مشخصات مقاله | |
ترجمه عنوان مقاله | اتوماسیون بویلر در نیروگاه حرارتی با استفاده از سنسورها و IoT |
عنوان انگلیسی مقاله | Automation of boiler process at thermal power plant using sensors and IoT |
انتشار | مقاله سال 2018 |
تعداد صفحات مقاله انگلیسی | 11 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه تیلور و فرانسیس |
مقاله بیس | این مقاله بیس نمیباشد |
فرمت مقاله انگلیسی | |
رشته های مرتبط | مهندسی مکانیک، فناوری اطلاعات |
گرایش های مرتبط | مکاترونیک، اینترنت و شبکه های گسترده، مهندسی مکانیک نیروگاه |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | مجله آمار و سیستم های مدیریت – Journal of Statistics and Management Systems |
دانشگاه | School of Engineering and Technology – Jaipur National University – Rajasthan |
کلمات کلیدی | اتوماسیون، اینترنت اشیا، قدرت حرارتی، شبیه سازی، سنسورها، مدل سازی، الگو |
کلمات کلیدی انگلیسی | Automation, Internet of Things, Thermal power, Simulated, Sensors, Modeling, pattern. |
شناسه دیجیتال – doi |
https://doi.org/10.1080/09720510.2018.1475078 |
کد محصول | E9941 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract Introduction The Concept of Boiler IoT exploited for Automation Conversion of Manual Task to Automated System Conclusion References |
بخشی از متن مقاله: |
Abstract
Internet of Things is the new era for industrial automation to produced quality products, by implementing automation in accordance with the existing man power at low cost. Despite the use of Distributed Control System or Programmable Logic Controller (DCS/PLC) in Thermal Power Plants, controlling parameters like temperature, humidity and pressure is a critical and essential process, and require a well-organized and trained labor for the completion of tasks without any mishappening. This paper focuses on developing a smart simulation and automated system to makes use of modeling and pattern discovery along with the data mining techniques to collect data from the thermal power plant. Each boiler is attached with each other through a sensor which in turn is connected to an IOT driven application at remote location. The sensors work on a specific pattern depending on the possible situations that may arise while the plant is in process. The same pattern is recognized in a simulated environment with the help of modeling technique. The message passing service in the whole system makes the working of the automation project prone to accidents since modification can be made according to the environment conditions. This paper also tends to propose an idea for an IOT device that provides a virtual knob facility to adjust the parameters of the furnace from the remote area. Introduction In the past decade there has been an extensive increase in the area of Industrial automation. Over the past few years, machines have taken the command of the manual tasks and reduced the mental as well as the physical presence of the human at the location. Thermal Power Plant is one such process which requires regular inspection and proper management. Any change in the parameters controlling the power plant should be reported directly to the central controlling area for required amendment [1]. The boiling section of the thermal power plant produces high temperature steam due to which it often becomes resistant to operate from the site. Moreover, gone are those days when labors used to stand at a particular location for hours and manage the complete process. Though, there has been some work in the area for automating the boiler processes, each research study show that there had been some drawbacks at each step. 1.1 PLC / DCS System Distributed Control System (DCS) was used to centralize the digital binary information produced at each boiler level as an output of the logic statement stored in the memory of Programmable Logic Controller (PLC). Though PLC was a bit outdated automation technique but the slow scanning time of DCS allowed both of them to be used together [3]. Testing of problems at subsystem level was much more economical than at the centralized level, however, the cost effectiveness in the whole system could not be achieved. |