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Refrigerant Management by Using Iot Technology With the Co-benefit in Energy Saving at Malaysia Food and Cold Chain Sector

Abstract Fluorocarbon gas consumption can contribute in direct and indirect impact to the environment such as ozone depletion and global warming due to fluorocarbon gas leakage and increase in electricity consumption, respectively. This study is focused on early detection of fluorocarbon gas leakage on refrigeration unit to determine scenario of fluorocarbon gas leakage management and estimate reduction potential of greenhouse gas (GHG) emission and the co-benefits in energy saving. In this study, the Internet of Things (IoT) that utilized ultrasonic sensor detection system to detect early fluorocarbon gas leakage was installed at the chiller unit at two selected facilities, A and B. After installation, the data was monitored, and any gas leakage detected was countermeasure. Results from the data monitoring, reduction potential of GHG emission and energy saving co-benefits calculated by using formula adopted from the Japan Refrigeration and Air Conditioning Industry Association, (JRAIA). The monitoring results found that different response rate towards leakage detection between two facilities A and B (chiller 1 and chiller 2) contributes to 33.9%, 60.6% and 21.3% of fluorocarbon leakage. Installation of IoT based refrigerant leakage device proved that early detection and on time countermeasures successfully reduce 30 t-CO2e/yrs (facility A) and 460 t-CO2e/yrs (facility B). Thus, proper fluorocarbon gas management is important to reduce environmental impact of the fluorocarbon gases.

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An Initial Analysis of E-Procurement Search Behaviour

AbstractProcurement or tender search is where suppliers seek opportunities for providing goods, works or services that authorities, organisations and businesses require. Such opportunities are listed as procurement contract notices for which suppliers can submit tenders. Typically, an E-Procurement system is used to help find and carry out one or more of the stages involved in the procurement process (from finding potential opportunities, bidding on such opportunities, to delivering the goods, works or services, i.e. find, win, deliver). Such systems are crucial in enabling suppliers to efficiently search through the available listings of procurement contract notices listed across various public and commercial portals. However, little research has investigated how end-users search for such opportunities. In this paper, we perform a descriptive analysis of the professional search behaviours of suppliers using a bespoke e-procurement system. Our analysis is based on a sub-sample of six months of search log interaction data. First, we provide an overview of the usage patterns of our sample of users before investigating how the behaviour of searchers is influenced by the type of search form used (quick vs advanced), user expertise (new vs experienced), and the domain of the procurement notices (General, Defence, Medical, etc.). Our findings highlight that more experienced searchers appear to be more strategic than less experienced searchers and that searchers behave differently depending on the domain in terms of querying and assessing behaviours. This analysis suggests that e-procurement search engines need to be mindful of the differences across searchers and between domains when designing a system to help support their users.KeywordsProfessional searchProcurement search

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Implicit requirements for ontological multi-level types in the UNICLASS classification

In the multi-level type modeling community, claims that most enterprise application systems use ontologically multi-level types are ubiquitous. To be able to empirically verify this claim one needs to be able to expose the (often underlying) ontological structure and show that it does, indeed, make a commitment to multi-level types. We have not been able to find any published data showing this being done. From a top-level ontology requirements perspective, checking this multi-level type claim is worthwhile. If the datasets for which the top-level ontology is required are ontologically committed to multi-level types, then this is a requirement for the top-level ontology. In this paper, we both present some empirical evidence that this ubiquitous claim is correct as well as describing the process we used to expose the underlying ontological commitments and examine them. We describe how we use the bCLEARer process to analyse the UNICLASS classifications making their implicit ontological commitments explicit. We show how this reveals the requirements for two general ontological commitments; higher-order types and first-class relations. This establishes a requirement for a top-level ontology that includes the UNICLASS classification to be able to accommodate these requirements. From a multi-level type perspective, we have established that the bCLEARer entification process can identify underlying ontological commitments to multi-level type that do not exist in the surface linguistic structure. So, we have a process that we can reuse on other datasets and application systems to help empirically verify the claim that ontological multi-level types are ubiquitous.

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Accelerated heat transfer simulations using coupled DEM and CFD

This work presents an accelerated simulation of heat and mass transfer by coupling Discrete Element Methodologies (DEMs) and Computational Fluid Dynamics (CFD), utilising Graphics Processing Unit (GPU) technology. The presented model is a continuation of previous work from Hobbs (2009) [1] and focuses on demonstrating the capabilities and effectiveness of implementing the GPU combined with the Central Processing Unit (CPUs) technologies to run a complex industrial simulation. Furthermore, different flighting configurations have been used to investigate the influence of the design to the drying process. A model of an aggregate drum dryer was used to produce hot mix asphalt and different computing configurations have been implemented to investigate the effect of GPU-CPU technology in such a complex simulation. Commercial codes from ANSYS and DEM Solutions were coupled to simulate heat transfer from the hot gases to the aggregate particles. Fluid flow and particle-fluid interactions are solved by the CFD solver which exchanges information at regular intervals. The results showed that the coupled model captures accurately the convective heat transfer from the fluid to the solid phase and demonstrated significant improvement in terms of simulation time. The proposed model has significant impact in industrial applications as it provides insight on how to simulate large-scale applications rapidly and accurately.

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