Search results for: real-time data monitoring

Authors: Dauda A. Oladosu, Kamorudeen A Olaiya, Abdurahman Bello

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The deployment of smart meters by utility providers to gather fine grained spatiotemporal consumption data has grossly influenced the consumers’ emotion and behavior towards energy utilization. The quest for reduction in power consumption is now a subject of concern and one the methods adopted by the consumers to achieve this is Non-intrusive Load (appliance) Monitoring. Hence, this work presents performance Analysis of Domotics System as a tool for load monitoring when integrated with Consumer Control Unit of residential building. The system was developed with basic elements which enhance remote sensing, DTMF (Dual Tone Multi-frequency) recognition and cryptic messaging when specific task was performed. To demonstrate its applicability and suitability, this prototype was used consistently for six months at different load demands and the utilities consumed were documented. The results obtained shows good response when phone dialed, and the packet delivery of feedback SMS was quite satisfactory, making the implemented system to be of good quality with affordable cost and performs the desired functions. Besides, comparative analysis showed notable reduction in energy consumption and invariably lessened electrical bill of the consumer.

Keywords: automation, domotics, energy, load, remote, schedule

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

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Authors: Carmen E. Zapata, José F. Jiménez, Mauricio Ramiréz, Natalia A. Cano

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The majority of the urban areas in Latin America face the challenges associated with city planning and development problems, attributed to human, technical, and economical factors; therefore, we cannot ignore the issues related to climate change because the city modifies the natural landscape in a significant way transforming the radiation balance and heat content in the urbanized areas. These modifications provoke changes in the temperature distribution known as “the heat island effect”. According to this phenomenon, we have the need to conceive the urban planning based on climatological patterns that will assure its sustainable functioning, including the particularities of the climate variability. In the present study, it is identified the Local Climate Zones (LCZ) in the Metropolitan Area of the Aburrá Valley (Colombia) with the objective of relocate the air quality monitoring stations as a partial solution to the problem of how to measure representative air quality levels in a city for a local scale, but with instruments that measure in the microscale.

Keywords: air quality, monitoring, local climatic zones, valley, monitoring stations

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Authors: Davit Mirzoyan, Ararat Khachatryan

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A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

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Authors: A. Yawootti, P. Intra, P. Sardyoung, P. Phoosomma, R. Puttipattanasak, S. Leeragreephol, N. Tippayawong

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The aim of this work is to design, develop and test the low-cost implementation of a particulate air pollution sensor system for continuous monitoring of outdoors and indoors particulate air pollution at a lower cost than existing instruments. In this study, measuring electrostatic charge of particles technique via high efficiency particulate-free air filter was carried out. The developed detector consists of a PM10 impactor, a particle charger, a Faraday cup electrometer, a flow meter and controller, a vacuum pump, a DC high voltage power supply and a data processing and control unit. It was reported that the developed detector was capable of measuring mass concentration of particulate ranging from 0 to 500 µg/m3 corresponding to number concentration of particulate ranging from 106 to 1012 particles/m3 with measurement time less than 1 sec. The measurement data of the sensor connects to the internet through a GSM connection to a public cellular network. In this development, the apparatus was applied the energy by a 12 V, 7 A internal battery for continuous measurement of about 20 hours. Finally, the developed apparatus was found to be close agreement with the import standard instrument, portable and benefit for air pollution and particulate matter measurements.

Keywords: particulate, air pollution, wireless communication, sensor

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Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: R. Farhoud, G. Hermand, S. Delepine-lesoille

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Future underground facility of French radioactive waste disposal, named Cigeo, is designed to store intermediate and high level - long-lived French radioactive waste. Intermediate level waste cells are tunnel-like, about 400m length and 65 m² section, equipped with several concrete layers, which can be grouted in situ or composed of tunnel elements pre-grouted. The operating space into cells, to allow putting or removing waste containers, should be monitored for several decades without any maintenance. To provide the required information, design was performed and tested in situ in Andra’s underground laboratory (URL) at 500m under the surface. Based on distributed optic fiber sensors (OFS) and backscattered Brillouin for strain and Raman for temperature interrogation technics, the design consists of 2 loops of OFS, at 2 different radiuses, around the monitored section (Orthoradiale strains) and longitudinally. Strains measured by distributed OFS cables were compared to classical vibrating wire extensometers (VWE) and platinum probes (Pt). The OFS cables were composed of 2 cables sensitive to strains and temperatures and one only for temperatures. All cables were connected, between sensitive part and instruments, to hybrid cables to reduce cost. The connection has been made according to 2 technics: splicing fibers in situ after installation or preparing each fiber with a connector and only plugging them together in situ. Another challenge was installing OFS cables along a tunnel mad in several parts, without interruption along several parts. First success consists of the survival rate of sensors after installation and quality of measurements. Indeed, 100% of OFS cables, intended for long-term monitoring, survived installation. Few new configurations were tested with relative success. Measurements obtained were very promising. Indeed, after 3 years of data, no difference was observed between cables and connection methods of OFS and strains fit well with VWE and Pt placed at the same location. Data, from Brillouin instrument sensitive to strains and temperatures, were compensated with data provided by Raman instrument only sensitive to temperature and into a separated fiber. These results provide confidence in the next steps of the qualification processes which consists of testing several data treatment approach for direct analyses.

Keywords: monitoring, fiber optic, sensor, data treatment

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Authors: Hind Mohammed A. Asiri, Asia Mohammed Asiri, Hana Falah Alruwaili, Joseph Almazan

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With the rapid growth of the older adult population, an underlying growth of public health concern is also seen. Various technologies were developed to help mitigate the arising problems of older adults with cognitive impairment and the improvement of their cognitive functions. This scooping review used the Joanna Briggs Institute (JBI) and the PRISMA extension for scoping reviews. The eligibility criteria were defined using the Population, Concept, Context (PCC) framework, as described in the JBI’s Reviewers Manual (Peters et al.,2020). The population of interest for this review is older adults 65 years old or older. Studies involving monitoring technology devices utilized in caring with older adult with cognitive impairment. This scoping review has shown information that researchers are more focused on creating alternative and novel methods or technological devices and use these as a tool for designing interventions depending on the data of the patient. This study has shown the type of technologies that have been explored in terms of assessing, detecting, monitoring, and interventions for cognitive impairment. Thus, there is a need for this technology to be applied in the practical field to further strengthen the evidence that it could enhance the lives of older adults.

Keywords: technology devices, cognitive impairment, older adult, nursing care, caring

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Kaiyi Zhang, Wenlong Li, Haicheng Li, Yifei Luo, Zheng Li, Xiaoshi Wang, Xiaodong Chen

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Plant wearable sensors facilitate the real-time monitoring of plant physiological status. In situ monitoring of the plant chlorophyll content over days could provide valuable information on the photosynthetic capacity, nitrogen content, and general plant health. However, it cannot be achieved by current chlorophyll measuring methods. Here, a miniaturized and plant-wearable chlorophyll meter was developed for rapid, non-destructive, in situ, and long-term chlorophyll monitoring. This reflectance-based chlorophyll sensor with 1.5 mm thickness and 0.2 g weight (1000 times lighter than the commercial chlorophyll meter), includes a light emitting diode (LED) and two symmetric photodetectors (PDs) on a flexible substrate and is patched onto the leaf upper epidermis with a conformal light guiding layer. A chlorophyll content index (CCI) calculated based on this sensor shows a better linear relationship with the leaf chlorophyll content (r² > 0.9) than the traditional chlorophyll meter. This meter can wirelessly communicate with a smartphone to monitor the leaf chlorophyll change under various stresses and indicate the unhealthy status of plants for long-term application of plants under various stresses earlier than chlorophyll meter and naked-eye observation. This wearable chlorophyll sensing patch is promising in smart and precision agriculture.

Keywords: plant wearable sensors, reflectance-based measurements, chlorophyll content monitoring, smart agriculture

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Authors: Abraham Rotich

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The purpose of this study was to determine the effects of relationship banking on the financial performance of SMEs. The paper attempted to establish the link between the constructs of relationship banking and SME performance. The study was guided by relationship lending, relationship monitoring, relationship risk sharing and bundle of products as independent variables while financial performance will be the dependent variable. The study used a quasi experimental design with population being the 620 SMEs who have a relationship banking arrangement with banks in Nairobi. The study used stratified sampling to pick a sample of 235. The population of interest will be the CEOs of the respective companies. The basis of stratification is the sectors in which the SMEs operate in. The study will use a questionnaire to collect data. The questionnaire will have both open and close ended questions. A pilot study will be conducted to test reliability and validity of questionnaire. The data was analyzed using descriptive statistics. Regression analysis was employed to test if there is a relationship between the dependent and the independent variable. The study found evidence that relationship banking positively impacts on financial performance of SMEs. Specifically, the study established that each component of relationship banking in this study i.e relationship lending, monitoring, bundle of products and risk sharing positively affects financial performance.

Keywords: relationship banking, SMEs, financial performance, entrepreneurial orientation

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Authors: Adriana de Paula Lacerda Santos, Bruna Godke, Mauro Lacerda Santos Filho

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Several dams in the world have already collapsed, causing environmental, social and economic damage. The concern to avoid future disasters has stimulated the creation of a great number of laws and rules in many countries. In Brazil, Law 12.334/2010 was created, which establishes the National Policy on Dam Safety. Overall, this policy requires the dam owners to invest in the maintenance of their structures and to improve its monitoring systems in order to provide faster and straightforward responses in the case of an increase of risks. As monitoring tools, visual inspections has provides comprehensive assessment of the structures performance, while auscultation’s instrumentation has added specific information on operational or behavioral changes, providing an alarm when a performance indicator exceeds the acceptable limits. These limits can be set using statistical methods based on the relationship between instruments measures and other variables, such as reservoir level, time of the year or others instruments measuring. Besides the design parameters (uplift of the foundation, displacements, etc.) the dam instrumentation can also be used to monitor the behavior of defects and damage manifestations. Specifically in concrete gravity dams, one of the main causes for the appearance of cracks, are the concrete volumetric changes generated by the thermal origin phenomena, which are associated with the construction process of these structures. Based on this, the goal of this research is to propose a monitoring process of the thermal cracking behavior in concrete gravity dams, through the instrumentation data analysis and the establishment of control values. Therefore, as a case study was selected the Block B-11 of José Richa Governor Dam Power Plant, that presents a cracking process, which was identified even before filling the reservoir in August’ 1998, and where crack meters and surface thermometers were installed for its monitoring. Although these instruments were installed in May 2004, the research was restricted to study the last 4.5 years (June 2010 to November 2014), when all the instruments were calibrated and producing reliable data. The adopted method is based on simple linear correlations procedures to understand the interactions among the instruments time series, verifying the response times between them. The scatter plots were drafted from the best correlations, which supported the definition of the limit control values. Among the conclusions, it is shown that there is a strong or very strong correlation between ambient temperature and the crack meters and flowmeters measurements. Based on the results of the statistical analysis, it was possible to develop a tool for monitoring the behavior of the case study cracks. Thus it was fulfilled the goal of the research to develop a proposal for a monitoring process of the behavior of thermal cracking in concrete gravity dams.

Keywords: concrete gravity dam, dams safety, instrumentation, simple linear correlation

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Authors: Ahmad Badawi Saluy

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This study aims to develop strategies for the prevention or elimination of environmental pollution as well as changes in external variables of the environment in order to implement the environmental management system ISO 14001:2004 by integrating analysis of environmental issues data, RKL-RPL transactional data and regulation as part of ERP on the management dashboard. This research uses a quantitative descriptive approach with analysis method comparing with air quality standard (PP 42/1999, LH 21/2008), water quality standard (permenkes RI 416/1990, KepmenLH 51/2004, kepmenLH 55/2013 ), and biodiversity indicators. Based on the research, the parameters of RPL monitoring have been identified, among others, the quality of emission air (SO₂, NO₂, dust, particulate) due to the influence of fuel quality, combustion performance in a combustor and the effect of development change around the generating area. While in water quality (TSS, TDS) there was an increase due to the flow of water in the cooling intake carrying sedimentation from the flow of Banjir Kanal Timur. Including compliance with the ISO 14001:2004 clause on application design significantly contributes to the improvement of the quality of power plant management.

Keywords: environmental management systems, power plant management, regulatory compliance , enterprises resource planning

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Authors: Lea B. Milan

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This descriptive study evaluated the existing Food Safety Management in Central Elementary School Canteens of Region 3. It made used of survey questionnaires, interview guide questions and validated knowledge test on food for data gathering. Results of the study revealed that school principals and canteen managers shared responsibilities in food safety management of school canteen. It also showed that the schools applied different methods of communication, monitoring and evaluation of food safety management. The study further revealed that implementation of monitoring and evaluation of food safety compliance are not being practiced in all elementary schools in the region. The study also showed that school canteens in the Region 3 do not have the thermometers and timers to use to conduct proper monitoring of foods during storage, preparation and serving. It was also found out from the study that canteen personnel lacks the basic knowledge and trainings on food safety. Potential source of physical, chemical and biological hazards that could contaminate foods were also found present in the canteen facilities of the elementary schools in the region. Moreover, evaluation showed that the existing implementation of food safety management in the Central Elementary School Canteens of Region 3 were below the expected level and the need to strengthen the appreciation and advocacy on food safety management in school canteens of Region 3 is still wanting.

Keywords: food safety management, food safety school catering, food safety, school food safety management

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Authors: John Mark Payawal, Francis Aldrine Uy, John Paul Carreon

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This paper shows the application of Structural Health Monitoring, SHM into bridges. Bridges are structures built to provide passage over a physical obstruction such as rivers, chasms or roads. The Philippines has a total of 8,166 national bridges as published on the 2015 atlas of the Department of Public Works and Highways (DPWH) and only 2,924 or 35.81% of these bridges are in good condition. As a result, PHP 30.464 billion of the 2016 budget of DPWH is allocated on roads and/or bridges maintenance alone. Intensive spending is owed to the present practice of outdated manual inspection and assessment, and poor structural health monitoring of Philippine infrastructures. As the School of Civil, Environmental, & Geological Engineering of Mapua Institute of Technology (MIT) continuous its well driven passion in research based projects, a partnership with the Department of Science and Technology (DOST) and the DPWH launched the application of Structural Health Monitoring, (SHM) in Padre Jacinto Zamora Flyover. The flyover is located along Nagtahan Boulevard in Sta. Mesa, Manila that connects Brgy. 411 and Brgy. 635. It gives service to vehicles going from Lacson Avenue to Mabini Bridge passing over Legarda Flyover. The flyover is chosen among the many located bridges in Metro Manila as the focus of the pilot testing due to its site accessibility, and complete structural built plans and specifications necessary for SHM as provided by the Bureau of Design, BOD department of DPWH. This paper focuses on providing a method to calibrate theoretical readings from STAAD Vi8 Pro and sync the data to actual MEMS accelerometer readings. It is observed that while the design standards used in constructing the flyover was reflected on the model, actual readings of MEMS accelerometer display a large difference compared to the theoretical data ran and taken from STAAD Vi8 Pro. In achieving a true seismic response of the modeled bridge or hence syncing the theoretical data to the actual sensor reading also called as the independent variable of this paper, analysis using single degree of freedom (SDOF) of the flyover under free vibration without damping using STAAD Vi8 Pro is done. The earthquake excitation and bridge responses are subjected to earthquake ground motion in the form of ground acceleration or Peak Ground Acceleration, PGA. Translational acceleration load is used to simulate the ground motion of the time history analysis acceleration record in STAAD Vi8 Pro.

Keywords: accelerometer, analysis using single degree of freedom, micro electro mechanical system, peak ground acceleration, structural health monitoring

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Authors: Marilena Ianculescu, Adriana Alexandru

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Internet of Health Things (IoHT) has already proved to be a persuasive means to support a proper assessment of the living conditions by collecting a huge variety of data. For a customized health management of a senior patient, IoHT provides the capacity to build a dynamic solution for sustaining the shift inside the patient-physician relationship by allowing a real-time and continuous remote monitoring of the health status, well-being, safety and activities of the senior, especially in a non-clinical environment. Thus, is created a win-win solution in which both the patient and the physician enhance their involvement and shared decision-making, with significant outcomes. Health monitoring systems in smart environments are becoming a viable alternative to traditional healthcare solutions. The ongoing “Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)” project aims to demonstrate that the existence of complete and accurate information is critical for assessing the health condition of the seniors, improving wellbeing and quality of life in relation to health. The researches performed inside the project aim to highlight how the management of IoHT devices connected to the RO-SmartAgeing platform in a secure way by using a role-based access control system, can allow the physicians to provide health services at a high level of efficiency and accessibility, which were previously only available in hospitals. The project aims to identify deficient aspects in the provision of health services tailored to a senior patient’s specificity and to offer a more comprehensive perspective of proactive and preventive medical acts.

Keywords: health management, internet of health things, remote monitoring, senior patient

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

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Authors: Mojtaba Valinejadshoubi, Ashutosh Bagchi, Osama Moselhi

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Structures such as buildings, bridges, dams, wind turbines etc. need to be maintained against various factors such as deterioration, excessive loads, environment, temperature, etc. Choosing an appropriate monitoring system is important for determining any critical damage to a structure and address that to avoid any adverse consequence. Structural Health Monitoring (SHM) has emerged as an effective technique to monitor the health of the structures. SHM refers to an ongoing structural performance assessment using different kinds of sensors attached to or embedded in the structures to evaluate their integrity and safety to help engineers decide on rehabilitation measures. Ability of SHM in identifying the location and severity of structural damages by considering any changes in characteristics of the structures such as their frequency, stiffness and mode shapes helps engineers to monitor the structures and take the most effective corrective actions to maintain their safety and extend their service life. The main objective of this study is to review the overall SHM process specifically determining the natural frequency of an instrumented simply-supported concrete beam using modal testing and finite element model updating.

Keywords: structural health monitoring, natural frequency, modal analysis, finite element model updating

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Authors: Hatim Laalej, Jon Stammers

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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.

Keywords: machining, manufacturing, tool wear, signal processing

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Authors: Haggag H. Mohamed, Ghatass F. Zekry, Shalaby A. Elsayed

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Most sources of air pollution in Egypt are of anthropogenic origin. Alexandria Governorate is located at north of Egypt. The main contributing sectors of air pollution in Alexandria are industry, transportation and area source due to human activities. Alexandria includes more than 40% of the industrial activities in Egypt. Cement manufacture contributes a significant amount to the particulate pollution load. Alexandria Portland Cement Company (APCC) surrounding was selected to be the study area. APCC main kiln stack Total Suspended Particulate (TSP) continuous monitoring data was collected for assessment of dust emission control technology. Electro Static Precipitator (ESP) was fixed on the cement kiln since 2002. The collected data of TSP for first quarter of 2012 was compared to that one in first quarter of 2013 after installation of new bag house filter. In the present study, based on these monitoring data and metrological data a detailed air dispersion modeling investigation was carried out using the Industrial Source Complex Short Term model (ISC3-ST) to find out the impact of applying new bag house filter control technology on the neighborhood ambient air quality. The model results show a drastic reduction of the ambient TSP hourly average concentration from 44.94μg/m3 to 5.78μg/m3 which assures the huge positive impact on the ambient air quality by applying bag house filter technology on APCC cement kiln

Keywords: air pollution modeling, ambient air quality, baghouse filter, cement industry

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Authors: Heba Abdelmotaal

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This paper explores the impact of the extent of a company's environmental, social, and governance (ESG) disclosure on credit rating and capital structure. The analysis is based on a sample of 202 firms from the 350 FTSE firms over the period of 2008-2013. ESG disclosure score is measured using Proprietary Bloomberg score based on the extent of a company's Environmental, Social, and Governance (ESG) disclosure. The credit rating is measured by The QuiScore, which is a measure of the likelihood that a company will become bankrupt in the twelve months following the date of calculation. The Capital Structure is measured by long term debt ratio. Two hypotheses are test using panel data regression. The results suggested that the higher degree of ESG disclosure leads to better credit rating. There is significant negative relationship between ESG disclosure and the long term debit percentage. The paper includes implications for the transparency which is resulting of the ESG disclosure could support the Monitoring Function. The monitoring role of disclosure is the increasing in the transparency of the credit rating agencies, also it could affect on managers’ actions. This study provides empirical evidence on the material of ESG disclosure on credit ratings changes and the firms’ capital decision making.

Keywords: capital structure, credit rating agencies, ESG disclosure, panel data regression

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Authors: Rahul Kumar Singh, Apurva Sharma, Dilip Kumar Srivastava

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Tilting of our interest towards unconventional exploitation of hydrocarbons has raised a serious concern to environmentalists. Emerging technologies like horizontal/multi-lateral drilling with subsequent hydraulic fracturing or fracking etc., for exploitation of different conventional/unconventional hydrocarbon reservoirs, are related to creating micro-level seismic events below the surface of the earth. Monitoring of these micro-level seismic events is not possible by the conventional methodology of the seismic method. So, to tackle this issue, a new technology that is microseismic is very much in discussions around the globe. Multiple researches are being carried out these days around the globe in order to prove microseismic as a new essential in the E & P industry, especially for unconventional reservoir management. Microseismic monitoring is now used for reservoir surveillance, and the best application is checking the integrity of the caprock and containment of fluid in it. In general, in whatever terms we want to use micro-seismic related events monitoring and understanding the effectiveness of stimulation, this technology offers a lot of value in terms of insight into the subsurface characteristics and processes, and this makes it really a good geophysical method to be used in future.

Keywords: microseismic, monitoring, hydraulic fracturing or fracking, reservoir surveillance, seismic hazards

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Authors: Natasha Correa Vitória Bandeira, Lais Cordeiro Soares, Claudineia Brazil, Luciane Teresa Salvi

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The intense rain event that occurred between February 16 and 18, 2018, in the city of Barcarena in Pará, located in the North region of Brazil, demonstrates the importance of analyzing this type of event. The metropolitan mesoregion of Belem was severely punished by rains much above the averages normally expected for that time of year; this phenomenon affected, in addition to the capital, the municipalities of Barcarena, Murucupi and Muruçambá. Resulting in a great flood in the rivers of the region, whose basins were affected with great intensity of precipitation, causing concern for the local population because in this region, there are located companies that accumulate ore tailings, and in this specific case, the dam of any of these companies, leaching the ore to the water bodies of the Murucupi River Basin. This article aims to characterize this phenomenon through a special analysis of the distribution of rainfall, using data from atmospheric soundings, satellite images, radar images and data from the GPCP (Global Precipitation Climatology Project), in addition to rainfall stations located in the study region. The results of the work demonstrated a dissociation between the data measured in the meteorological stations and the other forms of analysis of this extreme event. Monitoring carried out solely on the basis of data from pluviometric stations is not sufficient for monitoring and/or diagnosing extreme weather events, and investment by the competent bodies is important to install a larger network of pluviometric stations sufficient to meet the demand in a given region.

Keywords: extreme precipitation, great flood, GPCP, ore dam

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Authors: S. Aouabdi, M. Taibi

Abstract:

The supervision of chemical processes is the subject of increased development because of the increasing demands on reliability and safety. An important aspect of the safe operation of chemical process is the earlier detection of (process faults or other special events) and the location and removal of the factors causing such events, than is possible by conventional limit and trend checks. With the aid of process models, estimation and decision methods it is possible to also monitor hundreds of variables in a single operating unit, and these variables may be recorded hundreds or thousands of times per day. In the absence of appropriate processing method, only limited information can be extracted from these data. Hence, a tool is required that can project the high-dimensional process space into a low-dimensional space amenable to direct visualization, and that can also identify key variables and important features of the data. Our contribution based on powerful techniques for development of a new monitoring method based on multi-scale entropy MSE in order to characterize the behaviour of the concentrations of different gases present in synthesis and soft sensor based on PCA is applied to estimate these variables.

Keywords: ammonia synthesis, concentrations of different gases, soft sensor, multi-scale entropy, multivarite statistics

Procedia PDF Downloads 309

Authors: Veruska C. Dutra, Lucio F.M. Adorno, Mary L. G. S. Senna

Abstract:

Since it recognized the importance of planning sustainable tourism, which has been discussed effective methods of monitoring tourist. In this sense, the indicators, can transmit a set of information about complex processes, events or trends, showing up as an important monitoring tool and aid in the environmental assessment, helping to identify the progress of it and to chart future actions, contributing, so for decision making. The World Tourism Organization - WTO recognizes the importance of indicators to appraise the tourism activity in the point of view of sustainability, launching in 1995 eleven Keys Indicators of Sustainable Tourism to assist in the monitoring of tourist destinations. So we propose a case study to examine the applicability or otherwise of a monitoring methodology and aid in the understanding of tourism sustainability, analyzing the effectiveness of local indicators on the approach defined by the WTO. The study was applied to the Lagoa da Confusão City, in the state of Tocantins - North Brazil. The case study was carried out in 2006/2007, with the guiding deductive method. The indicators were measured by specific methodologies adapted to the study site, so that could generate quantitative results which could be analyzed at the proposed scale WTO (0 to 10 points). Applied indicators: Attractive Protection – AP (level of a natural and cultural attractive protection), Sociocultural Impact–SI (level of socio-cultural impacts), Waste Management - WM (level of management of solid waste generated), Planning Process-PP (trip planning level) Tourist Satisfaction-TS (satisfaction of the tourist experience), Community Satisfaction-CS (satisfaction of the local community with the development of local tourism) and Tourism Contribution to the Local Economy-TCLE (tourist level of contribution to the local economy). The city of Lagoa da Confusão was presented as an important object of study for the methodology in question, as offered condition to analyze the indicators and the complexities that arose during the research. The data collected can help discussions on the sustainability of tourism in the destination. The indicators TS, CS, WM , PP and AP appeared as satisfactory as allowed the measurement "translating" the reality under study, unlike TCLE and the SI indicators that were not seen as reliable and clear and should be reviewed and discussed for an adaptation and replication of the same. The application and study of various indicators of sustainable tourism, give better able to analyze the local tourism situation than monitor only one of the indicators, it does not demonstrate all collected data, which could result in a superficial analysis of the tourist destination.

Keywords: indicators, Lagoa da Confusão, Tocantins, Brazil, monitoring, sustainability

Procedia PDF Downloads 372

Authors: Ismayanti Magfirah, Sartohadi Junun, Samodra Guruh

Abstract:

Landslides are one of the most frequent and devastating natural disasters in Indonesia. Many studies have been conducted regarding this phenomenon. However, there is a lack of attention in the landslide inventory mapping. The natural condition (dense forest area) and the limited human and economic resources are some of the major problems in building landslide inventory in Indonesia. Considering the importance of landslide inventory data in susceptibility, hazard, and risk analysis, it is essential to generate landslide inventory based on available resources. In order to achieve this, the first thing we have to do is identify the landslides' location. The presence of Sentinel-1A and Sentinel-2A data gives new insights into land monitoring investigation. The free access, high spatial resolution, and short revisit time, make the data become one of the most trending open sources data used in landslide mapping. Sentinel-1A and Sentinel-2A data have been used broadly for landslide detection and landuse/landcover mapping. This study aims to generate landslide map by integrating Sentinel-1A and Sentinel-2A data use change detection method. The result will be validated by field investigation to make preliminary landslide inventory in the study area.

Keywords: change detection method, landslide inventory mapping, Sentinel-1A, Sentinel-2A

Procedia PDF Downloads 140

Authors: Federico Pittino, Thomas Arnold

Abstract:

The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

Procedia PDF Downloads 101

Authors: Robson C. Santos, David D. Oliveira, Matheus M. Reis, Gerson G. Cunha, Marcos A. C. Moreira

Abstract:

The project demonstrates the construction of a solar power generation, integrated inverter equipment to a "Grid-Tie" by converting direct current generated by solar panels, into alternating current, the same parameters of frequency and voltage concessionaire distribution network. The energy generated is quantified by smart metering module that transmits the information in specified periods of time to a microcontroller via GSM modem. The modem provides the measured data on the internet, using networks and cellular antennas. The monitoring, fault detection and maintenance are performed by a supervisory station. Employed board types, best inverter selection and studies about control equipment and devices have been described. The article covers and explores the global trend of implementing smart distribution electrical energy networks and the incentive to use solar renewable energy. There is the possibility of the excess energy produced by the system be purchased by the local power utility. This project was implemented in residences in the rural community of the municipality of Cabo Frio/RJ. Data could be seen through daily measurements during the month of November 2013.

Keywords: rural residence, supervisory, smart grid, solar energy

Procedia PDF Downloads 554

Authors: Gabrielle Peck, Ryan Hayes

Abstract:

This research presents the design and creation of a drone gas analyser, aimed at addressing the need for independent data collection and analysis of gas emissions during large-scale fires, particularly wasteland fires. The analyser drone, comprising a lightweight gas analysis system attached to a remote-controlled drone, enables the real-time assessment of smoke toxicity and the monitoring of gases released into the atmosphere during such incidents. The key components of the analyser unit included two gas line inlets connected to glass wool filters, a pump with regulated flow controlled by a mass flow controller, and electrochemical cells for detecting nitrogen oxides, hydrogen cyanide, and oxygen levels. Additionally, a non-dispersive infrared (NDIR) analyser is employed to monitor carbon monoxide (CO), carbon dioxide (CO₂), and hydrocarbon concentrations. Thermocouples can be attached to the analyser to monitor temperature, as well as McCaffrey probes combined with pressure transducers to monitor air velocity and wind direction. These additions allow for monitoring of the large fire and can be used for predictions of fire spread. The innovative system not only provides crucial data for assessing smoke toxicity but also contributes to fire prediction and management. The remote-controlled drone's mobility allows for safe and efficient data collection in proximity to the fire source, reducing the need for human exposure to hazardous conditions. The data obtained from the gas analyser unit facilitates informed decision-making by emergency responders, aiding in the protection of both human health and the environment. This abstract highlights the successful development of a drone gas analyser, illustrating its potential for enhancing smoke toxicity analysis and fire prediction capabilities. The integration of this technology into fire management strategies offers a promising solution for addressing the challenges associated with wildfires and other large-scale fire incidents. The project's methodology and results contribute to the growing body of knowledge in the field of environmental monitoring and safety, emphasizing the practical utility of drones for critical applications.

Keywords: fire prediction, drone, smoke toxicity, analyser, fire management

Procedia PDF Downloads 55