Theory and Applications of Intelligent Coal Blending in Coal Petrology | Book Publisher International

 

China is the world's largest coal consumer, mainly concentrated in the four major industries of electric power, building materials, metallurgy, and chemical industry.   The three major directions of the coal chemical industry are coking, gasification and liquefaction. Coal coking refers to the process in which coal is isolated from air in a coke oven and heated to about 1000°C, so that coke, chemical products and coal gas can be obtained. The quality control of coke involves the quality control of coal blending and the actual operation of coke ovens. The technologies involved in coal blending include coke quality prediction, coal yard management and scientific coal blending. Coal petrology blending is the use of coal petrology parameters for coking coal blending, which is a major achievement of coal petrology application in the coking industry.  The Ammosov-Schapiro method is a mathematical function that represents the vitrinite reflectance distribution, reflecting the mass details of all vitrinite and all microscopic components, vitrinite, inertinite, chitin, and minerals. These two quality details, vitrinite quality and microscopic component quality determine the strength and composition balance of the coke made from the coal blending and thus determine the coke performance. Based on the concept of fuzzy sets, data processing and calculation methods, the theory of intelligent coal blending in coal petrology creatively defines two sets, the pseudo-strength index set and pseudo-composition balance index set, and their elements to solve the problem of accurately predicting coke quality, effectively managing coking coal yards, and scientifically calculating coal blending ratios.

 

Author (s) Details

Li Dong
Xi'an Siyuan University, Xian, Shaanxi, 710038, China.

 

ZHANG Xuemei
Xi'an Siyuan University, Xian, Shaanxi, 710038, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48119-60-5

On Algebraic Properties of k-Q-Anti Fuzzy Normed Rings | Book Publisher International

 In this paper, the concept of k – Q -Anti fuzzy normed ring is introduced and some basic properties related to it are established. That our definition of normed rings on k - Q - Anti fuzzy sets leads to a algebraic structure which we call a k – Q - Anti Fuzzy Normed Rings. We also defined k - Q - Anti Fuzzy Normed Rings homomorphism,  Anti Fuzzy Normed Subring,  Fuzzy Normed Ideal, k – Q - Fuzzy Normed Prime Ideal and k - Q - Anti Fuzzy Normed Maximal Ideal of a Normed ring respectively. We show that the some algebraic properties of normed ring theory on a k - Q - fuzzy sets, prove theorem and given relevant examples.

Author(s) Details:

Premkumar Munusamy,
Department of Mathematics, Sathyabama Institute of Science and Technology (Deemed to be University), Chennai-600119, Tamil Nadu, India.

J. Juliet Jeyapackiam,
Department of Mathematics, Jayaraj Annapackiam CSI College of Engineering Nazareth, Tuticorin-628617, India.

Abdul Salam,
Gulf Asian English School, Sharjah, United Arab Emirates.

H. Girija Bai,
Department of Mathematics, Sathyabama Institute of Science and Technology (Deemed to be University), Chennai-600119, Tamil Nadu, India.

Y. Immanuel,
Department of Mathematics, Sathyabama Institute of Science and Technology (Deemed to be University), Chennai-600119, Tamil Nadu, India.

 
A. Prasanna,
PG and Research Department of Mathematics, Jamal Mohamed College (Autonomous), (Affiliated to Bharathidasan University), Tiruchirappalli-620020, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/OAPFNR/article/view/12570

Effect of Fuzzy Time Series Technique on Estimators of Spectral Analysis | Chapter 04 | Recent Advances in Mathematical Research and Computer Science Vol. 6

 The estimate of the spectral density function, as well as the properties of the resulting estimator, the periodogram, are discussed in this chapter. For actual and anticipated observations of the fuzzy time series, the statistical features of this periodogram are explored. MSEs, which are based on these statistical features, can be used to compare the periodogram results in both scenarios. For this, a programme was developed that converts observed time series into fuzzy time series with huge sizes.

Study Goal: To enhance the estimate of the spectral density function while maintaining its statistical features using the fuzzy time series technique.

Author(S) Details

Abd El-Moneim A. M. Teamah
Mathematics Department, Faculty of Science, Tanta University, Tanta, Egypt.

Hasnaa M. Faied
Mathematics Department, Faculty of Science, A1-Azhar University (Girls), Nasr-City, Cairo, Egypt.

Mohammed H. El-Menshawy
Mathematics Department, Faculty of Science, A1-Azhar University (Men), Nasr-City, Cairo, Egypt.

View Book:- https://stm.bookpi.org/RAMRCS-V6/article/view/5307

Determining the Rules for Computing Fixpoints and Introduction of Min-generated Fuzzy Concepts from a Fuzzy Context | Chapter 6 | Current Topics on Mathematics and Computer Science Vol. 10

 One of the core problems in fuzzy concept lattice theory is creating fuzzy ideas from data with fuzzy properties. The problem of creating fuzzy concepts turns out to be the problem of computing all fixpoints of a given fuzzy operator that is associated with input data, because fuzzy concepts are the fixpoints of a particular fuzzy operator that is connected with input data. We've created ten rules for generating fixpoints for two fuzzy closure operators, and, in this post. After that, we give a new approach and methodology for computing fixpoints (fuzzy concepts), which are defined as min-generated fuzzy concepts, by combining all of the proposed criteria.

Author(s) Details

Partha Ghosh
Department of Mathematics, Goenka College of Commerce and Business Administration, 210, B. B. Ganguly Street, Kolkata-700012, India.

View Book :- https://stm.bookpi.org/CTMCS-V10/article/view/3749

Generalized Measures of Fuzzy Entropy with Order and Degree for Intuitionistic Fuzzy Set| Chapter 9 | Current Topics on Mathematics and Computer Science Vol. 4

 In an axiomatic technique of entropy, the present communication introduces and specifies a parametric exponential fuzzy information measure. Anshu Ohlan's definition of entropy on fuzzy sets is generalised to intuitionistic fuzzy sets. We examined various aspects of our proposed measure to ensure that it is a legitimate entropy.

Author (S) Details

Bhagwan Dass
Department of Mathematics, Deenbandhu Chhotu Ram University of Science and Technology, Murthal-131039, India.

Dr. Vijay Prakash Tomar
Department of Mathematics, Deenbandhu Chhotu Ram University of Science and Technology, Murthal-131039, India.

Dr. Krishan Kumar
Department of Mathematics, Deenbandhu Chhotu Ram University of Science and Technology, Murthal-131039, India

View Book :-  https://stm.bookpi.org/CTMCS-V4/article/view/2284