Influence of coke structure on coke quality using image analysis method

The quality of coke affects the performance of the blast furnace, factors affecting coke quality include coal properties, coal charge granulometry and carbonization conditions. The coke properties in elude the size analysis, cold strength (Micum Indices-M4(). MI0) and hot strength (Coke Reactivity Index-CRI, Coke Strength after Reaction-CSR) properties and structural properties such as coke structure and texture. Structural properties comprise the porosity, pore-cell wall thickness and pore sizes, while textures consist of the carbon forms in the coke. In present work, advanced method such as image analysis method was used to interpret coke microstructure. Conventional methods such as determination of coke porosity by measurement of real and apparent density and mercury porosimetry have a number of limitations. Coke size, magnification, number of image frames captured, process of pellet preparations and coke properties such as M4(), M|0, CRI and CSR (low, medium and high values) were taken as variables for experimental purposes. The coke structure parameters such as porosity, length, perimeter, breadth, roundness, pore-wall thickness and pore size distribution of the pores were determined by image analysis method. This method provided average porosity in addition to pore-wall thickness and pore-size distribution. The pore wall thickness measuremenl by image analysis method provided significant correlations with M40, CRI and CSR values. This explained the usability of image analysis for coke structure measurement.

Underground mining of thick coal seams

This paper reviews underground mining methods for total thickness of a thick coal seam in single lift （TI＇rCSSL）. Review shows the required engineering for extraction of thick seams needs to be fitted with thickness of the seam, behavior of rock-mass and surrounding stress conditions for efficient mining. Variants of TI＇rCSSL are able to extract a maximum 10-12 m thickness only. An improvement in bending moment of the overlying coal band in longwall top coal caving （LTCC） provides better under-winning opportunity for the roof coal band. An acceptable limit of 25 MPa compressive strength of coal for the success of LTCC may be increased under favorable geo-technical conditions. Bord and pillar in India adopted induced caving of roof coal band for single lift depillaring of total thickness （SLDTr） of a compe- tent thick coal seam developed along floor. Case studies are given to arrest the adverse effects of extrac- tion height on pillars.

Optimization of Electrical Parameters for Production of Carbon Nanotubes

For more than two decades, there had been extensive research on the production of carbon nanotubes (CNT) and opti- mization of its manufacture for the industrial applications. It is believed that they are the strong enough but most flexi- ble materials known to mankind. They have potential to take part in new nanofabricated materials. It is known that, carbon nanotubes could behave as the ultimate one-dimensional material with remarkable mechanical properties. More- over, carbon nanotubes exhibit strong electrical and thermal conducting properties. In the process of optimizing the production in line with the industrial application, the researchers have found a new material to act as an anode i.e. coal, which is inexpensive as compared to graphite. There are various methods such as arc discharge, laser ablation, chemical vapour deposition (CVD), template-directed synthesis and the use of the growth of CNTs in the presence of catalyst particles. The production of carbon nanotubes in large quantities is possible with inexpensive coal as the starting carbon source by the arc discharge technique. It is found that a large amount of carbon nanotubes of good quality can be ob- tained in the cathode deposits in which carbon nanotubes are present in nest-like bundles. This paper primarily concen- trates on the optimising such parameters related to the mass production of the product. It has been shown through Sim- plex process that based on the cost of the SWNT obtained by the arc discharge technique, the voltage and the current should lie in the range of 30 - 42 V and 49 - 66 A respectively. Any combination above the given values will lead to a power consumption cost beyond the final product cost, in turn leading to infeasibility of the process.

Parametric Optimization of PMEDM Process using Chromium Powder Mixed Dielectric and Triangular Shape Electrodes

In this article, parametric optimization for material removal rate (MRR) and tool wear rate (TWR) study on the powder mixed electrical discharge machining (PMEDM) of EN-8 steel has been carried out. Response surface methodology (RSM) has been used to plan and analyze the experiments. Average current, duty cycle, angle of electrode and concentration of chromium powder added into dielectric fluid of EDM were chosen as process parameters to study the PMEDM performance in terms of MRR and TWR. Experiments have been performed on newly designed experimental setup developed in laboratory. Most important parameters affecting selected performance measures have been identified and effects of their variations have been observed.

Rice Disease Diagnosis System (RDDS)

Hitherto,Rice(Oryza Sativa)has been one of the most demanding food crops in the world,cultivated in larger quantities,but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern.During cultivation,the crops are most prone to biotic stresses such as bacterial,viral,fungal diseases and pests.These stresses can drastically damage the crop.Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy.The proven methods of molecular biology can provide accurate detection of pathogenic factors,but these methods are not accessible to the majority of the farmers,needs high costs or resources,and require domain knowledge to implement.Expert’s field inspection report provides precise crop diagnosis but continuous field inspection over the remotely placed agriculture fields is not feasible.Therefore,cost effective approach for early detection of diseases can help farmers to take necessary steps in time to boost up the crop production.Precision agriculture makes use of decision support systems built using Machine Learning(ML)or Deep Learning(DL)approaches to cut down heavy costs.Timely crop diagnosis process can be automated with the involvement of Computer Vision,Image Processing and Deep Learning(DL)based methods for more precise prediction in less cost and time.Latest research shows that more accurate image classification can be implemented using Deep Learning based Convolutional Neural Network(CNN)model.In this paper,we have proposed an automated Rice Disease Diagnosis System(RDDS)for timely,more accurate and detailed crop disease diagnosis,which consisting of two modules,they are Leaf Disease Identification(LDI)module for disease detection and Infection Intensity Estimation(IIE)module for disease severity analysis.The LDI module is based on the proposed novel RDD_CNN model that classified the eight most harmful and commonly occurring diseases,it has obtained the best test accuracy of 98.47%when compared to its first three versions.And the IIE module is designed for estimating identified disease’s intensity in terms of extent and stage of infection providing detailed and overall diagnosis report specially designed for Brown Spot disease.