Maintaining Multiphase Flow Meter Accuracy in Sour Environments

Imperial Users onl

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Design of a Novel Portable Flow Meter for Measurement of Average and Peak Inspiratory Flow

The maximum tolerable physical effort that workers can sustain is of significance across many industrial sectors. These limits can be determined by assessing physiological responses to maximal workloads. Respiratory response is the primary metric to determine energy expenditure in industries that use respirator masks to protect against airborne contaminants. Current studies fail to evaluate endurance under conditions that emulate employee operating environments. Values obtained in artificial laboratory settings may be poor indicators of respiratory performance in actual work environments. To eliminate such discrepancies, equipment that accurately measures peak respiratory flows in situ is needed. This study provides a solution in the form of a novel portable flow meter design that accurately measures average and peak inspiratory flow of a user wearing an M40A1 respirator mask

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Modelling of flows in vertical pipes and its application to multiphase flow metering at high gas content and to the prediction of well performance

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Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Modeling of residential outdoor exposure to traffic air pollution and assessment of associated health effects

Traffic air pollution is known to affect cardiopulmonary health in the population. Children with asthma are amongst the most susceptible groups. Several epidemiological studies linked traffic air pollution with increased reporting of asthmatic symptoms and decreased lung function. New approaches with pulmonary inflammation biomarkers allow assessment of acute effects induced by air pollution. Populations are usually exposed to a mixture of pollutants emitted by various sources. Also, epidemiological studies using central site measurements are not able to capture different spatiotemporal distributions of the pollutants. Therefore different modeling approaches are in use to refine the spatiotemporal and the source component in exposure assessment. The aim of this thesis was to build models for estimating short-term residential outdoor exposure to traffic-related air pollution, to find and apportion source contributions to particulate matter smaller 10µm (PM10) and to examine the relationship between spatially refined exposure estimates and respiratory health effects in children with asthma. Methods This thesis was conducted within the framework of two pediatric asthma panel studies: a Southern California study in the greater Los Angeles area, and the MfM-U (Monitoring flankierende Massnahmen – Umwelt) study in a Swiss Alpine valley. In the Southern California study measurements of personal particulate matter smaller 2.5µm (PM2.5), elemental carbon (EC), and organic carbon (OC) were collected in 63 children living in Riverside (Aug to Dec 2003) and Whittier (July to Nov 2004). Concurrently one home site and a fixed central site were monitored. Home site measurements were used to build city-specific and pooled models for estimating PM2.5, EC, and OC levels at all other participating children’s homes by using land-use regression methods including fixed site measurements and CALINE4 dispersion estimates (local traffic). We compared the home outdoor estimates with the personal measurements. The MfM-U panel study was conducted in Erstfeld located in a highway impacted Swiss Alpine valley. From November 2007 to June 2009, thirteen children with asthma had monthly monitoring of pulmonary inflammation (i.e. fractional exhaled nitric oxide (FeNO)) and oxidative stress markers in exhaled breath condensate (eBC) (i.e. nitrite, pH). Concurrently levels of PM10, nitrogen dioxide (NO2), EC, OC, and particle numbers (PN) were monitored at one background, one highway and seven mobile sites. NO2 measurements were used to build a model estimating outdoor concentrations at the participating children’s homes with a similar approach as in the Southern California study. Chemically speciated data was used in receptor modeling to apportion the source contributions to PM10. NO2 model estimates and source-specific PM10 were then used to investigate associations to pulmonary inflammation and oxidative stress marker levels in the children. Results In the Southern California study, all models could explain a large part of variation for home outdoor PM2.5, OC and EC (adj R2 = 0.75 to 0.97). Important predictors were central site measurement, distance to highway and wind variables. However, only PM2.5 model estimates correlated well with daily personal measurements (R2 = 0.65 to 0.69). In the MfM-U study, traffic-related pollutants NO2, EC and PN showed high concentrations at the highway site decaying some 30-40% to background levels within 150-200m. Weekday patterns of traffic pollutants followed the heavy-duty truck traffic counts on the highway. All pollutants showed higher levels in winter than in summer. The NO2 model explained a large part of variance (adj R2 = 0.91) and estimates matched very well the validation measurements (R2 = 0.74). We identified nine sources contributing to PM10. Traffic (29%) was the main source, including traffic exhaust (18%), road dust (8%), tire & brake wear (1%), and road salt (2%). Other contributions came from secondary particles (27%), biomass burning (18%), railway traffic (11%) and mineral sources from mineral dust (7%) and a tunnel construction site (6%). There were higher contributions from secondary particles (37%) in summer and from biomass burning (26%) and traffic (30%) in winter. Traffic, railway and mineral contributions to PM10 were higher at sites close to the specific source. Biomass burning estimates correlated well (R2 = 0.81) with levoglucosan (wood burning marker), while traffic exhaust estimates were weakly associated (R2=0.13) with 1-nitropyrine (diesel exhaust marker) due to the mixture of diesel and gasoline in the traffic fleet. Mean levels of FeNO, eBC nitrite, and eBC pH measured in the thirteen children were 17.04ppb, 0.82µM, and 7.06, respectively, indicative for mild asthma. For days without report of any cold symptoms, FeNO levels increased by 15%, 13% and 6% if NO2, EC and total PM10 on the prior day of the health measurement were increased by one inter quartile range, respectively. eBC pH levels decreased significantly with increasing PM10, NO2, and EC concentrations measured one, two or three days prior the health monitoring. However, no significant associations were observed between source-specific PM10 concentrations and FeNO, and between eBC nitrite and any of the pollutants. Conclusions We were able to build models to estimate residential outdoor air pollution exposure using only a limited number of spatially distributed monitoring sites. We could identify traffic as the major source contributing to PM10 in Erstfeld and observed a distinct relationship between highway traffic and concentration levels of NO2, EC and PN. Despite relatively low air pollution levels in Switzerland, we still detected associations between traffic-related air pollution and pulmonary inflammation markers in children with asthma

A Semantic-driven Approach for Maintenance Digitalization in the Pharmaceutical Industry

The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play an essential role in ensuring product quality and integral functioning of equipment and premises. This paper first identifies the key challenges of digitalization in pharmaceutical industry and creates the corresponding problem space for key involved elements. A literature review is conducted to investigate the mainstream maintenance strategies, digitalization models, tools and official guidance from authorities in pharmaceutical industry. Based on the review result, a semantic-driven digitalization framework is proposed aiming to improve the digital continuity and cohesion of digital resources and technologies for maintenance activities in the pharmaceutical industry. A case study is conducted to verify the feasibility of the proposed framework based on the water sampling activities in Merck Serono facility in Switzerland. A tool-chain is presented to enable the functional modules of the framework. Some of the key functional modules within the framework are implemented and have demonstrated satisfactory performance. As one of the outcomes, a digital sampling assistant with web-based services is created to support the automated workflow of water sampling activities. The implementation result proves the potential of the proposed framework to solve the identified problems of maintenance digitalization in the pharmaceutical industry