Sustainable Hydrogen Evaluation in Logistics; SHEL

© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Canadian Hydrogen and Fuel Cell Association. Open access under CC BY-NC-ND licenseMaterials handling vehicles are currently powered by either electric motor based on lead-acid batteries or combustion engines employing diesel or liquefied petroleum gas. Fuel cells offer significant advantage over the competing technology. SHEL is a three-year European project involving 13 partners from six countries. The overall aim of the project is to deploy 10 fuel-cell powered forklift trucks and associated hydrogen refuelling infrastructure across 3 sites in Europe. Real time information will be gathered, and efficient procedures will be developed to reduce the time required for product certification and infrastructural build approval

Materials Handling Vehicles : Policy Framework for an Emerging Fuel Cell Market

© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Canadian Hydrogen and Fuel Cell Association. Open access under CC BY-NC-ND licenseThere are several challenges to wide-spread commercialisation of the technology hydrogen fuel-cell technology; including reliability and cost implications, infrastructure requirements, and safety aspects of the upcoming technology. Targeted policy initiatives are required to address two significant bottlenecks; reliability and cost constraints. Such policy measures and financial mechanisms providing incentives for manufacturers and end-users of the novel technology create an initial impetus for the introduction of the forthcoming technology into the market place. The current approach, policy mechanisms and their impacts are reviewed in the context of demonstration projects, deploying material handling equipment, involving public-private initiatives.Final Published versio

A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach

Studies examining nanoparticles (NPs) and hazardous elements (HEs) contained in suspended sediments (SSs) are vital for watershed administration and ecological impact evaluation. The biochemical consequence of titanium-nanoparticles (Ti-NPs) from SSs in Colombia's Magdalena River was examined utilizing an innovative approach involving nanogeochemistry in this study. In general, the toxicity and the human health risk assessment associated with the presence of some Ti-NPs + HEs in SSs from riverine systems need to be determined with a robust analytical procedure. The mode of occurrence of Ti-NPs, total Ti and other elements contained within SSs of the Magdalena River were evaluated through advanced electron microscopy (field emission scanning electron microscope-FE-SEM and high resolution transmission electron microscope-HR-TEM) coupled with an energy dispersive X-ray microanalysis system (EDS); X-Ray Diffractions (XRD); and inductively coupled plasma-mass pectrometry (ICP-MS). This work showed that enormous quantities of Ti-NPs were present in the river studied and that they displayed diverse eochemical properties and posed various possible ecological dangers. Ti-NP contamination indices must be established for measuring the environmental magnitudes of NP contamination and determining contamination rank among rivers. Finally, SS contamination guidelines must be recommended on an international level. This study contributes to the scientific understanding of the relationship of HE and Ti-NP dynamics from SSs in riverine systems around the world.Keywords Titanium-nanoparticles, Rutile and Anatase nano-minerals, Particle mode of occurrence, Size-dependent properties, Nanomineral–water interface, Surface particle geochemistry

BUCKLING ASSESSMENT OF IMPERFECT CYLINDRICAL SHELLS UNDER AXIAL LOADS USING A GEP TECHNIQUE

Determination of buckling behavior of shell structures has long been identified as a challenging task. This is largely because the buckling behavior is masked by local large deformations, which occur once the critical load is reached. This research proposes a novel technique to accurately predict the buckling load of imperfect cylindrical shells using gene expression programming (GEP), which is an evolutionary artificial intelligence method. An existing experimental data bank was employed for training and testing the program, and the obtained buckling load of shell structures were accordingly verified. From the results, it is concluded that GEP is a promising and reliable method to determine the buckling load of shell structures subjected to axial compression

Big Data Analytics and Its Applications in Supply Chain Management

In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM)

Effects of Brace Configuration and Structure Height on Seismic Performance of BRBFs Based on the Collapse Fragility Analysis

The brace configuration and structure height are two factors that have a significant effect on the seismic behavior of braced frame buildings. In the present study, the buckling-restrained braced (BRB) frames were considered to estimate the effect of these two parameters using probabilistic seismic assessment methods. The uncertainty in the different parameters involved in the seismic design of the structural system was also considered. Four, six, and ten-story buildings with the Chevron and inverted Chevron bracing configurations were designed, and their responses due to various ground motions were estimated using incremental nonlinear dynamic analyses. Fragility curves, mean annual frequency of exceeding immediate occupancy (IO), and collapse prevention (CP) states were generated using probabilistic seismic analysis, fragility curves concept, and drift hazard curves. The results demonstrate that the inverted Chevron type BRBFs has better structural performance than Chevron bracing types. Furthermore, an increase of the height of structures, despite lower drift’s hazards, increases the fragility probability

A NEW ALGORITHM IN NONLINEAR ANALYSIS OF STRUCTURES USING PARTICLE SWARM OPTIMIZATION

Solving systems of nonlinear equations is a difficult problem in numerical computation. Probably the best known and most widely used algorithm to solve a system of nonlinear equations is Newton-Raphson method. A significant shortcoming of this method becomes apparent when attempting to solve problems with limit points. Once a fixed load is defined in the first step, there is no way to modify the load vector should a limit point occur within the increment. To overcome this defect, displacement control methods for passing limit points can be used. In displacement control method, the load ratio in the first step of an increment is defined so that a particular key displacement component will change only by a prescribed amount. In this paper the load ratio is obtained using particle swarm optimization (PSO) algorithm so that the complex behavior of structures can be followed, automatically. Design variable is load ratio and its unbalanced force is also considered as objective function in optimization process. Numerical results are performed under geometrical nonlinear analysis, elastic post-buckling analysis and inelastic post-buckling analysis. The efficiency and accuracy of proposed method are demonstrated by solving these examples. 

Gene expression programming application for prediction of ultimate axial strain of FRP-confined concrete

The last three decades have seen increasing applications of fiber-reinforced polymer materials in structural engineering because of their many advantages over traditional strengthening and reinforcing materials. On the other hand, soft computing approaches have recently been widely used to model human activity in many areas of civil engineering applications. This paper presents the use of genetic expression programming as a tool to predict the ultimate axial strain of fiber-reinforced polymer-confined concrete. A large experimental data set (219) of these tests is collected from published literature. The prediction of the proposed new genetic expression programming-based model was compared with the results obtained using the existing analytical equations proposed in the current literature. In this paper, attempts were made to present a complete review of genetic expression programming in structural engineering. Good agreement between the experimental data and predicted results is obtained

A framework for exploration and cleaning of environmental data : Tehran air quality data experience

Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013. ; The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase.; The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR.  The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data.; We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries