Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

نویسندگان

چکیده

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques describe relationship between dependent independent variables. This model can be effectively used develop PEMS, estimate amount emitted by sources, where fuel composition other process-related parameters are available. It often makes them sufficient predict emission discharge with acceptable accuracy. In cases PEMS accepted as an method CEMS, which use gas analyzers, they provide cost savings substantial benefits ongoing system support maintenance. The described mathematical concept based on matrix algebra representation in multiple involving precision arithmetic techniques. Challenging numerical examples big data analysis, investigated. Numerical illustrate computational accuracy efficiency analysis due increasing level. programming language C++ implementation. research development, including NOx emissions data, were obtained CEMS software installed petrochemical plant.

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ژورنال

عنوان ژورنال: Applied mathematics

سال: 2023

ISSN: ['2152-7393', '2152-7385']

DOI: https://doi.org/10.4236/am.2023.145023