Prediction of throw in bench blasting using neural networks: an approach

Tailoring the muckpile shape and its fragmentation to the requirements of the excavating equipment in surface mines can significantly improve the efficiency and savings through increased production, machine life and reduced maintenance. Considering the various blast parameters together to predict the throw is subtle and can lead to wrong conclusions. In this paper, a different approach was followed to combine the representational power of multilayer neural networks and various machine learning techniques to predict the throw of a bench blast using the data from a limestone mine located in central India. Then, using various analysis techniques, the training parameters have been adjusted to reduce the cross-validation error and increase the accuracy. Here, four different architectures of neural networks have been trained by different techniques, and the best model has been selected. The different machine learning techniques have been implemented on the basis of accuracy of the output. The sensitivity analysis has been done to get the relative importance of the variables in prediction of the output.

     

Effect of flow pattern on cellulase deactivation in stirred tank bioreactors

In biochemical process industries, the dynamic environment within the bioreactor and in the purification equipment is known to affect the enzyme activity and yield of enzyme production. This has drawn our attention to examine the effect of various flow parameters on the deactivation behavior of enzyme in stirred tank reactor. In the present work, cellulase deactivation was investigated in 0.1,0.3 and 0.57 m i.d. stirred vessels with single and dual impeller. Enzyme solution was subjected to hydrodynamic stress using various types of impeller and impeller combinations over a wide range of power consumption (). The effects of tank diameter, impeller diameter, blade width, blade angle and number of blades were studied on the extent of deactivation. The results have been compared with the previously published literature. Attempts have been made to relate the extent of deactivation with the flow pattern (maximum and average value of turbulent energy dissipation rate, average shear rate and average turbulent normal stress). The extent of cellulase deactivation has been found to correlate well with the average turbulent normal stress within the stirred vessel.     

Highly active robust oxide solid solution electro-catalysts for oxygen reduction reaction for proton exchange membrane fuel cell and direct methanol fuel cell cathodes

The identification and development of novel non-noble metals based electro-catalyst exhibiting excellent electrochemical activity and stability than noble metal electro-catalyst is important for commercial development of proton exchange membrane fuel cells (PEMFCs). Such non-noble electro-catalyst with unique electronic structure and superior electrochemical performance will immensely contribute to lowering the capital cost of PEMFCs. Herein, we have identified solid solution electro-catalysts of WO₃ and IrO₂ for oxygen reduction reaction (ORR) in PEMFCs exploiting theoretical first principles approaches. The theoretical results were experimentally validated by generation of nanostructured (W_1-xIr_x)O_y (x = 0.2, 0.3; y = 2.7–2.8) electro-catalysts for ORR. (W_0.7Ir_0.3)O_y demonstrated ～43% improved electrochemical activity than Pt/C with similar loading at 0.9 V (vs RHE), respectively. Moreover, single full cell PEMFC study revealed an acceptable ～81% improved maximum power density for (W_0.7Ir_0.3)O_y than Pt/C combined with excellent long term stability. These results thus, show the potential of (W_0.7Ir_0.3)O_y as ORR electro-catalyst for replacing of Pt/C in PEMFCs and direct methanol fuel cells on the additional grounds of superior methanol tolerance.     

Mathematical Modelling for Removal of Mixture of Heavy Metal Ions from Waste-Water Using Micellar Enhanced Ultrafiltration (MEUF) Process

The micellar enhanced ultrafiltration (MEUF) was studied for the removal of heavy metals using a mixture of non-ionic surfactant TWEEN-80 and anionic surfactant sodium dodecyl sulphate (SDS). The gel polarization model and resistance-in-series model were used to estimate the mass transfer coefficient (3 x 10-6 m/s) and the permeate flux. The total metal ion concentration was varied from 1 mM-4 mM and the corresponding effect of the trans-membrane pressure and limiting fluxes were studied. The modified resistance-in-series model was applied to these experimental data to correlate the flux with the feed concentration and applied pressure.     

Separation of liquid–liquid dispersion in a batch settler: CFD-PBM simulations incorporating interfacial coalescence

Several commercially important chemical processes involve liquid–liquid phase separation. In the present work, we have developed a multi-fluid Eulerian CFD model using OpenFOAM that incorporates binary and interfacial coalescence processes. We simulated separation of kerosene dispersed in water in a batch settler and validated the predictions using the measurements of time-evolution of coalescing and settling interfaces, local dispersed-phase volume fraction (α_O) and drop size distribution (DSD). Simulations are performed to understand the contributions of binary and interfacial coalescence processes to the phase separation process. While the time-evolution of coalescing and settling fronts can be predicted reasonably well using the two-fluid model with empirically-corrected drag models, local α_O and DSD could not be predicted. We have shown that the comprehensive multi-fluid Eulerian model, which incorporates binary and interfacial coalescence, predicts the time-evolution of the coalescing and settling fronts, local α_O and the DSD in an excellent agreement with the measurements.     

Laser dressing of alumina grinding wheels

High-power lasers are being explored as a non-contact-type dressing tool for alumina grinding wheels. The alumina grinding wheel surface underwent melting and/or vaporization on the surface when laser-dressed, forming a modified layer on the surface. Refinement of the grain size took place. The individual particles that formed on the surface had well-defined faceted structures. Microcutting edges were generated on the individual grains and particles, which can act as cutting edges for efficient grinding. The results of x-ray diffraction and pole figure analysis suggested that the formation of these faceted structures was due to the preferential orientation of the grains after dressing. This paper was presented at the fourth International Surface Engineering Congress and Exposition held August 1–3, 2005 in St. Paul, MN.     

A cBN-TiN composite coating for carbide inserts: Coating characterization and its applications for finish hard turning

Cubic boron nitride (cBN)-titanium nitride (TiN) composite coating combines the thermal stability and super abrasiveness of cubic boron nitride (cBN) and the good lubricity of TiN, offering the opportunity for designing cutting tools with application specific new geometries (flat, chip breaker, and round shape) and cost effectiveness. In particular, the cBN based coating on carbide inserts is complementary to widely used polycrystalline cubic boron nitride (PCBN) compact tools for finish hard turning applications. This paper reports the results of a study addressing the surface morphology, surface roughness, coating cross section, chemical composition, crystal structure, microhardness, adhesion, and the wear life of this cBN-based coating deposited on carbide inserts (SNMG432) for finish turning of hardened AISI 4340 steel bars. The surface quality of machined workpieces in terms of their surface roughness and white layer formation are also analyzed and the results are presented.