Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 17

Search results for: Chandrasekaran

17 Heavy Metal Pollution in Soils of Yelagirihills,Tamilnadu by EDXRF Technique

Authors: Chandrasekaran, Ravisankar N. Harikrishnan, Rajalakshmi, K. K. Satapathy M. V. R. Prasad, K. V. Kanagasabapathy

Abstract:

Heavy metals were considered as highly toxic environmental pollutants to soil ecosystem and human health. In present study the 12 heavy metals (Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co,Ni and Zn.) are determined in soils of Yelagiri hills, Tamilnadu by energy dispersive X-ray fluorescence technique. Metal concentrations were used to quantify pollution contamination factors such as enrichment factor (EF), geo-accumulation index (Igeo) and contamination factor (CF) are calculated and reported.

Keywords: soil, heavy metals, EDXRF, pollution contamination factors

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16 Heavy Metal Contamination in Sediments of North East Coast of Tamilnadu by EDXRF Technique

Authors: R. Ravisankar, Tholkappian A. Chandrasekaran, Y. Raghu, K. K. Satapathy, M. V. R. Prasad, K. V. Kanagasabapathy

Abstract:

The coastal areas of Tamilnadu are assuming greater importance owing to increasing human population, urbanization and accelerated industrial activities. sIn the present study, sediment samples are collected along the east coast of Tamilnadu for assessment of heavy metal pollution. The concentration of 13 selected heavy metals such as Mg, Al, Si, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni and Zn determined by Energy dispersive X-ray fluorescence (EDXRF) technique. In order to describe the pollution status, Contamination factor and pollution load index are calculated and reported. This result suggests that sources of metal contamination were mainly attributed to natural inputs from surrounding environments.

Keywords: sediments, heavy metals, EDXRF, pollution contamination factors

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15 Computational Chemical-Composition of Carbohydrates in the Context of Healthcare Informatics

Authors: S. Chandrasekaran, S. Nandita, M. Shivathmika, Srikrishnan Shivakumar

Abstract:

The objective of the research work is to analyze the computational chemical-composition of carbohydrates in the context of healthcare informatics. The computation involves the representation of complex chemical molecular structure of carbohydrate using graph theory and in a deployable Chemical Markup Language (CML). The parallel molecular structure of the chemical molecules with or without other adulterants for the sake of business profit can be analyzed in terms of robustness and derivatization measures. The rural healthcare program should create awareness in malnutrition to reduce ill-effect of decomposition and help the consumers to know the level of such energy storage mixtures in a quantitative way. The earlier works were based on the empirical and wet data which can vary from time to time but cannot be made to reuse the results of mining. The work is carried out on the quantitative computational chemistry on carbohydrates to provide a safe and secure right to food act and its regulations.

Keywords: carbohydrates, chemical-composition, chemical markup, robustness, food safety

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14 A Simplified, Fabrication-Friendly Acoustophoretic Model for Size Sensitive Particle Sorting

Authors: V. Karamzadeh, J. Adhvaryu, A. Chandrasekaran, M. Packirisamy

Abstract:

In Bulk Acoustic Wave (BAW) microfluidics, the throughput of particle sorting is dependent on the complex interplay between the geometric configuration of the channel, the size of the particles, and the properties of the fluid medium, which therefore calls for a detailed modeling and understanding of the fluid-particle interaction dynamics under an acoustic field, prior to designing the system. In this work, we propose a simplified Bulk acoustophoretic system that can be used for size dependent particle sorting. A Finite Element Method (FEM) based analytical model has been developed to study the dependence of particle sizes on channel parameters, and the sorting efficiency in a given fluid medium. Based on the results, the microfluidic system has been designed to take into account all the variables involved with the underlying physics, and has been fabricated using an additive manufacturing technique employing a commercial 3D printer, to generate a simple, cost-effective system that can be used for size sensitive particle sorting.

Keywords: 3D printing, 3D microfluidic chip, acoustophoresis, cell separation, MEMS (Microelectromechanical Systems), microfluidics

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13 Numerical Analysis on Triceratops Restraining System: Failure Conditions of Tethers

Authors: Srinivasan Chandrasekaran, Manda Hari Venkata Ramachandra Rao

Abstract:

Increase in the oil and gas exploration in ultra deep-water demands an adaptive structural form of the platform. Triceratops has superior motion characteristics compared to that of the Tension Leg Platform and Single Point Anchor Reservoir platforms, which is well established in the literature. Buoyant legs that support the deck are position-restrained to the sea bed using tethers with high axial pretension. Environmental forces that act on the platform induce dynamic tension variations in the tethers, causing the failure of tethers. The present study investigates the dynamic response behavior of the restraining system of the platform under the failure of a single tether of each buoyant leg in high sea states. Using the rain-flow counting algorithm and the Goodman diagram, fatigue damage caused to the tethers is estimated, and the fatigue life is predicted. Results shows that under failure conditions, the fatigue life of the remaining tethers is quite alarmingly low.

Keywords: fatigue life, pm spectrum, rain flow counting, triceratops, failure analysis

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12 Analysis and Design of Offshore Triceratops under Ultra-Deep Waters

Authors: Srinivasan Chandrasekaran, R. Nagavinothini

Abstract:

Offshore platforms for ultra-deep waters are form-dominant by design; hybrid systems with large flexibility in horizontal plane and high rigidity in vertical plane are preferred due to functional complexities. Offshore triceratops is relatively a new-generation offshore platform, whose deck is partially isolated from the supporting buoyant legs by ball joints. They allow transfer of partial displacements of buoyant legs to the deck but restrain transfer of rotational response. Buoyant legs are in turn taut-moored to the sea bed using pre-tension tethers. Present study will discuss detailed dynamic analysis and preliminary design of the chosen geometric, which is necessary as a proof of validation for such design applications. A detailed numeric analysis of triceratops at 2400 m water depth under random waves is presented. Preliminary design confirms member-level design requirements under various modes of failure. Tether configuration, proposed in the study confirms no pull-out of tethers as stress variation is comparatively lesser than the yield value. Presented study shall aid offshore engineers and contractors to understand suitability of triceratops, in terms of design and dynamic response behaviour.

Keywords: offshore structures, triceratops, random waves, buoyant legs, preliminary design, dynamic analysis

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11 Mathieu Stability of Offshore Buoyant Leg Storage and Regasification Platform

Authors: S. Chandrasekaran, P. A. Kiran

Abstract:

Increasing demand for large-sized Floating, Storage and Regasification Units (FSRUs) for oil and gas industries led to the development of novel geometric form of Buoyant Leg Storage and Regasification Platform (BLSRP). BLSRP consists of a circular deck supported by six buoyant legs placed symmetrically with respect to wave direction. Circular deck is connected to buoyant legs using hinged joints, which restrain transfer of rotational response from the legs to deck and vice-versa. Buoyant legs are connected to seabed using taut moored system with high initial pretension, enabling rigid body motion in vertical plane. Encountered environmental loads induce dynamic tether tension variations, which in turn affect stability of the platform. The present study investigates Mathieu stability of BLSRP under the postulated tether pullout cases by inducing additional tension in the tethers. From the numerical studies carried out, it is seen that postulated tether pullout on any one of the buoyant legs does not result in Mathieu type instability even under excessive tether tension. This is due to the presence of hinged joints, which are capable of dissipating the unbalanced loads to other legs. However, under tether pullout of consecutive buoyant legs, Mathieu-type instability is observed.

Keywords: offshore platforms, stability, postulated failure, dynamic tether tension

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10 Improving Perceptual Reasoning in School Children through Chess Training

Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

Abstract:

Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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9 Structural Health Monitoring of Offshore Structures Using Wireless Sensor Networking under Operational and Environmental Variability

Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

Abstract:

The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network

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8 Identification of High-Rise Buildings Using Object Based Classification and Shadow Extraction Techniques

Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

Abstract:

Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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7 Polymerization of Epsilon-Caprolactone Using Lipase Enzyme for Medical Applications

Authors: Sukanya Devi Ramachandran, Vaishnavi Muralidharan, Kavya Chandrasekaran

Abstract:

Polycaprolactone is polymer belonging to the polyester family that has noticeable characteristics of biodegradability and biocompatibility which is essential for medical applications. Polycaprolactone is produced by the ring opening polymerization of the monomer epsilon-Caprolactone (ε-CL) which is a closed ester, comprising of seven-membered ring. This process is normally catalysed by metallic components such as stannous octoate. It is difficult to remove the catalysts after the reaction, and they are also toxic to the human body. An alternate route of using enzymes as catalysts is being employed to reduce the toxicity. Lipase enzyme is a subclass of esterase that can easily attack the ester bonds of ε-CL. This research paper throws light on the extraction of lipase from germinating sunflower seeds and the activity of the biocatalyst in the polymerization of ε-CL. Germinating Sunflower seeds were crushed with fine sand in phosphate buffer of pH 6.5 into a fine paste which was centrifuged at 5000rpm for 10 minutes. The clear solution of the enzyme was tested for activity at various pH ranging from 5 to 7 and temperature ranging from 40oC to 70oC. The enzyme was active at pH6.0 and at 600C temperature. Polymerization of ε-CL was done using toluene as solvent with the catalysis of lipase enzyme, after which chloroform was added to terminate the reaction and was washed in cold methanol to obtain the polymer. The polymerization was done by varying the time from 72 hours to 6 days and tested for the molecular weight and the conversion of the monomer. The molecular weight obtained at 6 days is comparably higher. This method will be very effective, economical and eco-friendly to produce as the enzyme used can be regenerated as such at the end of the reaction and can be reused. The obtained polymers can be used for drug delivery and other medical applications.

Keywords: lipase, monomer, polycaprolactone, polymerization

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6 Hydrodynamic Performance of a Moored Barge in Irregular Wave

Authors: Srinivasan Chandrasekaran, Shihas A. Khader

Abstract:

Motion response of floating structures is of great concern in marine engineering. Nonlinearity is an inherent property of any floating bodies subjected to irregular waves. These floating structures are continuously subjected to environmental loadings from wave, current, wind etc. This can result in undesirable motions of the vessel which may challenge the operability. For a floating body to remain in its position, it should be able to induce a restoring force when displaced. Mooring is provided to enable this restoring force. This paper discuss the hydrodynamic performance and motion characteristics of an 8 point spread mooring system applied to a pipe laying barge operating in the West African sea. The modelling of the barge is done using a computer aided-design (CAD) software RHINOCEROS. Irregular waves are generated using a suitable wave spectrum. Both frequency domain and time domain analysis is done. Numerical simulations based on potential theory are carried out to find the responses and hydrodynamic performance of the barge in both free floating as well as moored conditions. Initially, potential flow frequency domain analysis is done to obtain the Response Amplitude Operator (RAO) which gives an idea about the structural motion in free floating state. RAOs for different wave headings are analyzed. In the following step, a time domain analysis is carried out to obtain the responses of the structure in the moored condition. In this study, wave induced motions are only taken into consideration. Wind and current loads are ruled out and shall be included in future studies. For the current study, 5000 seconds simulation is taken. The results represent wave-induced motion responses, mooring line tensions and identifies critical mooring lines.

Keywords: irregular wave, moored barge, time domain analysis, numerical simulation

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5 Development and Evaluation of a Calcium Rich Plant-Based Supplement on Bone Turnover of Peri and Post Menopausal Women

Authors: Gayathri.G, Hemamalini.A.J, Chandrasekaran.A

Abstract:

Problem statement: Nutritional deficiency, especially calcium, may lead to poor bone formation and mineralization. Although there are plenty of synthetic supplements available, it is essential to make a calcium rich food supplement accessible to combat calcium deficiency that could be readily prepared at the household level. Thus the current study aimed to formulate and standardize an indigenous low-cost calcium-rich food supplement and to study the impact of supplementation on the bone resorption and formation markers. Methods: A Randomized controlled trial was conducted with 60 subjects distributed equally in control and experimental groups, including perimenopausal and postmenopausal women. A plant-based calcium-rich product was developed and supplemented in form of balls as a midmorning and evening snack by addition of optimized proportions of leaves of Sesbania Grandiflora, seeds of Sesamum indicum, Eleusine coracana, Glycine max, Vigna mungo for a period of 6 months. Postmenopausal and perimenopausal women received 1200mg and 800mg of calcium per day from the supplemented, respectively. Outcome measures like serum calcium; betacrosslaps (bone resorption marker) and total P1NP (bone absorption marker) were assessed after 3 months and after 6 months. Results: There were no significant changes seen in the serum calcium and total P1NP levels (bone formation marker) among the subjects during the supplementation period. The bone resorption marker (betacrosslaps) reduced in all the groups and the reduction (0.32 ± 0.130 ng/ml to 0.25 ± 0.130 ng/ml) was found to be statistically highly significant (p < 0.01) in experimental group of perimenopausal subjects and significant (p < 0.05) in experimental group of postmenopausal subjects (1.11 ± 0.290 ng/ml to 0.42 ± 0.263 ng/ml). Conclusion: With the current severe calcium deficiency in the Indian population, integrating low-cost, calcium-rich native foods that could be readily prepared at household level would be useful in raising the nutritional consumption of calcium, which would, in turn, decrease bone turnover.

Keywords: calcium, sesbania grandiflora, sesamum indicum, eleusine coracana, glycine max, vigna mungo, postmenopause, perimenopause, bone resorption, bone absorption, betacrosslaps, total P1NP

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4 Improving Working Memory in School Children through Chess Training

Authors: Veena Easvaradoss, Ebenezer Joseph, Sumathi Chandrasekaran, Sweta Jain, Aparna Anna Mathai, Senta Christy

Abstract:

Working memory refers to a cognitive processing space where information is received, managed, transformed, and briefly stored. It is an operational process of transforming information for the execution of cognitive tasks in different and new ways. Many class room activities require children to remember information and mentally manipulate it. While the impact of chess training on intelligence and academic performance has been unequivocally established, its impact on working memory needs to be studied. This study, funded by the Cognitive Science Research Initiative, Department of Science & Technology, Government of India, analyzed the effect of one-year chess training on the working memory of children. A pretest–posttest with control group design was used, with 52 children in the experimental group and 50 children in the control group. The sample was selected from children studying in school (grades 3 to 9), which included both the genders. The experimental group underwent weekly chess training for one year, while the control group was involved in extracurricular activities. Working memory was measured by two subtests of WISC-IV INDIA. The Digit Span Subtest involves recalling a list of numbers of increasing length presented orally in forward and in reverse order, and the Letter–Number Sequencing Subtest involves rearranging jumbled alphabets and numbers presented orally following a given rule. Both tasks require the child to receive and briefly store information, manipulate it, and present it in a changed format. The Children were trained using Winning Moves curriculum, audio- visual learning method, hands-on- chess training and recording the games using score sheets, analyze their mistakes, thereby increasing their Meta-Analytical abilities. They were also trained in Opening theory, Checkmating techniques, End-game theory and Tactical principles. Pre equivalence of means was established. Analysis revealed that the experimental group had significant gains in working memory compared to the control group. The present study clearly establishes a link between chess training and working memory. The transfer of chess training to the improvement of working memory could be attributed to the fact that while playing chess, children evaluate positions, visualize new positions in their mind, analyze the pros and cons of each move, and choose moves based on the information stored in their mind. If working-memory’s capacity could be expanded or made to function more efficiently, it could result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess training, cognitive development, executive functions, school children, working memory

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3 Various Shaped ZnO and ZnO/Graphene Oxide Nanocomposites and Their Use in Water Splitting Reaction

Authors: Sundaram Chandrasekaran, Seung Hyun Hur

Abstract:

Exploring strategies for oxygen vacancy engineering under mild conditions and understanding the relationship between dislocations and photoelectrochemical (PEC) cell performance are challenging issues for designing high performance PEC devices. Therefore, it is very important to understand that how the oxygen vacancies (VO) or other defect states affect the performance of the photocatalyst in photoelectric transfer. So far, it has been found that defects in nano or micro crystals can have two possible significances on the PEC performance. Firstly, an electron-hole pair produced at the interface of photoelectrode and electrolyte can recombine at the defect centers under illumination of light, thereby reducing the PEC performances. On the other hand, the defects could lead to a higher light absorption in the longer wavelength region and may act as energy centers for the water splitting reaction that can improve the PEC performances. Even if the dislocation growth of ZnO has been verified by the full density functional theory (DFT) calculations and local density approximation calculations (LDA), it requires further studies to correlate the structures of ZnO and PEC performances. Exploring the hybrid structures composed of graphene oxide (GO) and ZnO nanostructures offer not only the vision of how the complex structure form from a simple starting materials but also the tools to improve PEC performances by understanding the underlying mechanisms of mutual interactions. As there are few studies for the ZnO growth with other materials and the growth mechanism in those cases has not been clearly explored yet, it is very important to understand the fundamental growth process of nanomaterials with the specific materials, so that rational and controllable syntheses of efficient ZnO-based hybrid materials can be designed to prepare nanostructures that can exhibit significant PEC performances. Herein, we fabricated various ZnO nanostructures such as hollow sphere, bucky bowl, nanorod and triangle, investigated their pH dependent growth mechanism, and correlated the PEC performances with them. Especially, the origin of well-controlled dislocation-driven growth and its transformation mechanism of ZnO nanorods to triangles on the GO surface were discussed in detail. Surprisingly, the addition of GO during the synthesis process not only tunes the morphology of ZnO nanocrystals and also creates more oxygen vacancies (oxygen defects) in the lattice of ZnO, which obviously suggest that the oxygen vacancies be created by the redox reaction between GO and ZnO in which the surface oxygen is extracted from the surface of ZnO by the functional groups of GO. On the basis of our experimental and theoretical analysis, the detailed mechanism for the formation of specific structural shapes and oxygen vacancies via dislocation, and its impact in PEC performances are explored. In water splitting performance, the maximum photocurrent density of GO-ZnO triangles was 1.517mA/cm-2 (under UV light ~ 360 nm) vs. RHE with high incident photon to current conversion Efficiency (IPCE) of 10.41%, which is the highest among all samples fabricated in this study and also one of the highest IPCE reported so far obtained from GO-ZnO triangular shaped photocatalyst.

Keywords: dislocation driven growth, zinc oxide, graphene oxide, water splitting

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2 Machine Learning Approach for Automating Electronic Component Error Classification and Detection

Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

Abstract:

The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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1 Hybrid GNN Based Machine Learning Forecasting Model For Industrial IoT Applications

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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