Experimental evaluation of a membrane distillation system for integration with concentrated photovoltaic/thermal (CPV/T) energy

AbstractResults are presented for a concentrated solar photovoltaic and thermal powered membrane distillation (MD) system for seawater desalination. Solar intensity data was input into a mathematical model for the solar energy system and out let temperature from the energy system was calculated. The MD module was tested for a fluctuating inlet temperature, as would be produced from a solar energy source. A maximum distillate flux of 3.4 l/m2h was recorded, though this did not correspond to the highest inlet temperature. An observed delay in the modules response to the fluctuations in temperature was due to the thermal mass of the MD unit. The conductivity of the distillate was measured to assess the effects of transient operation on the quality of the distillate produced. It was determined that although the quantity and quality of the distillate varied with the fluctuations in power supplied to the module, the effects were not significant enough to rule out the integration of the MD module with solar energy

IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility

Effects of Capping on the (Ga,Mn)As Magnetic Depth Profile

Annealing can increase the Curie temperature and net magnetization in uncapped (Ga,Mn)As films, effects that are suppressed when the films are capped with GaAs. Previous polarized neutron reflectometry (PNR) studies of uncapped (Ga,Mn)As revealed a pronounced magnetization gradient that was reduced after annealing. We have extended this study to (Ga,Mn)As capped with GaAs. We observe no increase in Curie temperature or net magnetization upon annealing. Furthermore, PNR measurements indicate that annealing produces minimal differences in the depth-dependent magnetization, as both as-grown and annealed films feature a significant magnetization gradient. These results suggest that the GaAs cap inhibits redistribution of interstitial Mn impurities during annealing.Comment: 12 pages, 3 figures, submitted to Applied Physics Letter

Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a largescale Air Handling Unit (AHU)

Cluster analysis of wind turbine alarms for characterising and classifying stoppages

Turbine alarm systems can give useful information to remote technicians on the cause of a fault or stoppage. However, alarms are generally generated at much too high a rate to gain meaningful insight from on their own, so generally require extensive domain knowledge to interpret. By grouping together commonly occurring alarm sequences, the burden of analysis can be reduced. Instead of analysing many individual alarms that occur during a stoppage, the stoppage can be linked to a commonly occurring sequence of alarms. Hence, maintenance technicians can be given information about the shared characteristics or root causes of stoppages where that particular alarm sequence appeared in the past. This research presents a methodology to identify relevant alarms from specific turbine assemblies and group together similar alarm sequences as they appear during stoppages. Batches of sequences associated with 456 different stoppages are created, and features are extracted from these batches representing the order the alarms appeared in. The batches are grouped together using clustering techniques, and evaluated using silhouette analysis and manual inspection. Results show that almost half of all stoppages can be attributed to one of 15 different clusters of alarm sequences

Comparative performance of prediction model, non-expert and telediagnosis of common external and middle ear disease using a patient cohort from Cambodia that included one hundred and thirty-eight ears

Efforts to combat the large global burden of ear and hearing disorders are hampered by poor availability of expert diagnosis We report the first study to directly compare prediction model, non-expert and tele-diagnosis of middle and external ear disorders. A prediction model based upon a novel automated otological symptom questionnaire performed poorly, but absence of otorrhoea was found to reliably exclude a diagnosis of chronic suppurative otitis media. Both on-site non-expert and expert tele-diagnosis had high diagnostic specificity, but low sensitivity. Future work could explore how the validity of these diagnostic methods may be improved

Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine's sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine fault

A systematic analysis of real-world energy blockchain Initiatives

The application of blockchain technology to the energy sector promises to derive new operating models focused on local generation and sustainable practices, which are driven by peer-to-peer collaboration and community engagement. However, real-world energy blockchains differ from typical blockchain networks insofar as they must interoperate with grid infrastructure, adhere to energy regulations, and embody engineering principles. Naturally, these additional dimensions make real-world energy blockchains highly dependent on the participation of grid operators, engineers, and energy providers. Although much theoretical and proof-of-concept research has been published on energy blockchains, this research aims to establish a lens on real-world projects and implementations that may inform the alignment of academic and industry research agendas. This research classifies 131 real-world energy blockchain initiatives to develop an understanding of how blockchains are being applied to the energy domain, what type of failure rates can be observed from recently reported initiatives, and what level of technical and theoretical details are reported for real-world deployments. The results presented from the systematic analysis highlight that real-world energy blockchains are (a) growing exponentially year-on-year, (b) producing relatively low failure/drop-off rates (~7% since 2015), and (c) demonstrating information sharing protocols that produce content with insufficient technical and theoretical depth

How much dystrophin is enough: the physiological consequences of different levels of dystrophin in the mdx mouse

Splice modulation therapy has shown great clinical promise in Duchenne muscular dystrophy, resulting in the production of dystrophin protein. Despite this, the relationship between restoring dystrophin to established dystrophic muscle and its ability to induce clinically relevant changes in muscle function is poorly understood. In order to robustly evaluate functional improvement, we used in situ protocols in the mdx mouse to measure muscle strength and resistance to eccentric contraction-induced damage. Here, we modelled the treatment of muscle with pre-existing dystrophic pathology using antisense oligonucleotides conjugated to a cell-penetrating peptide. We reveal that 15% homogeneous dystrophin expression is sufficient to protect against eccentric contraction-induced injury. In addition, we demonstrate a >40% increase in specific isometric force following repeated administrations. Strikingly, we show that changes in muscle strength are proportional to dystrophin expression levels. These data define the dystrophin restoration levels required to slow down or prevent disease progression and improve overall muscle function once a dystrophic environment has been established in the mdx mouse model