Manufacturing of Cement from Egg Shell
Effective deployment of bio-waste has been given importance in our society for environmental and economic concerns. Reclamation of eggshell from hatcheries, home, bakeries and industries is an efficient and cost productive way to reduce waste disposal and prevent serious environmental pollution. Egg shells waste constitutes essential organic and inorganic materials that can be composted with other materials for enhancing the pre-existing property. The major concern in any civil sector is efficient construction with minimal cost investment. Cement is one of the pivotal components for construction. It is the backbone to the infrastructure development. Rapid infrastructure developments ensued in high demand for raw materials worldwide that resulted in huge imbalance between demand and supply. However, cement plants are the source of few harmful compounds like nitrogen oxide (NOx), Sulphur dioxide (SO2) and Carbon monoxide (CO) which can cause serious health defects and also affects our environment as well. The cement manufacturing sector is the third largest reason for total pollution in our environment. In spite of all these there is a huge demand for the cements for the development of a country. This increase in demand, led to search for alternative raw materials from enormous waste product which is both efficient and cost productive began. In this work, calcinations of chicken eggshells with different ingredients were carried out and the chemical composition of the resultant product was analyzed.
Published by: Samarth Bhardwaj
Author: Samarth Bhardwaj
Paper ID: V2I3-1146
Paper Status: published
Published: May 16, 2016
Mechanical Characterization of Thermal Spray Coating on Stainless Steel 316 L
Thermal spray coating process is a surface modification technique in which a coating material like cermets, metallic, ceramic and some other materials in form powder are feed into a torch or a gun, the powder inserted into torch will be melted by high temperature developed by torch. Coating thickness can achieve by applying multiple layer of melted coated material. This paper aims at the study of mechanical characterization of thermal spray single layer and multi-layer coatings. Coatings on SS 316L is followed by the wear test .It has been found that the wear rate of base metal i.e. SS 316L is more than single layer and multi layer coatings. The multilayer has shown the maximum resistance to the wear rate.
Published by: Prajapati Amit Kumar, Vaibhav khurana
Author: Prajapati Amit Kumar
Paper ID: V2I3-1148
Paper Status: published
Published: May 16, 2016
A Review Study of Thermal Spray Coatings for Corrosive Wear
Thermal spray coating process is a surface modification technique in which a coating material likes cermets, metallic, ceramic and some other materials in form powder are feed into a torch or a gun, the powder inserted into torch will be melted by high temperature developed by torch. Coating thickness can achieve by applying multiple layer of melted coated material. This papers aims at the review of various coating techniques used for the corrosive wear applications. Thermal sprayed thick (from 50 to 3000 μm) coatings, including cold spray coatings are more and more used in industry for the following reasons: (i) They provide specific properties onto substrates which properties are very different from those of the sprayed coating; (ii) They can be applied with rather low or no heat input to substrates (allowing for example spraying ceramics onto polymer substrates); (iii) Virtually any material that melts without decomposition or vaporizing can be sprayed including cermets or very complex metal or ceramic mixtures, allowing tailoring coatings to the wished service property; (iv) Sprayed coatings can be strip off and the worn or damaged coatings re-coated without changing part properties and dimensions; (v) Some spray processes can be moved on site, allowing spraying rapidly big parts, which displacement would otherwise be rather long and expensive.
Published by: Prajapati Amit Kumar, Vaibhav khurana
Author: Prajapati Amit Kumar
Paper ID: V2I3-1147
Paper Status: published
Published: May 16, 2016
Analysis of Temperature Variation in a Mild Steel Plate using LBM
LBM is a method used for the computations in dynamics of fluid by fluid simulation. In the present study, a square plate (Mild steel plate) of 10 mm X 10 mm dimensions has been selected. The temperature variation and heat flow at different nodes through the plate from its one end to another end would be simulated by giving heat from room temperature to 100° C in MATLAB software, C++ etc. After the simulation process, the results of temperature variation at different nodes will be shown on Tech-Plots in the form graphs b/w temperature and distance from one end of the plate to the another end of the plate.
Published by: Neeraj Kumar, Abhishek Dadwal, Ajay Kumar, Gagan Bhatt, Amit
Author: Neeraj Kumar
Paper ID: V2I3-1145
Paper Status: published
Published: May 7, 2016
Heart Disease Prediction System Using PCA and SVM Classification
Heart is the most significant part of human body. In this fast and busy life people eat what they want and diagnosis themselves. As a result they get sick and it results into heart failure. Life is completely dependent on the proper working of heart. If functioning of heart is not properly worked, it will also affect the other body parts of human body such as brain, kidney, etc. Heart Diseases are the major cause of deaths in the world. Various factors that increase the risk of Heart Diseases such as stress, cholesterol, high blood pressure, lack of physical exercise, smoking and obesity etc. The heart disease prediction system helps the physician and healthcare professionals as a tool for heart disease diagnosis. To protect the life of a patient from heart diseases there have to be quick and efficient prediction technique is to be followed. The main goal of this work is to develop an efficient heart disease prediction system using feature extraction and SVM classifier that can be used to predict the occurrence of disease. The prediction of heart disease pattern with classification algorithms is proposed here. Classification is one of the most important tasks in data mining. It is very essential to find the best fit classification algorithm that has greater accuracy on classification in the case of heart disease classification. This cleaned data is classified by the classification algorithms SVM classifier. This technique is widely used to validate the accuracy of medical data.
Published by: Kiranjeet Kaur, Lalit Mann Singh
Author: Kiranjeet Kaur
Paper ID: V2I3-1141
Paper Status: published
Published: May 2, 2016
Prediction of Heart Disease using Data Mining Techniques
Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining applications are able to give the answer of business questions which can take much time to resolve traditionally. High amount of data that can be generated for the prediction of disease is analyzed traditionally and is too complicated along with voluminous to be processed. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. The healthcare sector assembles enormous quantity of healthcare data which cannot be mined to uncover hidden information for effectual decision making. However, there is a plenty of hidden information in this data which is untapped and not being used appropriately for predictions. It becomes more influential in case of heart disease that is considered as the predominant reason behind death all over the world. In medical field, Data Mining provides several methods which are widely used in the medical and clinical decision support systems which should be helpful for diagnosis and predicting of various diseases. These data mining techniques can be used in heart diseases takes less time and make the process much faster for the prediction system to predict diseases with good accuracy to improve their health. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict probable complications well in advance. Identify the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as diabetes, high blood cholesterol, obesity, hyper tension, smoking, poor diet, stress, etc. Data mining techniques and functions are used to identify the level of risk factors which helps the patients to take precautions in advance to save their life.
Published by: Era Singh Kajal, Ms. Nishika
Author: Era Singh Kajal
Paper ID: V2I3-1139
Paper Status: published
Published: May 2, 2016
