مقاله انگلیسی رایگان در مورد بهینه سازی فرایند تصفیه فاضلاب با روش هوادهی – الزویر ۲۰۱۷

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله بهینه سازی فرایند تصفیه فاضلاب با روش هوادهی: یک رویکرد داده کاوی
عنوان انگلیسی مقاله Wastewater treatment aeration process optimization: A data mining approach
انتشار مقاله سال ۲۰۱۷
تعداد صفحات مقاله انگلیسی  ۱۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۰۰۵ در سال ۲۰۱۷
شاخص H_index ۱۳۱ در سال ۲۰۱۸
شاخص SJR ۱٫۱۶۱ در سال ۲۰۱۸
رشته های مرتبط  محیط زیست – مهندسی کامپیوتر
گرایش های مرتبط  اب و فاضلاب – بازیافت و مدیریت پسماند – الگوریتم و محاسبات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس Journal of Environmental Management
دانشگاه Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, United States
کلمات کلیدی مدلسازی مبتنی بر داده، بهینه سازی انرژی، فاضلاب، فرآیند هوادهی، داده کاوی
کلمات کلیدی انگلیسی Data-driven modeling, Energy optimization, Effluents, Aeration process, Data-mining
شناسه دیجیتال – doi
https://doi.org/10.1016/j.jenvman.2016.07.047
کد محصول E11715
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Outline
Highlights
Abstract
Keywords
۱٫ Introduction
۲٫ Data description
۳٫ Solution methodology
۴٫ Optimization results
۵٫ Conclusions
Acknowledgment
References

بخشی از متن مقاله:

Abstract

Being water quality oriented, large-scale industries such as wastewater treatment plants tend to overlook potential savings in energy consumption. Wastewater treatment process includes energy intensive equipment such as pumps and blowers to move and treat wastewater. Presently, a data-driven approach has been applied for aeration process modeling and optimization of one large scale wastewater in Midwest. More specifically, aeration process optimization is carried out with an aim to minimize energy usage without sacrificing water quality. Models developed by data mining algorithms are useful in developing a clear and concise relationship among input and output variables. Results indicate that a great deal of saving in energy can be made while keeping the water quality within limit. Limitation of the work is also discussed.

Introduction

In order to clean wastewater from certain contaminants, wastewater treatment includes different methods and processes that energy intensive. Across USA, wastewater treatment facilities collect, treat, and release about 4 billion gallons of treated effluent per day from about 26 million homes, businesses, and recreational facilities nationwide (Electric Power Research Institute and Inc. (EPRI, 2002). Such moving and treating processes accounts for more than 4% of the US electricity consumption. Minimizing the energy use of WWTPs by just 10% could lead to an annual savings of $400 million or more (http://water.epa.gov/infr). Due to the environmental regulations, wastewater industries are primarily concerned with water quality. The energy consumption in WWTPs is mainly attributed to their heavy mechanical systems, such as the pump and air support systems which are responsible for moving and treating wastewater (Singh et al., 2012; Zhang et al., 2016). The air support system consists of a group of air blowers that provides oxygen to the aeration tanks for removing organic compounds and converting ammonia. Pump system and the air support system are typically 0.5-MW class mechanical equipment and accounts for more than 70% of the electricity consumption of WWTPs. Traditionally, WWTP operations and designs are based on kinetic models or simulated data (Flores-Alsina et al., 2008; Sin et al., 2009). While such models have provided promising results, it requires some expert knowledge about different systems and subsystems within the process. Moreover, modeling of such systems heavily depends on the design of WWTPs and hence cannot be efficiently generalized.

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