Search results for: continuous ambient air quality monitoring

Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

Procedia PDF Downloads 283

Authors: César M. Moreira, Marco A. Pazmiño-Hernández, Marco A. Pazmiño-Barreno, Kyle Griffin, Pratap Pullammanappallil

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An anaerobic digestion system that was completely operated on solar power (both photovoltaic and solar thermal energy), and mounted on a trailer to make it mobile, was designed and constructed. A 55-gallon batch digester was placed within a chamber that was heated by hot water pumped through a radiator. Hot water was produced by a solar thermal collector and photovoltaic panels charged a battery which operated pumps for recirculating water. It was found that the temperature in the heating chamber was maintained above ambient temperature but it follows the same trend as ambient temperature. The temperature difference between the chamber and ambient values was not constant but varied with time of day. Advantageously, the temperature difference was highest during night and early morning and lowest near noon. In winter, when ambient temperature dipped to 2 °C during early morning hours, the chamber temperature did not drop below 10 °C. Model simulations showed that even if the digester is subjected to diurnal variations of temperature (as observed in winter of a subtropical region), about 63 % of the waste that would have been processed under constant digester temperature of 38 °C, can still be processed. The cost of the digester system without the trailer was $1,800.

Keywords: anaerobic digestion, solar-mobile, rural communities, solar, hybrid

Procedia PDF Downloads 237

Authors: Norbert Magyar, Jozsef Kovacs, Peter Tanos, Balazs Trasy, Tamas Garamhegyi, Istvan Gabor Hatvani

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Water is one of the most important common resources, and as a result of urbanization, agriculture, and industry it is becoming more and more exposed to potential pollutants. The prevention of the deterioration of water quality is a crucial role for environmental scientist. To achieve this aim, the operation of monitoring networks is necessary. In general, these networks have to meet many important requirements, such as representativeness and cost efficiency. However, existing monitoring networks often include sampling sites which are unnecessary. With the elimination of these sites the monitoring network can be optimized, and it can operate more economically. The aim of this study is to illustrate the applicability of the CCDA (Combined Cluster and Discriminant Analysis) to the field of water quality monitoring and optimize the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and two surface-subsurface water systems on the watershed of Lake Neusiedl/Lake Fertő and on the Szigetköz area over a period of approximately two decades. CCDA combines two multivariate data analysis methods: hierarchical cluster analysis and linear discriminant analysis. Its goal is to determine homogeneous groups of observations, in our case sampling sites, by comparing the goodness of preconceived classifications obtained from hierarchical cluster analysis with random classifications. The main idea behind CCDA is that if the ratio of correctly classified cases for a grouping is higher than at least 95% of the ratios for the random classifications, then at the level of significance (α=0.05) the given sampling sites don’t form a homogeneous group. Due to the fact that the sampling on the Lake Neusiedl/Lake Fertő was conducted at the same time at all sampling sites, it was possible to visualize the differences between the sampling sites belonging to the same or different groups on scatterplots. Based on the results, the monitoring network of the Danube yields redundant information over certain sections, so that of 12 sampling sites, 3 could be eliminated without loss of information. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, and in the case of Lake Balaton, 5 out of 10 could be discarded. For the groundwater system of the catchment area of Lake Neusiedl/Lake Fertő all 50 monitoring wells are necessary, there is no redundant information in the system. The number of the sampling sites on the Lake Neusiedl/Lake Fertő can decrease to approximately the half of the original number of the sites. Furthermore, neighbouring sampling sites were compared pairwise using CCDA and the results were plotted on diagrams or isoline maps showing the location of the greatest differences. These results can help researchers decide where to place new sampling sites. The application of CCDA proved to be a useful tool in the optimization of the monitoring networks regarding different types of water bodies. Based on the results obtained, the monitoring networks can be operated more economically.

Keywords: combined cluster and discriminant analysis, cost efficiency, monitoring network optimization, water quality

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Cian O'Shea, Ross O'Halloran, Peter Haigh

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Wireless Sensor Networks (WSN) are Internet of Things (IoT) edge devices. They are becoming widely adopted in many industries, including health care, building energy management, and conditional monitoring. As the scale of WSN deployments increases, the cost and complexity of battery replacement and disposal become more significant and in time may become a barrier to adoption. Harvesting ambient energies provide a pathway to reducing dependence on batteries and in the future may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device were modelled including, sensors, transceiver, and microcontroller as well as the energy source components (batteries, solar cells, thermoelectric generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre characterized devices as well as add user-defined devices. The goal of this simulation tool is to predict the lifetime of a device and scope for extension using ambient energy sources.

Keywords: Wireless Sensor Network, IoT, edge device, simulation, solar cells, TEG, supercapacitor, energy harvesting

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Authors: Mohamed Chabi

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The project of helping students at risk started at the Math department in the new foundation program at Qatar University in the fall 2012 semester. The purpose was to find ways to help students who were struggling with their math courses Elementary algebra or Precalculus course due to many factors. Department had formed the Committee “students at Risk” at the start of 12-13 to assist struggling students in our math courses to get their studies on track. A mechanism was developed to support students who are at risk using a developed E-Monitoring system. E-Monitoring system was developed to manage automatically all transactions relevant to the students’ attendance, Students ‘‘warning Students’’ grading, etc. E-Monitoring System produce various statistics such as, Overall course statistics, Performance, Students at Risk… to help department to develop a higher quality of education in the Foundation Program at Math department. The mechanism was studies and evaluated. Whatever the cause, the sooner we identify students who are not performing well academically, the sooner we can provide, or direct them to the resources that are available to them. In this paper, we outline the mechanism and its effect on students’ performance. The collected data from various exams shows that students had benefited from the mechanism.

Keywords: students at risk, e-monitoring system, warning students, performance

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Authors: Maryam Kiani

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The self-sensing capability opens up new possibilities for structural health monitoring, offering real-time information on the condition and performance of constructions. The synthesis and characterization of these functional 2D material geopolymers will be explored in this study. Various fabrication techniques, including mixing, dispersion, and coating methods, will be employed to ensure uniform distribution and integration of the 2D materials within the geopolymers. The resulting composite materials will be evaluated for their mechanical strength, electrical conductivity, and sensing capabilities through rigorous testing and analysis. The potential applications of these self-sensing geopolymers are vast. They can be used in infrastructure projects, such as bridges, tunnels, and buildings, to provide continuous monitoring and early detection of structural damage or degradation. This proactive approach to maintenance and safety can significantly improve the lifespan and efficiency of constructions, ultimately reducing maintenance costs and enhancing overall sustainability. In conclusion, the development of functional 2D material geopolymers as self-sensing materials presents an exciting advancement in the construction industry. By integrating these innovative materials into structures, we can create a new generation of intelligent, self-monitoring constructions that can adapt and respond to their environment.

Keywords: 2D materials, geopolymers, electrical properties, self-sensing

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Authors: Cigdem Yangin-Gomec, Aigerim Jaxybayeva, Orhan Ince

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In this study, the anaerobic treatability of chicken manure diluted with tap water (with an influent feed ratio of 1 kg of fresh chicken manure to 6 liter of tap water) was investigated in a lab-scale anaerobic sludge bed (ASB) reactor inoculated with the granular sludge already adapted to chicken manure. The raw waste digested in this study was the manure from laying-hens having average total solids (TS) of about 30% with ca. 60% volatile content. The ASB reactor was fed semi-continuously at ambient operating temperature range (17-23^◦C) at a HRT of 13 and 26 days for about 6 months, respectively. The respective average total and soluble chemical oxygen demand (COD) removals were ca. 90% and 75%, whereas average biomethane production rate was calculated ca. 180 lt per kg of COD_removed from the ASB reactor at an average HRT of 13 days. Moreover, total suspended solids (TSS) and volatile suspended solids (VSS) in the influent were reduced more than 97%. Hence, high removals of the organic compounds with respective biogas production made anaerobic stabilization of the diluted chicken manure by ASB reactor at ambient operating temperatures viable. By this way, external heating up to 35^◦C (i.e. anaerobic processes have been traditionally operated at mesophilic conditions) could be avoided in the scope of this study.

Keywords: ambient anaerobic digestion, biogas recovery, poultry manure, renewable energy

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Authors: Chandra Prayaga, Lakshmi Prayaga, Aaron Wade, Kyle Rank, Gopi Shankar Mallu, Sri Satya, Harsha Pola

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In today's tech-driven world, where connectivity shapes our daily lives, maintaining physical and emotional health is crucial. Athletic trainers play a vital role in optimizing athletes' performance and preventing injuries. However, a shortage of trainers impacts the quality of care. This study introduces a vision-based exercise monitoring system leveraging Google's MediaPipe library for precise tracking of bicep curl exercises and simultaneous posture monitoring. We propose a three-stage methodology: landmark detection, side detection, and angle computation. Our system calculates angles at the elbow, wrist, neck, and torso to assess exercise form. Experimental results demonstrate the system's effectiveness in distinguishing between good and partial repetitions and evaluating body posture during exercises, providing real-time feedback for precise fitness monitoring.

Keywords: physical health, athletic trainers, fitness monitoring, technology driven solutions, Google’s MediaPipe, landmark detection, angle computation, real-time feedback

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Authors: E. Benga, T. Tengen, A. Alugongo

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Periodic and continuous inventory models are the two classical management tools used to handle inventories. These models have advantages and disadvantages. The implementation of both continuous (r,Q) inventory and periodic (R, S) inventory models in most manufacturing plants comes with higher cost. Such high inventory costs are due to the fact that most manufacturing plants are not flexible enough. Since demand and lead-time are two important variables of every inventory models, their effect on the flexibility of the manufacturing plant matter most. Unfortunately, these effects are not clearly understood by managers. The reason is that the decision parameters of the continuous (r, Q) inventory and periodic (R, S) inventory models are not designed to effectively deal with the issues of uncertainties such as poor manufacturing performances, delivery performance supplies performances. There is, therefore, a need to come up with a predictive and hybrid inventory model that can combine in some sense the feature of the aforementioned inventory models. A linear combination technique is used to hybridize both continuous (r, Q) inventory and periodic (R, S) inventory models. The behavior of such hybrid inventory model is described by a differential equation and then optimized. From the results obtained after simulation, the continuous (r, Q) inventory model is more effective than the periodic (R, S) inventory models in the short run, but this difference changes as time goes by. Because the hybrid inventory model is more cost effective than the continuous (r,Q) inventory and periodic (R, S) inventory models in long run, it should be implemented for strategic decisions.

Keywords: periodic inventory, continuous inventory, hybrid inventory, optimization, manufacturing plant

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Authors: Melanie Redding

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Historic widespread nitrate contamination of the Lower Yakima Valley aquifer in Washington State initiated a community-based effort to reduce nitrate concentrations to below-drinking water standards. This group commissioned studies on characterizing local nitrogen sources, deep soil assessments, drinking water, and assessing nitrate concentrations at the water table. Nitrate is the most prevalent groundwater contaminant with common sources from animal and human waste, fertilizers, plants and precipitation. It is challenging to address groundwater contamination when common sources, such as agriculture, on-site sewage systems, and animal production, are widespread. Remediation is not possible, so mitigation is essential. The Lower Yakima Valley is located over 175,000 acres, with a population of 56,000 residents. Approximately 25% of the population do not have access to safe, clean drinking water, and 20% of the population is at or below the poverty level. Agriculture is the primary economic land-use activity. Irrigated agriculture and livestock production make up the largest percentage of acreage and nitrogen load. Commodities include apples, grapes, hops, dairy, silage corn, triticale, alfalfa and cherries. These commodities are important to the economic viability of the residents of the Lower Yakima Valley, as well as Washington State. Mitigation of nitrate in groundwater is challenging. The goal is to ensure everyone has safe drinking water. There are no easy remedies due to the extensive and pervasiveness of the contamination. Monitoring at the water table indicates that 45% of the 30 spatially distributed monitoring wells exceeded the drinking water standard. This indicates that there are multiple sources that are impacting water quality. Washington State has several areas which have extensive groundwater nitrate contamination. The groundwater in these areas continues to degrade over time. However, the Lower Yakima Valley is being successful in addressing this health issue because of the following reasons: the community is engaged and committed; there is one common goal; there has been extensive public education and outreach to citizens; and generating credible data using sound scientific methods. Work in this area is continuing as an ambient groundwater monitoring network is established to assess the condition of the aquifer over time. Nitrate samples are being collected from 170 wells, spatially distributed across the aquifer. This research entails quarterly sampling for two years to characterize seasonal variability and then continue annually afterward. This assessment will provide the data to statistically determine trends in nitrate concentrations across the aquifer, over time. Thirty-three of these wells are monitoring wells that are screened across the aquifer. The water quality from these wells are indicative of activities at the land surface. Additional work is being conducted to identify land use management practices that are effective in limiting nitrate migration through the soil column. Tracking nitrate in the soil column every season is an important component of bridging land-use practices with the fate and transport of nitrate through the subsurface. Patience, tenacity, and the ability to think outside the box are essential for dealing with widespread nitrate contamination of groundwater.

Keywords: community, groundwater, monitoring, nitrate

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Authors: Raja Umer Sajjad, Chang Hee Lee

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Stormwater runoff is the leading contributor to pollution of receiving waters. In response, an efficient stormwater monitoring program is required to quantify and eventually reduce stormwater pollution. The overall goals of stormwater monitoring programs primarily include the identification of high-risk dischargers and the development of total maximum daily loads (TMDLs). The challenge in developing better monitoring program is to reduce the variability in flux estimates due to sampling errors; however, the success of monitoring program mainly depends on the accuracy of the estimates. Apart from sampling errors, manpower and budgetary constraints also influence the quality of the estimates. This study attempted to develop optimum stormwater monitoring design considering both cost and the quality of the estimated pollutants flux. Three years stormwater monitoring data (2012 – 2014) from a mix land use located within Geumhak watershed South Korea was evaluated. The regional climate is humid and precipitation is usually well distributed through the year. The investigation of a large number of water quality parameters is time-consuming and resource intensive. In order to identify a suite of easy-to-measure parameters to act as a surrogate, Principal Component Analysis (PCA) was applied. Means, standard deviations, coefficient of variation (CV) and other simple statistics were performed using multivariate statistical analysis software SPSS 22.0. The implication of sampling time on monitoring results, number of samples required during the storm event and impact of seasonal first flush were also identified. Based on the observations derived from the PCA biplot and the correlation matrix, total suspended solids (TSS) was identified as a potential surrogate for turbidity, total phosphorus and for heavy metals like lead, chromium, and copper whereas, Chemical Oxygen Demand (COD) was identified as surrogate for organic matter. The CV among different monitored water quality parameters were found higher (ranged from 3.8 to 15.5). It suggests that use of grab sampling design to estimate the mass emission rates in the study area can lead to errors due to large variability. TSS discharge load calculation error was found only 2 % with two different sample size approaches; i.e. 17 samples per storm event and equally distributed 6 samples per storm event. Both seasonal first flush and event first flush phenomena for most water quality parameters were observed in the study area. Samples taken at the initial stage of storm event generally overestimate the mass emissions; however, it was found that collecting a grab sample after initial hour of storm event more closely approximates the mean concentration of the event. It was concluded that site and regional climate specific interventions can be made to optimize the stormwater monitoring program in order to make it more effective and economical.

Keywords: first flush, pollutant load, stormwater monitoring, surrogate parameters

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Authors: A. Apostolopoulou, D. Sygkridou, A. N. Kalarakis, E. Stathatos

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Mesoscopic perovskite solar cells (mp-PSCs) with mesoporous bilayer were fabricated under ambient conditions. The bilayer was formed by capping the mesoporous TiO₂ layer with a layer of In₂O₃. CH₃NH₃I_3-xCl_x mixed halide perovskite was prepared through the one-step method and was used as the light absorber. The mp-PSCs with the composite TiO₂/In₂O₃ mesoporous layer exhibited optimized electrical parameters, compared with the PSCs that employed only a TiO₂ mesoporous layer, with a current density of 23.86 mA/cm², open circuit voltage of 0.863 V, fill factor of 0.6 and a power conversion efficiency of 11.2%. These results indicate that the formation of a proper semiconductor capping layer over the basic TiO₂ mesoporous layer can facilitate the electron transfer, suppress the recombination and subsequently lead to higher charge collection efficiency.

Keywords: ambient conditions, high efficiency solar cells, mesoscopic perovskite solar cells, TiO₂ / In₂O₃ bilayer

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Authors: Nguyen Manh Hien

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Service quality is the highest requirement from users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are many researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality but focused on a new conceptual and provides a new methodological in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, the study will suggest a key factor to evaluate e-government service quality better.

Keywords: dimensionality, e-government, e-service, e-service quality

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Authors: Ali Borhani Manesh, Sirus Mohammadi

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Harmonic distortion is caused by nonlinear devices in the power system. A nonlinear device is one in which the current is not proportional to the applied voltage. Harmonic distortion is present to some degree on all power systems. Proactive monitoring of power quality disturbance levels by electricity utilities is vital to allow cost-effective mitigation when disturbances are perceived to be approaching planning levels and also to protect the security of customer installations. Ensuring that disturbance levels are within limits at the HV and EHV points of supply of the network is essential if satisfactory levels downstream are to be maintained. This paper presents discussion on a power quality monitoring campaign performed at the sub-transmission point of supply of a distribution network with the objective of benchmarking background disturbance levels prior to modifications to the substation and to ensure emissions from HV customers and the downstream MV networks are within acceptable levels. Some discussion on the difficulties involved in such a study is presented. This paper presents a survey of voltage and current harmonic distortion levels at transmission system in Kohgiloye and Boyrahmad. The effects of harmonics on capacitors and power transformers are discussed.

Keywords: power quality, harmonics, flicker, measurement, substation

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: Farhana Tarannum

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Ambient (outdoor) air pollution is now recognized as an important problem, both nationally and worldwide. The concentrations of gaseous pollutants (SOx, NOx, CO and O3) have been determined from samples collected at Kallyanpur along Shamoli corridor in Dhaka city. Pollutants were determined in a sample collected at ground level and a roof of a 7-storied building. These pollutants are emitted largely from stationary sources like fossil fuel fired power plants, industrial plants, and manufacturing facilities as well as mobile sources. The incomplete combustion of fuel, wood and the Sulphur containing fuel used in the vehicles are one of the main causes of CO and SOx respectively in our natural environment. When the temperature of combustion in high enough and some of that nitrogen reacts with oxygen in the air, various nitrogen oxides (NOx) are then formed. The VOCs react with NOx in the presence of sunlight to form O3. UV Visible spectrophotometric method has been used for the determination of SOx, NOx and O3. The sensor type device was used for the estimation of CO. It was found that the air pollutants (CO, SOx, NOx and O3) of a sample collected at the roof of a building were lower compared to the ground level; it indicated that ground level people are mostly affected by the gaseous pollutants.

Keywords: gaseous pollutants, UV-visible spectrophotometry, ambient air quality, Dhaka city

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Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

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Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

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Authors: Ashish Agarwal, Vaibhav Singh

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Harmful emissions from oil and gas processing facilities have long remained a matter of concern for governments and environmentalists throughout the world. This paper analyses Oil and Natural Gas Corporation (ONGC) gas processing plant in Hazira, Gujarat, India. It is the largest gas-processing complex in the country designed to process 41MMSCMD sour natural gas & associated sour condensate. The complex, sprawling over an area of approximate 705 hectares is the mother plant for almost all industries at Hazira and enroute Hazira Bijapur Jagdishpur pipeline. Various sources of pollution from each unit starting from Gas Terminal to Dew Point Depression unit and Caustic Wash unit along the processing chain were examined with the help of different emission data obtained from ONGC. Pollution discharged to the environment was classified into Water, Air, Hazardous Waste and Solid (Non-Hazardous) Waste so as to analyze each one of them efficiently. To protect air environment, Sulphur recovery unit along with automatic ambient air quality monitoring stations, automatic stack monitoring stations among numerous practices were adopted. To protect water environment different effluent treatment plants were used with due emphasis on aquaculture of the nearby area. Hazira plant has obtained the authorization for handling and disposal of five types of hazardous waste. Most of the hazardous waste were sold to authorized recyclers and the rest was given to Gujarat Pollution Control Board authorized vendors. Non-Hazardous waste was also handled with an overall objective of zero negative impact on the environment. The effect of methods adopted is evident from emission data of the plant which was found to be well under Gujarat Pollution Control Board limits.

Keywords: sulphur recovery unit, effluent treatment plant, hazardous waste, sour gas

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Authors: Li-Ching Lin, Yu-Tzu Dai

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Recurrent foot ulcers or foot amputation have a major impact on patients with diabetes mellitus (DM), medical professionals, and society. A critical procedure for foot care is foot self-monitoring. Medical professionals’ understanding of patients’ foot self-monitoring knowledge, attitude, and practice is beneficial for raising patients’ disease awareness. This study investigated these and related factors among patients with DM through a descriptive study of the correlations. A scale for measuring the foot self-monitoring knowledge, attitude, and practice of patients with DM was used. Purposive sampling was adopted, and 100 samples were collected from the respondents’ self-reports or from interviews. The statistical methods employed were an independent-sample t-test, one-way analysis of variance, Pearson correlation coefficient, and multivariate regression analysis. The findings were as follows: the respondents scored an average of 12.97 on foot self-monitoring knowledge, and the correct answer rate was 68.26%. The respondents performed relatively lower in foot health screenings and recording, and awareness of neuropathy in the foot. The respondents held a positive attitude toward self-monitoring their feet and a negative attitude toward having others check the soles of their feet. The respondents scored an average of 12.64 on foot self-monitoring practice. Their scores were lower in their frequency of self-monitoring their feet, recording their self-monitoring results, checking their pedal pulse, and examining if their soles were red immediately after taking off their shoes. Significant positive correlations were observed among foot self-monitoring knowledge, attitude, and practice. The correlation coefficient between self-monitoring knowledge and self-monitoring practice was 0.20, and that between self-monitoring attitude and self-monitoring practice was 0.44. Stepwise regression analysis revealed that the main predictive factors of the foot self-monitoring practice in patients with DM were foot self-monitoring attitude, prior experience in foot care, and an educational attainment of college or higher. These factors predicted 33% of the variance. This study concludes that patients with DM lacked foot self-monitoring practice and advises that the patients’ self-monitoring abilities be evaluated first, including whether patients have poor eyesight, difficulties in bending forward due to obesity, and people who can assist them in self-monitoring. In addition, patient education should emphasize self-monitoring knowledge and practice, such as perceptions regarding the symptoms of foot neurovascular lesions, pulse monitoring methods, and new foot self-monitoring equipment. By doing so, new or recurring ulcers may be discovered in their early stages.

Keywords: diabetic foot, foot self-monitoring attitude, foot self-monitoring knowledge, foot self-monitoring practice

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Authors: Sayed Isahaq Hossain, K. T. Chang, Moustapha Ndour

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Inference of the phreatic line through earth dams is of paramount importance because it could be directly associated with piping phenomena which may lead to the dam failure. Normally in the field, the instrumentations such as ‘diver’ and ‘standpipe’ are to be used to identify the seepage conditions which only provide point data with a fair amount of interpolation or assumption. Here in this paper, we employed high-resolution fiber optic cable sensors (FOCS) based on Raman Scattering in order to obtain a very accurate phreatic line and seepage profile. Unlike the above-mention devices which pinpoint the water level location, this kind of Distributed Fiber Optics Sensing gives us more reliable information due to its inherent characteristics of continuous measurement.

Keywords: standpipe, diver, FOCS, monitoring, Raman scattering

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Authors: Laura Mion

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The implementation of quality management systems in higher education in different countries is determined by national regulatory choices and supranational indications (such as the European Standard Guidelines for Quality Assurance). The effective functioning and transformative capacity of these quality management systems largely depend on the organizational context in which they are applied and, more specifically, on the culture of quality developed in single universities or in single countries. The University's concept of quality culture integrates the structural dimension of QA (quality management manuals, process definitions, tools) with the value dimension of an organization (principles, skills, and attitudes). Within the EHEA (European Higher Education Area), countries such as Portugal, the Netherlands, the UK, and Norway demonstrate a greater integration of QA principles in the various organizational levels and areas of competence of university institutions or have greater experience in implementation or scientific and political debate on the matter. Therefore, the study, through an integrative literature review, of the quality management systems of these countries is aimed at determining a framework of the culture of quality, helpful in defining the elements which, both in structural-organizational terms and in terms of values and skills and attitudes, have proved to be factors of success in the effective implementation of quality assurance systems in universities and in the countries considered in the research. In order for a QA system to effectively aim for continuous improvement in a complex and dynamic context such as the university one, it must embrace a holistic vision of quality from an integrative perspective, focusing on the objective of transforming the reality being evaluated.

Keywords: higher education, quality assurance, quality culture, Portugal, Norway, Netherlands, United Kingdom

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Authors: Masoud Soltani Hosseini, Fereshteh Rahmani, Mohammad Tajik Mansouri, Ali Zolghadr

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Climate change is accompanied with ambient temperature increase and water accessibility limitation. The main objective of this paper is to investigate the effects of climate change on thermal power plants including gas turbines, steam and combined cycle power plants in Iran. For this purpose, the ambient temperature increase and water accessibility will be analyzed and their effects on power output and efficiency of thermal power plants will be determined. According to the results, the ambient temperature has high effect on steam power plants with indirect cooling system (Heller). The efficiency of this type of power plants decreases by 0.55 percent per 1oC ambient temperature increase. This amount is 0.52 and 0.2 percent for once-through and wet cooling systems, respectively. The decrease in power output covers a range of 0.2% to 0.65% for steam power plant with wet cooling system and gas turbines per 1oC air temperature increase. Based on the thermal power plants distribution in Iran and different scenarios of climate change, the total amount of power output decrease falls between 413 and 1661 MW due to ambient temperature increase. Another limitation incurred by climate change is water accessibility. In optimistic scenario, the power output of steam plants decreases by 1450 MW in dry and hot climate areas throughout next decades. The remaining scenarios indicate that the amount of decrease in power output would be by 4152 MW in highlands and cold climate. Therefore, it is necessary to consider appropriate solutions to overcome these limitations. Considering all the climate change effects together, the actual power output falls in range of 2465 and 7294 MW and efficiency loss covers the range of 0.12 to .56 % in different scenarios.

Keywords: climate, change, thermal, power plants

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Authors: Yong-Su Jo, Seung-Min Yang, Seok Hong Min, Tae Kwon Ha

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The synthesis of nickel nanoparticles by the reduction of nickel chloride with hydrazine in an aqueous solution. The effect of hydrazine concentration on batch-processed particle characteristics was investigated using Field Emission Scanning Electron Microscopy (FESEM). Both average particle size and geometric standard deviation (GSD) were decreasing with increasing hydrazine concentration. The continuous synthesis of nickel nanoparticles by microemulsion method was also studied using FESEM and X-ray Diffraction (XRD). The average size and geometric standard deviation of continuous-processed particles were 87.4 nm and 1.16, respectively. X-ray diffraction revealed continuous-processed particles were pure nickel crystalline with a face-centered cubic (fcc) structure.

Keywords: nanoparticle, hydrazine reduction, continuous process, microemulsion method

Procedia PDF Downloads 428

Authors: Mehrnoosh Askarizadeh

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People, process and IT are the most important assets of any organization. Optimal utilization of these resources has been the question of research in business for many decades. The business world have responded by inventing various methodologies that can be used for addressing problems of quality improvement, efficiency of processes, continuous improvement, reduction of waste, automation, strategy alignments etc. Some of these methodologies can be commonly called as Business Process Quality Management methodologies (BPQM). In essence, the first references to the process management can be traced back to Frederick Taylor and scientific management. Time and motion study was addressed to improvement of manufacturing process efficiency. The ideas of scientific management were in use for quite a long period until more advanced quality management techniques were developed in Japan and USA. One of the first prominent methods had been Total Quality Management (TQM) which evolved during 1980’s. About the same time, Six Sigma (SS) originated at Motorola as a separate method. SS spread and evolved; and later joined with ideas of Lean manufacturing to form Lean Six Sigma. In 1990’s due to emerging IT technologies, beginning of globalization, and strengthening of competition, companies recognized the need for better process and quality management. Business Process Management (BPM) emerged as a novel methodology that has taken all this into account and helped to align IT technologies with business processes and quality management. In this article we will study various aspects of above mentioned methods and identified their relations.

Keywords: e-process, quality, TQM, BPM, lean, six sigma, CPI, information technology, management

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Authors: Danijela Vuković, Radovan Čobanović, Milorad Plačkić

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The expansion of population imposes increase in usage of animal meat, on whose quality directly affects the quality of the feed that the animals are fed with. The selection of raw materials, hygiene during the technological process, various hydrothermal treatments, methods of mixing etc. have an influence on the quality of feed. Monitoring of the feed is very important to obtain information about the quality of feed and the possible prevention of animal diseases which can lead to different human diseases outbreaks. In this study parameters of feed safety were monitored. According to the mentioned, the goal of this study was to evaluate microbiological safety of feed (feedstuffs and complete mixtures). Total number of analyzed samples was 4399. Analyzed feed samples were collected in various retail shops and feed factories during the period of 44 months (from January 2013 untill September 2017). Samples were analyzed on Salmonella spp. and Clostridium perfringens in quantity of 50g according to Serbian regulation. All microorganisms were tested according to ISO methodology: Salmonella spp. ISO 6579:2002 and Clostridium perfringens ISO 7937:2004. Out of 4399 analyzed feed samples 97,5% were satisfactory and 2,5% unsatisfactory concerning Salmonella spp. As far as Clostridium perfringens is concerned 100% of analyzed samples were satisfactory. The obtained results suggest that technological processing of feed in Serbia is at high level when it comes to safety and hygiene of the products, but there are still possibilities for progress and improvement which only can be reached trough the permanent monitoring of feed.

Keywords: microbiology, safety, hygiene, feed

Procedia PDF Downloads 272

Authors: Abdesselam Babouri, Ahcène Lemzadmi, M Rahmane, B. Belhadi, N. Abouchi

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Medical monitoring aims at monitoring and remotely controlling the vital physiological parameters of the patient. The physiological sensors provide repetitive measurements of these parameters in the form of electrical signals that vary continuously over time. Various measures allow informing us about the health of the person's physiological data (weight, blood pressure, heart rate or specific to a disease), environmental conditions (temperature, humidity, light, noise level) and displacement and movements (physical efforts and the completion of major daily living activities). The collected data will allow monitoring the patient’s condition and alerting in case of modification. They are also used in the diagnosis and decision making on medical treatment and the health of the patient. This work presents the implementation of a monitoring system to be used for the control of physiological parameters.

Keywords: clinical monitoring, physiological parameters, biomedical sensors, personal health

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Authors: Christian Fercher, Jiaul Islam, Simon R. Corrie

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Fluorescence-based immunodiagnostics are an emerging field in biosensor development and exhibit several advantages over traditional detection methods. While various affinity biosensors have been developed to generate a fluorescence signal upon sensing varying concentrations of analytes, reagentless, reversible, and continuous monitoring of complex biological samples remains challenging. Here, we aimed to genetically engineer biosensors based on single-chain antibody fragments (scFv) that are site-specifically labeled with environmentally sensitive fluorescent unnatural amino acids (UAA). A rational design approach resulted in quantifiable analyte-dependent changes in peak fluorescence emission wavelength and enabled antigen detection in vitro. Incorporation of a polarity indicator within the topological neighborhood of the antigen-binding interface generated a titratable wavelength blueshift with nanomolar detection limits. In order to ensure continuous analyte monitoring, scFv candidates with fast binding and dissociation kinetics were selected from a genetic library employing a high-throughput phage display and affinity screening approach. Initial rankings were further refined towards rapid dissociation kinetics using bio-layer interferometry (BLI) and surface plasmon resonance (SPR). The most promising candidates were expressed, purified to homogeneity, and tested for their potential to detect biomarkers in a continuous microfluidic-based assay. Variations of dissociation kinetics within an order of magnitude were achieved without compromising the specificity of the antibody fragments. This approach is generally applicable to numerous antibody/antigen combinations and currently awaits integration in a wide range of assay platforms for one-step protein quantification.

Keywords: antibody engineering, biosensor, phage display, unnatural amino acids

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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

Procedia PDF Downloads 224

Authors: J. M. Muñoz, Y. Vasquez, P. Oyola, M. Rubio

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All emissions of contaminants inside of homes, offices, school and another enclosure closer that affect the health of those who inhabit or use them are cataloged how indoor pollution. The importance of this study is because individuals spend most of their time in indoors ambient. The main indoor pollutants are oxides of nitrogen (NOₓ), sulfur dioxide (SO₂), carbon monoxide (CO) and particulate matter (PM). Combustion heaters are an important source of pollution indoors. It will be measured: NOₓ, SO₂, CO, PM₂,₅ y PM₁₀ continuous and discreet form at indoor and outdoor of two households with different heating energy; kerosene and electricity (control home) respectively, in addition to environmental parameters such as temperature. With the values obtained in the 'control home' it will be possible estimate the contaminants transport from outside to inside of the household and later the contribution generated by kerosene heating. Transporting the emissions from burning kerosene to a photochemical chamber coupled to a continuous and discreet measuring system of contaminants it will be evaluated the oxidation of the emissions and formation of secondary particulate matter. It will be expected watch a contaminants transport from outside to inside of the household and the kerosene emissions present a high potential of formation secondary particulate matter.

Keywords: heating, indoor pollution, kerosene, secondary particulate matter

Procedia PDF Downloads 195