scholarly journals PROCESS TOMOGRAPHY FOR MODEL FREE ADAPTIVE CONTROL (MFAC) VIA FLOW REGIME IDENTIFICATION IN MULTIPHASE FLOWS

2020 ◽  
Vol 53 (2) ◽  
pp. 11753-11760
Author(s):  
Ru Yan ◽  
Håkon Viumdal ◽  
Saba Mylvaganam
Author(s):  
Mohd. Fua’ad Rahmat ◽  
Hakilo Ahmed Sabit

Proses tomografi adalah suatu teknik membina imej yang murah, cekap dan sesuai untuk proses di industri yang kini semakin diguna pakai untuk tujuan pemantauan proses dan pengukuran. Mekanisme pengesanan dalam proses tomografi bergantung kepada bahan aliran dalam paip industri sama ada pepejal, gas atau cecair. Dalam kertas kerja ini, proses yang terlibat adalah pengaliran pepejal kering dalam paip mengikut arah graviti dan mekanisme pengesanan yang digunakan ialah penderia elektrodinamik. Pengenalpastian rejim aliran daripada pengukuran penderia adalah dengan menggunakan rangkaian neural yang akan mengenal pasti aliran pepejal sama ada dalam aliran penuh, suku separuh dan tiga suku. Kata kunci: Proses tomografi, rangkaian neural, penderia elektrodinamik, pengenalpastian Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and any of their mixtures. In this paper, the process to be imaged and measured involves solid particles flow in gravity drop system. Electrical charge tomography or electrodynamic tomography is a tomographic technique using electrodynamic sensors. This paper presents the flow regime identification using neural network. Keywork: Process tomography; neural network; electrodynamic sensor; identification


Author(s):  
Yao Wenlong ◽  
Qi Guanhua ◽  
Yang ke ◽  
Chi Ronghu ◽  
Yang Dejing

Author(s):  
Na Dong ◽  
Wenjin Lv ◽  
Shuo Zhu ◽  
Donghui Li

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.


Author(s):  
Xi Wu ◽  
Mengting Wang ◽  
Mohammad Shahidehpour ◽  
Shuang Feng ◽  
Xi Chen

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