پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 370 |
| تعداد مقالات | 3,873 |
| تعداد مشاهده مقاله | 5,218,927 |
| تعداد دریافت فایل اصل مقاله | 3,499,742 |
Reconciliation of data with non-random errors | ||
| Journal of Geomine | ||
| دوره 1، شماره 4، اسفند 2023، صفحه 171-178 اصل مقاله (413.67 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22077/jgm.2024.7416.1026 | ||
| نویسندگان | ||
| Zahra Sadat Abdollahi Douraki1؛ Ali Akbar Abdollahzadeh2؛ Mohammad Reza Khalesi* 3 | ||
| 1Department of Mining Eng., University of Kashan | ||
| 2Department of Mining Eng., Amir Kabir University of Technology | ||
| 3Faculty of Engineering, Tarbiat Modares University | ||
| چکیده | ||
| Errors of measured data could impact the offline optimizations or online control systems, leading to potentially uneconomical or unsafe process conditions. To address this issue, data reconciliation methods are introduced to enhance the data as much as possible. In this regard, existence of non-random errors is challenging. This article debates the use of conventional sum of squares objective function in the case of presence of non-random errors and shows how a robust estimator such as the maximum likelihood ameliorate the reconciliation. The robustness of the new objective function was assessed using simulated data. Results indicate that the sum of errors between real simulated data of flowrates and their estimation counterparts decreases from 124% to 27% in the case of a gross error in one stream, when robust objective function is manipulated. Even if no non-random error exists, it is shown that robust estimator could result in better reconciliation of data, if optimum parameters are chosen for the robust objective function. | ||
| کلیدواژهها | ||
| Data Reconciliation؛ Gross Error؛ M-estimators؛ Robust؛ Process Measurement | ||
|
آمار تعداد مشاهده مقاله: 359 تعداد دریافت فایل اصل مقاله: 253 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب