Air Quality Prediction in Multi Industries Using Computer Simulation

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The issues arising from coal washery operations, particularly concerning the air environment, demand a systematic examination through a suitable computer algorithm. This algorithm should be developed based on mathematical models to quantify the impact on the air environment system. Consequently, this study aims to design and implement a computer algorithm, utilizing an appropriate contaminant transport model, to predict environmental parameters in and around coal washery complexes. The study identifies significant sources within coal washery operations and examines the pollution scenario of various industrial complexes, as well as the environmental status of nearby rivers, streams, and townships. The developed computer algorithm for predicting the dispersion of air pollutants serves as a versatile tool to address environmental issues related to air pollution. It can be considered a universal model that, with the identification of air pollution sources and estimation of their emission rates using suitable statistical models, can be applied to predict air pollution for various industries.

Autorentext
Dr. R. Judith Kiruba is a distinguished academician with over 23 years of teaching experience serving at T.B.M.L. College, Porayar. Tamilnadu, India. Her research interests include Algorithms and Computer Simulation. She has published many papers in reputed journals, conferences. She has acted as Resource Person in conferences and Seminars.

Weitere Informationen

  • Allgemeine Informationen
    • Sprache Englisch
    • Anzahl Seiten 204
    • Herausgeber LAP LAMBERT Academic Publishing
    • Gewicht 322g
    • Untertitel System Analysis and Algorithm Development for Air Quality Prediction in and Around Multi Industrial Source Complexes
    • Autor R. Judith Kiruba , M. Sundara Rajan , R. Irene Hepzibah
    • Titel Air Quality Prediction in Multi Industries Using Computer Simulation
    • Veröffentlichung 07.12.2023
    • ISBN 6206786269
    • Format Kartonierter Einband
    • EAN 9786206786269
    • Jahr 2023
    • Größe H220mm x B150mm x T13mm
    • GTIN 09786206786269

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