primary sources of e-waste in India are computer equipment and mobile phones. Many developed nations utilize India as a dumping ground for their e-waste, with major contributions from the United States, China, Europe, Taiwan, South Korea, and Japan. The study identifies Maharashtra, Tamil Nadu, Mumbai, and Delhi as prominent contributors to India's e-waste crisis. This issue is contextualized within the broader framework of the United Nations' 2030 Agenda for Sustainable Development, which encompasses 17 Sustainable Development Goals (SDGs) and 169 associated targets to address poverty, environmental preservation, and universal prosperity. The study underscores the interconnectedness of e-waste management with several SDGs, including health, clean water, economic growth, sustainable cities, responsible consumption, and ocean conservation. Central Pollution Control Board (CPCB) data reveals that e-waste generation surpasses that of plastic waste, increasing annually at a rate of 31%. However, only 20% of electronic waste is recycled through organized and regulated methods in underdeveloped nations. In Europe, efficient e-waste management stands at just 35%. E-waste pollution poses serious threats to soil, groundwater, and public health due to toxic components such as mercury, lead, bromine, and arsenic. Long-term exposure to these toxins, notably arsenic in microchips, has been linked to severe health issues, including cancer, neurological damage, and skin disorders. Lead exposure, particularly concerning for children, can result in brain damage, kidney problems, and blood disorders. The study highlights the problematic transboundary movement of e-waste, with approximately 352,474 metric tonnes of electronic waste illegally shipped from Europe to developing nations annually, mainly to Africa, including Nigeria, Ghana, and Tanzania. Effective e-waste management, underpinned by appropriate infrastructure, regulations, and policies, offers opportunities for job creation and aligns with the objectives of the 2030 Agenda for SDGs, especially in the realms of decent work, economic growth, and responsible production and consumption. E-waste represents hazardous pollutants and valuable secondary resources, making it a focal point for anthropogenic resource exploitation. The United Nations estimates that e-waste holds potential secondary raw materials worth around 55 billion Euros. The study also identifies numerous challenges in e-waste management, encompassing the sheer volume of e-waste, child labor, inadequate legislation, insufficient infrastructure, health concerns, lack of incentive schemes, limited awareness, e-waste imports, high costs associated with recycling plant establishment, and more. To mitigate these issues, the study offers several solutions, such as providing tax incentives for scrap dealers, implementing reward and reprimand systems for e-waste management compliance, offering training on e-waste handling, promoting responsible e-waste disposal, advancing recycling technologies, regulating e-waste imports, and ensuring the safe disposal of domestic e-waste. A mechanism, Buy-Back programs, will compensate customers in cash when they deposit unwanted digital products. This E-waste could contain any portable electronic device, such as cell phones, computers, tablets, etc. Addressing the e-waste predicament necessitates a multi-faceted approach involving government regulations, industry initiatives, public awareness campaigns, and international cooperation to minimize environmental and health repercussions while harnessing the economic potential of recycling and responsible management.

Keywords: e-waste management, sustainable development goal, e-waste disposal, recycling technology, buy-back policy

Procedia PDF Downloads 64