Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Melting and mechanical properties of polymer grafted lipid bilayer membranes

The influence of polymer grafting on the phase behavior and elastic properties of two tail lipid bilayers have been investigated using dissipative particle dynamics simulations. For the range of polymer lengths studied, the L(c) to L(alpha) transition temperature is not significantly affected for grafting fractions, G(f) between 0.16 and 0.25. A decrease in the transition temperature is observed at a relatively high grafting fraction, G(f) = 0.36. At low temperatures, a small increase in the area per head group, a(h), at high G(f) leads to an increase in the chain tilt, inducing order in the bilayer and the solvent. The onset of the phase transition occurs with the nucleation of small patches of thinned membrane which grow and form continuous domains as the temperature increases. This region is the co-existence region between the L(beta)(thick) and the L(alpha)(thin) phases. The simulation results for the membrane area expansion as a function of the grafting density conform extremely well to the scalings predicted by self-consistent mean field theories. We find that the bending modulus shows a small decrease for short polymers (number of beads, N(p) = 10) and low G(f), where the influence of polymer is reduced when compared to the effect of the increased a(h). For longer polymers (N(p) > 15), the bending modulus increases monotonically with increase in grafted polymer. Using the results from mean field theory, we partition the contributions to the bending modulus from the membrane and the polymer and show that the dominant contribution to the increased bending modulus arises from the grafted polymer. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3631940]

Adherent Nanoporous Anatase TiO2 Membranes on Stainless Steel Substrates

Nanostructured TiO2 is one of the most commonly used materials in photocatalytic applications and photochemical solar cells. This article describes a method to synthesize nanoporous anatase TiO2 membranes directly on stainless steel (SS), an easily available substrate by anodization to form amorphous TiO2 and a subsequent heat treatment to convert it into anatase, the photoactive phase. To obtain adherent membranes with interfaces that are resistant to peeling, both anodization and heat treatment parameters need to be optimized to obtain a heterostructure that contains a Ti film between the TiO2 membrane and the substrate.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Chitosan-polyvinyl alcohol-sulfonated polyethersulfone mixed-matrix membranes as methanol-barrier electrolytes for DMFCs

Chitosan (CS)-polyvinyl alcohol (PVA) cross-linked with sulfosuccinic acid (SSA) and modified with sulfonated polyethersulfone (SPES) mixed-matrix membranes are reported for their application in direct methanol fuel cells (DMFCs). Polyethersulfone (PES) is sulfonated by chlorosulfonic acid and factors affecting the sulfonation reaction, such as time and temperature, are studied. The ion-exchange capacity, degree of sulfonation, sorption, and proton conductivity for the mixed-matrix membranes are investigated. The mixed-matrix membranes are also characterised for their mechanical and thermal properties. The methanol-crossover flux across the mixed-matrix membranes is studied by measuring the mass balance of methanol using the density meter. The methanol cross-over for these membranes is found to be about 33% lower in relation to Nafion-117 membrane. The DMFC employing CS-PVA-SPES mixed-matrix membrane with an optimum content of 25 wt % SPES delivers a peak power-density of 5.5 mW cm-2 at a load current-density of 25 mA cm-2 while operating at 70 degrees C. (C) 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

Chemically enhanced thermal stability of anodized nanostructured zirconia membranes

Anodized nanotubular and nanoporous zirconia membranes are of interest for applications involving elevated temperatures in excess of 400 degrees C, such as templates for the synthesis of nanostructures, catalyst supports, fuel cells and sensors. Thermal stability is thus an important attribute. The study described in this paper shows that the as-anodized nanoporous membranes can withstand more adverse temperature-time combinations than nanotubular membranes. Chemical treatment of the nanoporous membranes was found to further enhance their thermal stability. The net result is an enhancement in the limiting temperature from 500 degrees C for nanotubular membranes to 1000 degrees C for the chemically treated nanoporous membranes. The reasons for membrane degradation on thermal exposure and the mechanism responsible for retarding the same are discussed within the framework of the theory of thermal grooving.