مدل سازی شبکه عصبی برای سلول های سوخت
ترجمه نشده

مدل سازی شبکه عصبی برای سلول های سوخت

عنوان فارسی مقاله: چارچوب داده محور بهینه سازی مدل و مدل سازی شبکه عصبی برای سلول های سوخت میکروبی
عنوان انگلیسی مقاله: A Data-Driven Based Framework of Model Optimization and Neural Network Modeling for Microbial Fuel Cells
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: زیست شناسی، مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: میکروبیولوژی، هوش مصنوعی، شبکه های کامپیوتری
کلمات کلیدی فارسی: سلول های سوخت میکروبی، بهینه سازی مدل، انتخاب متغیر، شبکه های عصبی
کلمات کلیدی انگلیسی: Microbial fuel cells, model optimization, variable selection, neural networks
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2951943
دانشگاه: School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 14
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E13990
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Two-Chamber Microbial Fuel Cell Model


III. Methodology


IV. Results and Discussion


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Microbial fuel cells (MFCs) are devices that transform organic matters in wastewater into green energy. Microbial fuel cells systems have strong nonlinearity and high coupling, which involves control science, microbiology, electrochemistry and other disciplines. According to the requirements of microbial fuel cell system for model robustness and accuracy, we designed a comprehensive model optimization framework. Firstly, the influence of uncertain parameters on system was analyzed by combining global sensitivity analysis with uncertainty analysis. In accordance with analysis results, the uncertain parameters were optimized. Secondly, based on the optimized stochastic model, a simplified model was proposed by combining variable selection with neural networks. The results shown that the proposed framework can deeply analysis the influence of uncertain parameters on output, and provide theoretical basis for experimental research. It fully simplifies the original MFCs model, and has guiding significance for other types of fuel cells.


Introduction


Microbial Fuel Cells (MFCs) which required in lots of field [1]–[3] have received a widespread concern in the past several years as a green energy. MFCs can be considered as equipment realizes a conversion of bioenergy to green energy, taking the organism as the fuel and direct generation of electricity by microbial redox reactions. Many efforts have been directed to the power generation principle and application of MFCs [4]–[6]. The basic reaction principle is that the bacteria oxidize the substrate in the anaerobic anode through a catalyst, and the electrons generated by the anode chamber are transported to the aerobic cathode through an external circuit and form water molecules. Compared with hydrogen oxygen fuel cells and other chemical cells, MFCs use organisms as biocatalysts and possesses the advantages of high resourceusing rate, less pollution and mild reaction conditions. However, there are several obstacles constrain the development of MFCs. The main disadvantage of MFCs operation compared to other renewable energy, such as geothermal energy, tidal energy, nuclear power is the low power output, which limits the ability to drive high power devices. Over the past few years, the main direction of research was microbial cultivation, substrate analysis and electrode modification, various systems were built for different types of MFCs [7], [8]. In addition, a large number of experimental studies have also found the impact of operational parameters on MFCs performance, such as ionic concentration [9], temperature [10], [11], pH [12], substrate nitrogen concentration [13], [14], and electrode distance [15].

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

مدل سازی شبکه عصبی برای سلول های سوخت
نوشته های مرتبط
مقالات جدید
نماد اعتماد الکترونیکی
پیوندها