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.     

Effect of Compressive Load on Uplift Capacity of Model Piles

Thirty six tests on model tubular steel piles embedded in sand were carried out in the laboratory to assess the effects of compressive load on uplift capacity of piles considering various parameters. The model piles were of 25 mm outside diameter and 2 mm wall thickness. The soil–pile friction angles were 21 and 29° in loose and dense conditions of sand. The piles were embedded in sand for embedment length/diameter ratios of 8,16, and 24 inside a model tank. They were subjected to a static compressive load of 0, 25, 50, 75, and 100% of their ultimate capacity in compression and subjected to pull out loading tests. The experimental results indicated that the presence of the compressive load on the pile decreases the net uplift capacity of a pile and the decrease depends on the magnitude of the compressive load. A logical approach, based on the experimental results, has been suggested to predict the net uplift capacity of a pile considering the presence of compressive load.     

IEEE-PES Transmission and Distribution Conference and Exposition Call for Papers

Static network-related system voltage stability margin (VSM) depends on the availability of reactive power to support the transport of real power from sources to sinks. Based on this premise, the total VAr loss is minimized in the unified OPF framework considering real and reactive power controllers, and its effect on VSM is studied. Studies are conducted on a three-bus system, the IEEE 30-bus system, and a 191-bus Indian electric power system, and their results are reported.     

Microwave-Assisted Synthesis of Fine Particle Oxides Employing Wet Redox Mixtures

Interaction of electromagnetic radiation with a physical mixture of metal nitrates and amides/hydrazides is observed to initiate high-temperature reactions, useful for realizing several high-temperature ceramic materials. A judicious choice of such redox mixtures undergoes exothermic reactions when they couple with microwave radiation. The coupling of electromagnetic radiation with metal salts and amides/hydrazides depends on the dielectric properties of the individual components in the reaction mixture. The approach has been used to prepare γ-Fe₂O₃, Fe₃O₄, MgCr₂O₄, α-CaCr₂O₄, and La_0.7Ba_0.3MnO₃.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

Aggregation dynamics of particles in a microchannel due to an applied magnetic field

Functionalized magnetic microspheres have promising applications in different microfluidic devices including MEMS-scale biosensors. These particles exhibit magnetic field-induced aggregation, which can be harnessed to achieve several practical tasks in microfluidic devices. For this, the particle aggregation needs to be well characterized. Herein, a numerical simulation and experimental validation of particle-chaining is presented. Simulations show that the particle aggregation time scales linearly with a group parameter. The predicted growth of one- two- and three-particle chains with time shows a similar trend as that found in the experiments. The results of the study could help predicting the performance of magnetic aggregate-based lab-on-a-chip devices.     

Glass fiber-reinforced epoxy composites

Glass fiber-reinforced epoxy composites were prepared from the matrix resins tetraglycidyl diaminodiphenylmethane

1 Systematic name: N,N,N′,N′-Tetrakis(2,3-epoxypropyl)-4,4′-diaminodiphenylmethane.

(TGDDM) and tetraglycidyl bis(o-toluidino)-methane

2 Systematic name: N,N,N′,N′-Tetrakis(2,3-epoxypropyl)-4,4′-bis(o-toluidino)methane.

(TGMBT) using various amines like 4,4′-diaminodiphenylmethane (DDM), 4,4′-diaminodiphenylsulfone (DDS) and diethylene triamine (DETA) as curing agents. The fabricated laminates were evaluated for their mechanical and dielectrical properties and chemical resistance. The composites prepared using an epoxy fortifier (20 phr) showed significant improvement in the mechanical properties.     

Functionalized Reduced Graphene Oxide as a Versatile Tool for Cancer Therapy

Cancer is one of the deadliest diseases in human history with extremely poor prognosis. Although many traditional therapeutic modalities—such as surgery, chemotherapy, and radiation therapy—have proved to be successful in inhibiting the growth of tumor cells, their side effects may vastly limited the actual benefits and patient acceptance. In this context, a nanomedicine approach for cancer therapy using functionalized nanomaterial has been gaining ground recently. Considering the ability to carry various anticancer drugs and to act as a photothermal agent, the use of carbon-based nanomaterials for cancer therapy has advanced rapidly. Within those nanomaterials, reduced graphene oxide (rGO), a graphene family 2D carbon nanomaterial, emerged as a good candidate for cancer photothermal therapy due to its excellent photothermal conversion in the near infrared range, large specific surface area for drug loading, as well as functional groups for functionalization with molecules such as photosensitizers, siRNA, ligands, etc. By unique design, multifunctional nanosystems could be designed based on rGO, which are endowed with promising temperature/pH-dependent drug/gene delivery abilities for multimodal cancer therapy. This could be further augmented by additional advantages offered by functionalized rGO, such as high biocompatibility, targeted delivery, and enhanced photothermal effects. Herewith, we first provide an overview of the most effective reducing agents for rGO synthesis via chemical reduction. This was followed by in-depth review of application of functionalized rGO in different cancer treatment modalities such as chemotherapy, photothermal therapy and/or photodynamic therapy, gene therapy, chemotherapy/phototherapy, and photothermal/immunotherapy.