Abstract: This paper presents an approach on the advantages of using adequate coverage in the zenithal lighting typology in various areas of architectural production, while at the same time to encourage to the design concerns inherent in this choice of roofing in Brazil. Understanding that sustainability needs to cover several aspects, a roofing system such as zenithal lighting system can contribute to the provision of better quality natural light for the interior of the building, which is related to the good health and welfare; it will also be able to contribute for the sustainable aspects and environmental needs, when it allows the generation of energy in semitransparent or opacity photovoltaic solutions and economize the artificial lightning. When the energy balance in the building is positive, that is, when the building generates more energy than it consumes, it may fit into the Net Zero Energy Building concept. The zenithal lighting systems could be an important ally in Brazil, when solved the burden of heat gains, participate in the set of pro-efficiency actions in search of "zero energy buildings". The paper presents comparative three cases of buildings that have used this feature in search of better environmental performance, both in light comfort and sustainability as a whole. Two of these buildings are examples in Europe: the Notley Green School in the UK and the Isofóton factory in Spain. The third building with these principles of shed´s roof is located in Brazil: the Ipel´s factory in São Paulo.
Abstract: There is a need for new systems that can be attached to drinking water treatment plants and have the required treatment capacity as well as the selectivity regarding components derived from anthropogenic activities. In a context of high volumes of water and low concentration of contaminants, adsorption/interchange processes are appealing since they meet the required features. Iron oxides such as siderite and molysite, which are respectively based on FeCO3 and FeCl3, can be found in nature. In this work, their observed performance, raw or roasted at different temperatures, as adsorbents of some inorganic anions is discussed. Roasted 1:1 FeCO3: FeCl3 mixture was very selective for arsenic and allowed a 100% removal of As from a 10 mg L-1 As solution. Besides, the 1:1 FeCO3 and FeCl3 mixture roasted at 500 ºC showed good selectivity for, in order of preference, arsenate, bromate, phosphate, fluoride and nitrate anions with distribution coefficients of, respectively, 4200, 2800, 2500 0.4 and 0.03 L g-1.
Abstract: In this research, a multidimensional compromise optimization method is proposed for multidimensional decision making analysis in the development ranking of the Gulf Cooperation Council Countries and Turkey. The proposed approach presents ranking solutions resulting from different multicriteria decision analyses, which yield different ranking orders for the same ranking problem, consisting of a set of alternatives in terms of numerous competing criteria when they are applied with the same numerical data. The multiobjective optimization decision making problem is considered in three sequential steps. In the first step, five different criteria related to the development ranking are gathered from the research field. In the second step, identified evaluation criteria are, objectively, weighted using standard deviation procedure. In the third step, a country selection problem is illustrated with a numerical example as an application of the proposed multidimensional compromise optimization model. Finally, multidimensional compromise optimization approach is applied to rank the Gulf Cooperation Council Countries and Turkey.
Abstract: Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.
Abstract: Developing countries are nowadays confronted with great challenges related to domestic sanitation services in view of the imminent water scarcity. Contemporary sanitation technologies established in these countries are likely to pose health risks unless waste management standards are followed properly. This paper provides a solution to sustainable sanitation with the development of an innovative toilet system, called Nano Membrane Toilet (NMT), which has been developed by Cranfield University and sponsored by the Bill & Melinda Gates Foundation. The particular technology converts human faeces into energy through gasification and provides treated wastewater from urine through membrane filtration. In order to evaluate the environmental profile of the NMT system, a deterministic life cycle assessment (LCA) has been conducted in SimaPro software employing the Ecoinvent v3.3 database. The particular study has determined the most contributory factors to the environmental footprint of the NMT system. However, as sensitivity analysis has identified certain critical operating parameters for the robustness of the LCA results, adopting a stochastic approach to the Life Cycle Inventory (LCI) will comprehensively capture the input data uncertainty and enhance the credibility of the LCA outcome. For that purpose, Monte Carlo simulations, in combination with an artificial neural network (ANN) model, have been conducted for the input parameters of raw material, produced electricity, NOX emissions, amount of ash and transportation of fertilizer. The given analysis has provided the distribution and the confidence intervals of the selected impact categories and, in turn, more credible conclusions are drawn on the respective LCIA (Life Cycle Impact Assessment) profile of NMT system. Last but not least, the specific study will also yield essential insights into the methodological framework that can be adopted in the environmental impact assessment of other complex engineering systems subject to a high level of input data uncertainty.
Abstract: The new emerging Visible Light Communication
(VLC) technology has been subjected to intensive investigation,
evaluation, and lately, deployed in the context of convoy-based
applications for Intelligent Transportations Systems (ITS). The
technology limitations were defined and supported by different
solutions proposals to enhance the crucial alignment and mobility
limitations. In this paper, we propose the incorporation of VLC
technology and Lane-Centering (LC) technique to assure the
VLC-connectivity by keeping the autonomous vehicle aligned to
the lane center using vision-based lane detection in a convoy-based
formation. Such combination can ensure the optical communication
connectivity with a lateral error less than 30 cm. As soon as the
road lanes are detectable, the evaluated system showed stable
behavior independently from the inter-vehicle distances and
without the need for any exchanged information of the remote
vehicles. The evaluation of the proposed system is verified using
VLC prototype and an empirical result of LC running application
over 60 km in Madrid M40 highway.
Abstract: Multi-modal film boiling simulations are carried out on adaptive octree grids. The liquid-vapor interface is captured using the volume-of-fluid framework adjusted to account for exchanges of mass, momentum, and energy across the interface. Surface tension effects are included using a volumetric source term in the momentum equations. The phase change calculations are conducted based on the exact location and orientation of the interface; however, the source terms are calculated using the mixture variables to be consistent with the one field formulation used to represent the entire fluid domain. The numerical model on octree representation of the computational grid is first verified using test cases including advection tests in severely deforming velocity fields, gravity-based instabilities and bubble growth in uniformly superheated liquid under zero gravity. The model is then used to simulate both single and multi-modal film boiling simulations. The octree grid is dynamically adapted in order to maintain the highest grid resolution on the instability fronts using markers of interface location, volume fraction, and thermal gradients. The method thus provides an efficient platform to simulate fluid instabilities with or without phase change in the presence of body forces like gravity or shear layer instabilities.
Abstract: Electricity prices have sophisticated features such as
high volatility, nonlinearity and high frequency that make forecasting
quite difficult. Electricity price has a volatile and non-random
character so that, it is possible to identify the patterns based on the
historical data. Intelligent decision-making requires accurate price
forecasting for market traders, retailers, and generation companies.
So far, many shallow-ANN (artificial neural networks) models have
been published in the literature and showed adequate forecasting
results. During the last years, neural networks with many hidden
layers, which are referred to as DNN (deep neural networks) have
been using in the machine learning community. The goal of this
study is to investigate electricity price forecasting performance of the
shallow-ANN and DNN models for the Turkish day-ahead electricity
market. The forecasting accuracy of the models has been evaluated
with publicly available data from the Turkish day-ahead electricity
market. Both shallow-ANN and DNN approach would give successful
result in forecasting problems. Historical load, price and weather
temperature data are used as the input variables for the models.
The data set includes power consumption measurements gathered
between January 2016 and December 2017 with one-hour resolution.
In this regard, forecasting studies have been carried out comparatively
with shallow-ANN and DNN models for Turkish electricity markets
in the related time period. The main contribution of this study
is the investigation of different shallow-ANN and DNN models
in the field of electricity price forecast. All models are compared
regarding their MAE (Mean Absolute Error) and MSE (Mean Square)
results. DNN models give better forecasting performance compare to
shallow-ANN. Best five MAE results for DNN models are 0.346,
0.372, 0.392, 0,402 and 0.409.
Abstract: Load forecasting has become crucial in recent years
and become popular in forecasting area. Many different power
forecasting models have been tried out for this purpose. Electricity
load forecasting is necessary for energy policies, healthy and reliable
grid systems. Effective power forecasting of renewable energy load
leads the decision makers to minimize the costs of electric utilities
and power plants. Forecasting tools are required that can be used
to predict how much renewable energy can be utilized. The purpose
of this study is to explore the effectiveness of LSTM-based neural
networks for estimating renewable energy loads. In this study, we
present models for predicting renewable energy loads based on
deep neural networks, especially the Long Term Memory (LSTM)
algorithms. Deep learning allows multiple layers of models to learn
representation of data. LSTM algorithms are able to store information
for long periods of time. Deep learning models have recently been
used to forecast the renewable energy sources such as predicting
wind and solar energy power. Historical load and weather information
represent the most important variables for the inputs within the
power forecasting models. The dataset contained power consumption
measurements are gathered between January 2016 and December
2017 with one-hour resolution. Models use publicly available data
from the Turkish Renewable Energy Resources Support Mechanism.
Forecasting studies have been carried out with these data via deep
neural networks approach including LSTM technique for Turkish
electricity markets. 432 different models are created by changing
layers cell count and dropout. The adaptive moment estimation
(ADAM) algorithm is used for training as a gradient-based optimizer
instead of SGD (stochastic gradient). ADAM performed better than
SGD in terms of faster convergence and lower error rates. Models
performance is compared according to MAE (Mean Absolute Error)
and MSE (Mean Squared Error). Best five MAE results out of
432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting
performance of the proposed LSTM models gives successful results
compared to literature searches.
Abstract: Environmental and sustainability concerns push the industries to manufacture alternative materials having less environmental impact. The Wood Plastic Composites (WPCs) produced by blending the biopolymers and natural fillers permit not only to tailor the desired properties of materials but also are the solution to meet the environmental and sustainability requirements. This work presents the elaboration and characterization of the fully green WPCs prepared by blending a biopolymer, BIOPLAST® GS 2189 and spruce sawdust used as filler with different amounts. Since both components are bio-based, the resulting material is entirely environmentally friendly. The mechanical, thermal, structural properties of these WPCs were characterized by different analytical methods like tensile, flexural and impact tests, Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and X-ray Diffraction (XRD). Their water absorption properties and resistance to the termite and fungal attacks were determined in relation with different wood filler content. The tensile and flexural moduli of WPCs increased with increasing amount of wood fillers into the biopolymer, but WPCs became more brittle compared to the neat polymer. Incorporation of spruce sawdust modified the thermal properties of polymer: The degradation, cold crystallization, and melting temperatures shifted to higher temperatures when spruce sawdust was added into polymer. The termite, fungal and water absorption resistance of WPCs decreased with increasing wood amount in WPCs, but remained in durability class 1 (durable) concerning fungal resistance and quoted 1 (attempted attack) in visual rating regarding to the termites resistance except that the WPC with the highest wood content (30 wt%) rated 2 (slight attack) indicating a long term durability. All the results showed the possibility to elaborate the easy injectable composite materials with adjustable properties by incorporation of BIOPLAST® GS 2189 and spruce sawdust. Therefore, lightweight WPCs allow both to recycle wood industry byproducts and to produce a full ecologic material.
Abstract: This article is referring to a comparative study regarding the electrical energy consumption for lighting on diverse types of big sizes commercial buildings built in Romania after 2007, having 3, 4, 5 versus 8, 9, 10 operational years. Some buildings have installed building management systems (BMS) to monitor also the lighting performances starting with the opening days till the present days but some have chosen only local meters to implement. Firstly, for each analyzed building, the total required energy power and the energy power consumption for lighting were calculated depending on the lamps number, the unit power and the average daily running hours. All objects and installations were chosen depending on the destination/location of the lighting (exterior parking or access, interior or covering parking, building interior and building perimeter). Secondly, to all lighting objects and installations, mechanical counters were installed, and to the ones linked to BMS there were installed the digital meters as well for a better monitoring. Some efficient solutions are proposed to improve the power consumption, for example the 1/3 lighting functioning for the covered and exterior parking lighting to those buildings if can be done. This type of lighting share can be performed on each level, especially on the night shifts. Another example is to use the dimmers to reduce the light level, depending on the executed work in the respective area, and a 30% power energy saving can be achieved. Using the right BMS to monitor, the energy consumption depending on the average operational daily hours and changing the non-performant unit lights with the ones having LED technology or economical ones might increase significantly the energy performances and reduce the energy consumption of the buildings.
Abstract: 'Driving What’s Next' is a strong campaign of the new administration of De La Salle Lipa in promoting social innovation in quality education. The new leadership directs social innovation in quality education in the institutional directions and initiatives to address real-world challenges with real-world solutions. This research under study aims to qualify the commitment of the institution to extend the Lasallian quality human and Christian education to all, as expressed in the Institution’s new mission-vision statement. The Classic Grounded Theory methodology is employed in the process of generating concepts in reference to the documents, a series of meetings, focus group discussions and other related activities that account for the conceptualization and formulation of the new mission-vision along with the new education innovation framework. Notably, Driving What’s Next is the emergent theory that encapsulates the commitment of giving quality human and Christian education to all. It directs the new leadership in driving social innovation in quality education initiatives. Correspondingly, Driving What’s Next is continually resolved through four interrelated strategies also termed as the institution's four strategic directions, namely: (1) driving social innovation in quality education, (2) embracing our shared humanity and championing social inclusion and justice initiatives, (3) creating sustainable futures and (4) engaging diverse stakeholders in our shared mission. Significantly, the four strategic directions capture and integrate the 17 UN sustainable development goals, making the innovative curriculum locally and globally relevant. To conclude, the main concern of the new administration and how it is continually resolved, provide meaningful and fun learning experiences and promote a new way of learning in the light of the 21st century skills among the members of the academic community including stakeholders and extended communities at large, which are defined as: learning together and by association (collaboration), learning through engagement (communication), learning by design (creativity) and learning with social impact (critical thinking).
Abstract: The removal of lead ion (Pb2+) from aqueous solution by activated carbon with phosphoric acid activation employing mangrove propagule as precursor was investigated in a batch adsorption system. Batch studies were carried out to address various experimental parameters including pH and contact time. The Langmuir and Freundlich models were able to describe the adsorption equilibrium, while the pseudo first order and pseudo second order models were used to describe kinetic process of Pb2+ adsorption. The results show that the adsorption data are seen in accordance with Langmuir isotherm model and pseudo-second order kinetic model.
Abstract: In this paper, the Differential Transform Method (DTM) is employed to predict and to analysis the non-local critical buckling loads of carbon nanotubes with various end conditions and the non-local Timoshenko beam described by single differential equation. The equation differential of buckling of the nanobeams is derived via a non-local theory and the solution for non-local critical buckling loads is finding by the DTM. The DTM is introduced briefly. It can easily be applied to linear or nonlinear problems and it reduces the size of computational work. Influence of boundary conditions, the chirality of carbon nanotube and aspect ratio on non-local critical buckling loads are studied and discussed. Effects of nonlocal parameter, ratios L/d, the chirality of single-walled carbon nanotube, as well as the boundary conditions on buckling of CNT are investigated.
Abstract: The main hypothesis of the dynamics of solid phase microextraction (SPME) is that steady-state mass transfer is respected throughout the SPME extraction process. It considers steady-state diffusion is established in the two phases and fast exchange of the analyte at the solid phase film/water interface. An improved model is proposed in this paper to handle with the situation when the analyte (atrazine) is in contact with colloid suspensions (carboxylate latex in aqueous solution). A mathematical solution is obtained by substituting the diffusion coefficient by the mean of diffusion coefficient between analyte and carboxylate latex, and also thickness layer by the mean thickness in aqueous solution. This solution provides an equation relating the extracted amount of the analyte to the extraction a little more complicated than previous models. It also gives a better description of experimental observations. Moreover, the rate constant of analyte obtained is in satisfactory agreement with that obtained from the initial curve fitting.
Abstract: Structural frequency response testing is accurate in identifying the dynamic characteristic of a machinery structure. In practical perspective, conventional structural frequency response testing such as experimental modal analysis with impulse technique (also known as “impulse testing”) has limitation especially on its long acquisition time. The high acquisition time is mainly due to the redundancy procedure where the engineer has to repeatedly perform the test in 3 directions, namely the axial-, horizontal- and vertical-axis, in order to comprehensively define the dynamic behavior of a 3D structure. This is unfavorable to numerous industries where the downtime cost is high. This study proposes to reduce the testing time by using oblique impact. Theoretically, a single oblique impact can induce significant vibration responses and vibration modes in all the 3 directions. Hence, the acquisition time with the implementation of the oblique impulse technique can be reduced by a factor of three (i.e. for a 3D dynamic system). This study initiates an experimental investigation of impulse testing with oblique excitation. A motor-driven test rig has been used for the testing purpose. Its dynamic characteristic has been identified using the impulse testing with the conventional normal impact and the proposed oblique impact respectively. The results show that the proposed oblique impulse testing is able to obtain all the desired natural frequencies in all 3 directions and thus providing a feasible solution for a fast and time effective way of conducting the impulse testing.
Abstract: The current study couples a quasi-steady Vortex Lattice
Method and a camber correcting technique, ‘Decambering’ for
unsteady post-stall flow prediction. The wake is force-free and
discrete such that the wake lattices move with the free-stream once
shed from the wing. It is observed that the time-averaged unsteady
coefficient of lift sees a relative drop at post-stall angles of attack
in comparison to its steady counterpart for some angles of attack.
Multiple solutions occur at post-stall and three different algorithms
to choose solutions in these regimes show both unsteadiness and
non-convergence of the iterations. The distribution of coefficient of
lift on the wing span also shows sawtooth. Distribution of vorticity
changes both along span and in the direction of the free-stream as
the wake develops over time with distinct roll-up, which increases
with time.
Abstract: One of the major challenges for sustainable smart
building systems is to support device interoperability, i.e. connecting
sensor or actuator devices from different vendors, and present their
functionality to the external applications. Furthermore, smart building
systems are supposed to connect with devices that are not available
yet, i.e. devices that become available on the market sometime later.
It is of vital importance that a sustainable smart building platform
provides an appropriate external interface that can be leveraged
by external applications and smart services. An external platform
interface must be stable and independent of specific devices and
should support flexible and scalable usage scenarios. A typical
approach applied in smart home systems is based on a generic
device interface used within the smart building platform. Device
functions, even of rather complex devices, are mapped to that generic
base type interface by means of specific device drivers. Our new
approach, presented in this work, extends that approach by using the
smart building system’s rule engine to create complex virtual devices
that can represent the most diverse properties of real devices. We
examined and evaluated both approaches by means of a practical
case study using a smart building system that we have developed.
We show that the solution we present allows the highest degree
of flexibility without affecting external application interface stability
and scalability. In contrast to other systems our approach supports
complex virtual device configuration on application layer (e.g. by
administration users) instead of device configuration at platform layer
(e.g. platform operators). Based on our work, we can show that
our approach supports almost arbitrarily flexible use case scenarios
without affecting the external application interface stability. However,
the cost of this approach is additional appropriate configuration
overhead and additional resource consumption at the IoT platform
level that must be considered by platform operators. We conclude
that the concept of complex virtual devices presented in this work
can be applied to improve the usability and device interoperability of
sustainable intelligent building systems significantly.
Abstract: Internet of Things (IoT) is a powerful industry system, which end-devices are interconnected and automated, allowing the devices to analyze data and execute actions based on the analysis. The IoT technology leverages the technology of Radio-Frequency Identification (RFID) and Wireless Sensor Network (WSN), including mobile and sensor. These technologies contribute to the evolution of IoT. However, due to more devices are connected each other in the Internet, and data from various sources exchanged between things, confidentiality of the data becomes a major concern. This paper focuses on one of the major challenges in IoT; authentication, in order to preserve data integrity and confidentiality are in place. A few solutions are reviewed based on papers from the last few years. One of the proposed solutions is securing the communication between IoT devices and cloud servers with Elliptic Curve Cryptograhpy (ECC) based mutual authentication protocol. This solution focuses on Hyper Text Transfer Protocol (HTTP) cookies as security parameter. Next proposed solution is using keyed-hash scheme protocol to enable IoT devices to authenticate each other without the presence of a central control server. Another proposed solution uses Physical Unclonable Function (PUF) based mutual authentication protocol. It emphasizes on tamper resistant and resource-efficient technology, which equals a 3-way handshake security protocol.
Abstract: Among all possibilities to combat global warming, CO2 capture and sequestration (CCS) is presented as a great alternative to reduce greenhouse gas (GHG) emission. Several strategies for CCS from industrial and power plants are being considered. The concept of combined oxy-fuel combustion has been the most alternative solution. Nevertheless, due to the high cost of pure O2 production, additional ways recently emerged. In this paper, an innovative combustion process for a gas turbine cycle was studied: it was composed of methane combustion with oxygen enhanced air (OEA), exhaust gas recirculation (EGR) and H2O issuing from STIG (Steam Injection Gas Turbine), and the CO2 capture was realized by membrane separator. The effect on this combustion process was emphasized, and it was shown that a study of the influence of H2O dilution on the combustion parameters by experimental and numerical approaches had to be carried out. As a consequence, the laminar burning velocities measurements were performed in a stainless steel spherical combustion from atmospheric pressure to high pressure (up to 0.5 MPa), at 473 K for an equivalence ratio at 1. These experimental results were satisfactorily compared with Chemical Workbench v.4.1 package in conjunction with GRIMech 3.0 reaction mechanism. The good correlations so obtained between experimental and calculated flame speed velocities showed the validity of the GRIMech 3.0 mechanism in this domain of combustion: high H2O dilution, low N2, medium pressure. Finally, good estimations of flame speed and pollutant emissions were determined in other conditions compatible with real gas turbine. In particular, mixtures (composed of CH4/O2/N2/H2O/ or CO2) leading to the same adiabatic temperature were investigated. Influences of oxygen enrichment and H2O dilution (compared to CO2) were disused.