Plug-load densities for energy analysis: K-12 schools

Benchmarking plug-load densities is essential to bypass arbitrary and/or incorrect inputs used in building energy analysis. As more building simulationists play a decision-making role for the design team, they tend to lean on building energy standards and guidelines for preliminary inputs such as plug-load densities. It is necessary for building energy standards and rating systems to implement plug-load density benchmarks to reward design teams in their efforts to reduce plug-load energy use. Using case study buildings, this paper establishes benchmark plug-load densities for K-12 schools under two new categories – classrooms with computers and classrooms without computers. Eighteen K-12 schools including 9 elementary, 2 middle, and 7 high schools are assessed for actual plug-load densities. For the same case study buildings, four existing approaches – NREL, COMNET, ASHRAE 90.1-1989, and Title-24 are evaluated for plug-load densities. Results show under- and over-estimation of plug-load densities over actual densities. The development of benchmark for K-12 schools will pave way for instituting targets for trimming plug-load densities in new and retrofit building projects.     

Friction Buttering: A New Technique for Dissimilar Welding

This work offers a fresh perspective on buttering, a technique often considered for fusion welding of dissimilar metals. For the first time, buttering was attempted in solid state using friction deposition. Using this new “friction buttering” technique, fusion welding of two different dissimilar metal pairs (austenitic stainless steel/borated stainless steel and Al-Cu-Mg/Al-Zn-Mg-Cu) was successfully demonstrated. The results show that friction buttering can simplify a tough dissimilar welding problem into a routine fusion welding task.     

A figure of merit for evaluating sustainability of renewable energy systems

For grading different types of renewable energy based electricity generation systems, a figure of merit has been proposed and evaluated. The figure of merit has been obtained by analyzing total energy utilization in constructing and installing the systems and by their respective electricity generation. The gross carbon emission has also been estimated for evaluation of figure of merit. This figure of merit clearly indicates that wind power is the most sustainable source for electricity generation, followed by small hydropower and solar photovoltaics.     

Fluxless arc weld-brazing of aluminium alloy to steel

In this work, joining of aluminium alloy AA6061-T6 to Interstitial Free steel using pulsed gas metal arc welding process has been attempted. The effect of different surface conditions of steel (viz, galvanized, galvanealed and uncoated) and gap between the sheets on braze joint formation have been investigated. Galvanized steel surface showed good bead width, joint formation and lap shear strength compared to the other two combinations. Interface gap has not affected the wetting behaviour significantly but presence of a gap of 300 μm or so helped in escape of zinc vapour during the process there by avoiding formation of any crevice or macroporosity in the joint. Features and properties of the joint are characterized by metallography, fractography, energy dispersive spectroscopy (EDS) and lap shear tests. Load carrying capacity of Al-Galvanized steel was highest (222 N/mm of seam length) compared to other combinations, aided by better wetting due to presence of Zn on the surface and minimum porosity due to interfacial gap provided during brazing.     

A case study of optimising UG2 flotation performance. Part 1: Bench,pilot and plant scale factors which influence Cr2O3 entrainment in UG2 flotation

In the Platinum Group Minerals (PGM) industry, almost all UG2 deposits being currently developed are deep level and bankable feasibility studies must rely entirely on batch testwork of borecore samples. Since UG2 has a significant chromite (FeO·Cr₂O₃) content, minimising chromium oxide (Cr₂O₃) recovery into final concentrate has become a critical aspect of downstream smelting since % Cr₂O₃ in furnace feed has a practical upper limit of about 2.5%. In most cases concentrate is toll smelted but penalties are severe depending on chromite content. Together with PGMs, the characterisation of chromite floatability and prediction of recovery and concentrate grade in a production plant using bench-scale data is therefore a key component of UG2 ore analysis. The need to accurately predict chromite recovery and particularly to identify characteristics that indicate when % Cr₂O₃ in concentrate can be reduced has a significant impact on the project’s financials and subsequent plant design.The natural floatability of chromite is low and its passage into concentrate is almost entirely by entrainment in water carry-over into concentrate whereas PGMs are recovered by true flotation. Optimising flotation performance has to accommodate these two completely different flotation behaviours in an effort to reject chromite whilst at the same time maximise Platinum Group Minerals (PGM) recovery and grade.Incorporating extensive testwork conducted by Barrick Platinum South Africa and Northam and other plant data, the paper traces chromite, and to a lesser extent PGM, floatability in bench, pilot and plant scales and highlights that certain mechanisms of chromite entrainment are consistent irrespective of cell size and efficiency.     

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.     

Synthesis of ultra-small silicon nanoparticles by femtosecond laser ablation of porous silicon

We report a detailed study on the synthesis of ultra-small (1–10 nm) colloidal silicon nanoparticles (Si NPs) by ablating porous silicon (pSi) in acetone using femtosecond laser pulses. Porous silicon is considered as a target material for ablation because it contains a large number of light emitting silicon nanoparticles. The pSi samples were prepared by anodic etching of silicon in aqueous HF solution for different etching current densities. Transmission electron microscope measurements confirmed the successful formation of well-isolated spherical silicon nanoparticles. The average size of spherical NPs were estimated to be ~7.6, ~7, and ~6 nm when anodic etching current densities of 5, 10, and 20 mA/cm² were used respectively for preparing pSi targets. The crystallinity of these Si NPs was confirmed by selective area electron diffraction and Raman spectroscopy measurements. The observed blue shift in the absorption and emission spectra are attributed to reduction in the average particle size with increase in etching current density. These Si NPs may be useful for fabricating low-dimensional microelectronic compatible photonic devices.