Influence of ZrB2 hard ceramic reinforcement on mechanical and wear properties of aluminum

In this work, the effect of ZrB₂ (0, 5, 10 and 20?vol%) ceramic reinforcement on densification, structure, and properties of mechanically alloyed Al was investigated. The milling of Al-ZrB₂ powder compositions resulted in formation of agglomerates with varied size. In particular, the size of agglomerates was reduced considerably with increased addition of ZrB₂ to Al. Interestingly, the densification of hot pressed Al increased from 96.06% to 99.22% with ZrB₂ addition. The reduction of agglomerates size was attributed to the enhanced densification of Al-ZrB₂ composites. Pure Al showed relatively low hardness (0.94?GPa) and it was improved to 1.78?GPa with the addition of 20?vol% ZrB₂. The mechanical properties have significantly been improved for Al-ZrB₂ composites. Especially Al - 20?vol% ZrB₂ possessed a very high yield strength (529?MPa), compressive strength (630?MPa) and compressive strain of 19.25%. Realization of such a good combination of mechanical properties is the highest ever reported for Al composites so far in the literature. The coefficient of friction (COF) of Al-ZrB₂ varied narrowly between 0.33 and 0.40 after dry sliding wear against steel disc. The wear rate of Al-ZrB₂ composites was within mild wear regime and varied between 98.88?×?10^?6 and 34.66?×?10^?6 mm³/Nm. Among all the compositions, Al - 20?vol% ZrB₂ composite exhibited the lowest wear rate and high wear rate was noted for pure Al. Mild abrasion, tribo-oxidation, third body wear (wear debris) and delamination were the major material removal mechanisms for Al-ZrB₂ composites. Overall the hardness, strength and wear resistance of Al - 20?vol% ZrB₂ composite was improved by 84.3%, 84.3% and 64.2%, respectively when compared to pure Al.     

Mathematical model for computer simulation and control of steelmaking

A dynamic model for computer simulation and control of steelmaking has been developed. It is essentially based on multicomponent mixed transport control theory with the incorporation of energy balance calculations. The model is applicable to both steelmaking in electric furnaces as well as in oxygen steelmaking converters. The adjustable parameters of the model for simulation of oxygen steelmaking are gas evolution rate (G_co). oxygen flux factor (F_o) and emulsification factor (EM). These simulation parameters, when combined with on-line measurement of off-gas composition and temperature, enable complete dynamic control of the process. The model developed is applied, as an example, to an industrially produced heat in a top blown oxygen steelmaking converter and the results of simulation are discussed.     

New fault-location method for a single multiterminal transmission line using synchronized phasor measurements

This paper describes a new iterative method to locate a fault on a single multiterminal transmission line. The method uses synchronized voltage and current measurements from all terminals. Using positive-sequence components of the prefault and postfault waveforms, positive-sequence source impedances are estimated. Using these source impedances and the line data, the positive-sequence bus impedance matrix (Z/sub bus/) is formed. Using the properties of Z/sub bus/, an iterative algorithm is proposed. The algorithm first identifies the faulted section and then locates the fault on this section. This algorithm is applied to the data obtained from the Electromagnetic Transients Program simulation of a multiterminal transmission line. The simulation results show that the distinctive features of this method are that it performs with very high accuracy for all types of faults at different fault locations and is practically immune to fault resistance.     

Synthesis of graphene/vitamin C template-controlled polyaniline nanotubes composite for high performance supercapacitor electrode

A graphene nanosheet/polyaniline nanotube (GPNT) composite is prepared for the first time by in-situ chemical oxidative polymerization of aniline using vitamin C as a structure directing agent. The vitamin C molecules lead to the synthesis of polyaniline (PANI) nanotubes through the development of rod-like assembly by H-bonding in an aqueous medium. The initially synthesized graphene oxide/polyaniline nanotubes composite is reduced to graphene using hydrazine monohydrate followed by re-oxidation and protonation of the PANI to produce the GPNT nanocomposite. This novel composite showed a high specific capacitance of 534.37 F/g and an excellent energy density of 74.27 Wh/kg at a constant current of 0.5 mA. Besides, the GPNT composite exhibited excellent cycle life with 91.4% specific capacitance retained after 500 charge-discharge cycles. The excellent performance is due to the synergistic combination of graphene which provides good electrical conductivity and mechanical stability, and PANI nanofiber which deals with good redox activity.     

Cold Model Investigations of Melting of Ice in a Gas-Stirred Vessel

The melting of steel scrap in high temperature liquid iron melt is investigated by conducting cold model experiments of the melting of ice sample of different geometries and sizes in an argon-stirred vessel containing water. The melting process of ice samples is observed using a high-speed camera. Design of experiments is based on similarity criteria. The relationships between non-dimensional groups related to heat transfer (Nu, Re, Pr, and Gr) are derived for different experimental conditions. The results are compared with those reported in the literature. The heat transfer coefficient is estimated as a function of mixing power and is found to be in good agreement with the calculated values obtained by using reported relationships in literature.     

Mixed valency character of bismuth in ferrite lattices

Abstracts are not published in this journal This revised version was published online in November 2006 with corrections to the Cover Date.     

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method

This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra‐patient parametric variation and non‐availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed‐loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post‐prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed‐loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post‐prandial hyperglycemia are significantly reduced in the presence of bounded (±30% ) parametric variability and uncertain exogenous meal disturbance.Inspec keywords: medical control systems, observers, uncertain systems, nonlinear control systems, robust control, control system synthesis, linear matrix inequalities, feedback, sugar, closed loop systems, diseasesOther keywords: virtual T1D patients, type 1 diabetes patients, closed‐loop simulations, uncertain conditions, post‐prandial hyperglycemia, designed controller, closed‐loop response, controller gains, linear matrix inequality, controller conditions, T1D patient model, control technique, intra‐patient parametric variation, principal objective, plasma glucose concentration, uncertain nonlinear system, robust observer based output feedback control law, attractive ellipsoid method, plasma glucose regulation     

Adaptive neural net (ANN) models for desulphurization of hot metal and steel

Neural nets can be adapted to complex patterns of interrelated input and output variables in a process even if the data sets contain a lot of noise. In this work two specific examples for the application of adaptive neural nets (ANN) in steel Industry are described. First, the sulphur content of hot-metal, obtained at the end of calcium carbide powder injection into 4001 torpedo ladles is predicted as a function of hot-metal weight, treatment time, initial sulphur content, gas flow rate and powder injection rate. The values predicted by the trained ANN model for a completely new set of input test data compare well with the actual values obtained on the shop floor. In the second example, the sulphur content of steel, obtained at the end of blow is predicted as a function of liquid-metal weight, total amount of oxygen injected, amount of iron ore added, and the temperature, contents of carbon, manganese, phosphorus and sulphur determined by in-blow sampling in a 300 t converter. The ANN predicted values of sulphur content of steel at tap (without reblow) also agree well with the values obtained on the shop floor.     

Optimization of back propagation algorithm and GAS-assisted ANN models for hot metal desulphurization

Adaptive neural net (ANN) model of hot metal desulphurization is first optimized by various search methods including the golden section search and Davies-Swann-Campey methods. Logarithmic preprocessing of input data leads to a further improvement in generalization ability of the net. Genetic adaptive search (GAS) method is used to optimize the mathematical model for desulphurization and when the input data are preprocessed with this optimized model and fed into an artificial neural net, the generalization ability of the net becomes even better. Best results are obtained when using GAS to optimize the interconnection weights during the training phase, while training data are preprocessed through a mathematical model already optimized by GAS. For every process several options presented by a combination of ANN and GAS must be systematically investigated before choosing the ultimate model for predictions on shop floor.     

The role of smart packaging system in food supply chain

Food supply chain is a rapidly growing integrated sector and covers all the aspects from farm to fork, including manufacturing, packaging, distribution, storing, as well as further processing or cooking for consumption. Along this chain, smart packaging could impact the quality, safety, and sustainability of food. Packaging systems have evolved to be smarter with integration of emerging electronics and wireless communication and cloud data solutions. Although there are many factors causing the loss and waste issues for foods throughout the whole supply chain of food and there have been several articles showing the recent advances and breakthroughs in developing smart packaging systems, this review integrates these conceptual frameworks and technological applications and focuses on how innovative smart packaging solutions are beneficial to the overall quality and safety of food supply by enhancing product traceability and reducing the amount of food loss and waste. We start by introducing the concept of the management for the integrated food supply chain, which is critical in tactical and operational components that can enhance product traceability within the entire chain. Then we highlight the impact of smart packaging in reducing food loss and waste. We summarize the basic information of the common printing techniques for smart packaging system (sensor and indicator). Then, we discuss the potential challenges in the manufacturing and deployment of smart packaging systems, as well as their cost-related drawbacks and further steps in food supply chain.