Removal of dimethylsulfide, n-hexane and toluene from waste air in a flat membrane bioreactor under continuous conditions

Dimethylsulfide (DMS), n-hexane and toluene removal from a waste air was carried out by using a flat composite membrane bioreactor under continuous feeding conditions. The composite membrane consisted of a dense polydimethylsiloxane top layer with an average thickness of 1.5 μm supported with a porous polyacrylonitrile layer of 50 μm. The membrane bioreactor (MBR) was operated during 9 months in which several operational conditions were applied. The inlet load of each compound ranged from 0 to 350 g m-3 h-1 and removal efficiencies of 80, 70 and 0 to 30 % were reached for DMS, toluene and hexane respectively. Two different empty bed residence time (EBRT) were applied on the MBR in order to check the influence of the residence time on the reactor performance. In this case, DMS and toluene removal increased with an increasing EBRT, while the removal of hexane remained constant. By increasing the flow rate of the recirculated liquid from 22 l min-1 to 45 l min-1, the total performance of the biofilter decreased. To increase the mass transfer of hexane in order to get a higher removal, an emulsion of water/silicone oil 80/20 V% was used as recirculated medium at the liquid side of the reactor. This caused a decrease in DMS removal while the removal of toluene remained constant. The variation on the hexane removal decreased significantly, so the reactor became more reliable for degrading hexane

Ethylbenzene removal under mesophilic conditions in a multiple-stage biofilter with macadamia nutshells as carrier material

A biofilter was used to treat air polluted with ethylbenzene. Macadamia nutshells, which are a cheap waste product in Thailand, were chosen as the packing material for the biofilter. The moisture content (6.6 w%), water holding capacity (16.8 w%), nutshell density (1.3 g ml-1) and organic content (78.0 w%) of the macadamia nutshell were measured. The biofilter was operated during 5 months, in which several inlet loads (IL), empty bed residence times (EBRT) and temperatures (T) were applied. At a T of 303 ± 2 K removal efficiencies (RE) higher than 90 % were obtained for IL lower than 95 g m-3 h-1 and 85 g m-3 h-1 at EBRTs of 150 and 90 s, respectively. The estimated yield coefficient, determined from the carbon dioxide production, resulted in 0.79 g dry biomass per g of ethylbenzene (EB) degraded. The results illustrate that a biofilter with macadamia nutshells as carrier material is a good option for air treatment at temperatures from 298 to 308 K. In the mesophilic area, the performance of the biofilter will higher with a higher temperature

Removal of ethyl acetate, n-hexane and toluene from waste air in a membrane bioreactor under continuous and intermittent feeding conditions

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A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust

Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications.This work was supported in part by the Conselleria d’Educació, Cultura i Esport, Generalitat Valenciana. Grant GV/2015/122

Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies