Fitria, Risa (2017) Estimasi Variabel Keadaan Pada Non-Isothermal Continuous Stirred Tank Reactor Menggunakan Fuzzy Kalman Filter. Masters thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
1211201202-Master_Theses.pdf - Published Version Download (2MB) | Preview |
Abstract
Continuous Stirred Tank Reactor (CSTR) merupakan salah satu alat yang penting dalam industri kimia. Pada umumnya reaksi pada CSTR berlangsung dalam waktu yang singkat dan hanya komponen – komponen stabil saja yang bisa teramati. Sehingga suatu estimasi dari variabel keadaan pada model sistem CSTR sangat dibutuhkan. Kalman Filter merupakan algoritma estimasi variabel sistem dinamik stokastik yang menggabungkan model matematika dan data pengukuran. Modifikasi Kalman Filter untuk sistem nonlinear dengan menggabungkan teori Fuzzy disebut Fuzzy Kalman Filter (FKF), untuk beberapa kasus memiliki kinerja yang baik. Pada penelitian ini, digunakan metode FKF untuk mengestimasi variabel keadaan pada Non-Isothermal CSTR. Kemudian hasil estimasi yang diperoleh akan dibandingkan tingkat akurasinya dengan metode pengembangan Kalman Filter yang lain yaitu EKF dan EnKF.
Hasil estimasi menunjukkan bahwa metode EnKF lebih akurat daripada metode FKF dan EnKF untuk estimasi konsentrasi reaktan dan temperatur tangki. Sedangkan untuk estimasi temperatur cooling jacket, metode FKF lebih akurat. Berdasarkan waktu komputasi metode EKF 8,4% lebih cepat dari waktu komputasi metode FKF dan 96,2% lebih cepat dari metode EnKF.
==============================================================================================
Continuous Stirred Tank Reactor (CSTR) is one of the most important tools in chemical manufacturing. In general, the reaction in the CSTR take place in short time and only the stable components that could be observed. So that the estimation of the state variable in CSRT model is needed. Kalman filter is an algorithm to estimate the state variable of the stochastic dynamical linear system. This algorithm combines the mathematical model with the measurement data. The famous modification of Kalman Filter for nonlinear system is Extended Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF). However, previous research has demonstrated that the Kalman Filter algorithm combines with Fuzzy theory, namely Fuzzy Kalman Filter (FKF), in some cases, have good performance. In this research state variable of Non-Isothermal CSTR will be estimated using FKF. Furthermore, The accuracy of estimation result using FKF will be compared with the estimation result using EKF and FKF.
The estimation results show that the EnKF method is more accurate than FKF and EKFmethods for estimating reactans concentration and tank temperature. Estimating cooling jacket temperature using FKF method is more accurate than EKF and EnKF methods. However, based on the computational time, EKF method 8,4% faster than the computational time of FKF method and 96,2% faster than the computational time of EnKF method.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Extended Kalman Filter (EKF), Ensemble Kalman Filter (EnKF), Fuzzy Kalman Filter (FKF), Non-Isothermal Continuous Stirred Tank Reactor (Non-Isothermal CSTR). |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA248_Fuzzy Sets |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44101-(S2) Master Thesis |
| Depositing User: | - RISA FITRIA |
| Date Deposited: | 20 Apr 2017 03:52 |
| Last Modified: | 08 Mar 2019 06:58 |
| URI: | http://repository.its.ac.id/id/eprint/3665 |
Actions (login required)
 |
View Item |