An advanced ash fusion study on the melting behaviour of coal,oil shale and blends under gasification conditions using picture analysis and graphing method

This study investigates the potential of solid fuel blending as an effective approach to manipulate ash melting behaviour to alleviate ashrelated problems during gasification,thus improving design,operability and safety.The ash fusion characteristics of Qinghai bituminous coal together with Fushun,Xinghua and Laoheishan oil shales(and their respective blends)were quantified using a novel picture analysis and graphing method,which incorporates conventional ash fusion study,dilatometry and sintering strength test,in a CO/CO_(2)atmosphere.This imagebased characterisation method was used to monitor and quantify the complete melting behaviour of ash samples from room temperature to 1520℃.The impacts of blending on compositional changes during heating were determined experimentally via Xray diffraction and validated computationally using FactSage.Results showed that the melting point of Qinghai coal ash to be the lowest at 1116℃,but would increase up to 1208℃,1161℃and 1160℃with the addition of 30%50%of Laoheishan,Fushun,and Xinghua oil shales,respectively.The formation of highmelting anorthite and mullite structures inhibits the formation of lowmelting hercynite.However,the sintering point of Qinghai coal ash was seen to decrease from 1005℃to 855℃,834℃,and 819℃in the same blends due to the formation of lowmelting aluminosilicate.Results also showed that blending directly influences the sintering strength during the various stages of melting.The key finding from this study is that it is possible to mitigate against the severe ash slagging and fouling issue arising from high calcium and iron coals by cogasification with a high silicaalumina oil shale.Moreover,blending coals with oil shales can also modify the ash melting behaviour of fuels to create the optimal ash chemistry that meets the design specification of the gasifier,without adversely affecting thermal performance.

AI-based small arms firing skill evaluation system in the military domain

Evaluation system of small arms firing has an important effect in the context of military domain. A partially automated evaluation system has been conducted and performed at the ground level. Automation of such system with the inclusion of artificial intelligence is a much required process. This papers puts focus on designing and developing an AI-based small arms firing evaluation systems in the context of military environment. Initially image processing techniques are used to calculate the target firing score. Additionally, firing errors during the shooting have also been detected using a machine learning algorithm. However, consistency in firing requires an abundance of practice and updated analysis of the previous results. Accuracy and precision are the basic requirements of a good shooter. To test the shooting skill of combatants, firing practices are held by the military personnel at frequent intervals that include 'grouping' and 'shoot to hit' scores. Shortage of skilled personnel and lack of personal interest leads to an inefficient evaluation of the firing standard of a firer. This paper introduces a system that will automatically be able to fetch the target data and evaluate the standard based on the fuzzy systems.Moreover it will be able to predict the shooter performance based on linear regression techniques.Thereby, it compares with recognized patterns to analyze the individual expertise and suggest improvements based on previous values. The paper is developed on a Small Arms Firing Skill Evaluation System, which makes the whole process of firing and target evaluation faster with better accuracy. The experiment has been conducted on real-time scenarios considering the military field and shows a promising result to evaluate the system automatically.