Control System Simulators for Gas-Liquid Cylindrical Cyclone Separators

2000 ◽  
Vol 122 (4) ◽  
pp. 177-184 ◽  
Author(s):  
Shoubo Wang ◽  
Ram S. Mohan ◽  
Ovadia Shoham ◽  
Jack D. Marrelli ◽  
Gene E. Kouba
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Pressure Fluctuations ◽  
Pressure Control ◽  
Liquid Level ◽  
Control Loop ◽  
Level Control ◽  
Two Phase ◽  
Bulk Separation ◽  
Cylindrical Cyclone

The control system performance of gas liquid cylindrical cyclone (GLCC©) separators can be considerably improved by adopting suitable control strategy and optimizing the design of the controller PID settings. Dynamic simulators have been developed in this study, based on Matlab/Simulink® software for evaluation of several different GLCC control philosophies for two-phase flow metering loop and bulk separation applications. Detailed analysis of the GLCC control system simulators indicates that for integrated liquid level and pressure control strategy, the level control loop compliments the operation of the pressure control loop, and vice versa. This strategy is ideal for reducing the pressure fluctuations in the GLCC. At severe slugging conditions, the integrated liquid level control is more desirable because of its faster response. However, there is no control of the GLCC pressure fluctuations. The results also show that the simulators are capable of representing the dynamic behavior of real physical systems. [S0195-0738(00)00504-5]

Application of GA-FNC in Crystallizer Liquid Level Control System

2011 ◽  
Vol 199-200 ◽  
pp. 1777-1780
Author(s):  
Ming Li ◽  
Da Yong Yang
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Steel Casting ◽  
Fuzzy Controller ◽  
Liquid Level ◽  
Level Control ◽  
Intelligent Control System ◽  
Casting System ◽  
The Stability ◽  
Self Learning

This article discusses the control strategy of the steel casting system which possesses the characteristics of large inertia, time-varying and nonlinear. Aiming at getting the minimum deviation of liquid level, the control strategy uses the genetic algorithm to off-line optimize the parameters (cij,bj) of the Gaussian membership function and the network structure of fuzzy controller which affect the overall system firstly. Then, BP algorithm is used to online regulate and optimize the weight parameters of the control output which affect the system partly. Finally, the intelligent control system of the liquid level which is based on GA-FNC is simulated. The results show that the method can enhance the ability of self-learning and robustness of the system greatly and improve the stability of steel casting system significantly.

scholarly journals RESEARCH OF LIQUID LEVEL CONTROL SYSTEM / SKYSČIO LYGIO VALDYMO SISTEMOS TYRIMAS

2015 ◽  
Vol 7 (3) ◽  
pp. 317-322
Author(s):  
Dominykas Beištaras
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Controller ◽  
Liquid Level ◽  
Water Tank ◽  
Level Control ◽  
Level Control System ◽  
Filling Time ◽  
Control System Model ◽  
Liquid Level Control

This paper presents liquid level control system model and analysis of dynamic characteristics. The system consists of scalar controlled induction motor drive, fuzzy logic controller, water tank and centrifugal pump. Simulink models of water tank, pump and controller are presented. The simulation of the system shows that the use of fuzzy logic controller reduces valve opening time and reservoir filling time. Nagrinėjamas skysčio lygio valdymo sistemos imitacinių modelių sudarymas, analizuojamos dinaminės charakteristikos. Valdymo sistema sudaryta iš skaliariniu būdu valdomos dažninės elektros pavaros su neraiškiosios logikos reguliatoriumi, vandens rezervuaro ir išcentrinio siurblio. Sudaryti rezervuaro, siurblio ir reguliatoriaus Simulink modeliai. Atlikus imitacijas gauta nedimensinė siurblio charakteristika, apibūdinanti siurblio veikimą, esant bet kokiam sukimosi greičiui. Nustatyta, kad sistemoje su neraiškiosios logikos reguliatoriumi vožtuvas yra atidaromas greičiau nei sistemoje su proporcinguoju integraliniu (PI) reguliatoriumi, ir todėl sumažinama rezervuaro pripildymo trukmė.

Intelligent Operation Control for the Fused Magnesia Production

2011 ◽  
Vol 391-392 ◽  
pp. 1450-1454
Author(s):  
Yong Jian Wu ◽  
Ping Zhou ◽  
Pin Shang ◽  
Tian You Chai
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Control Strategy ◽  
Control Method ◽  
Level Control ◽  
Loop Control ◽  
Intelligent Control System ◽  
Technical Requirements ◽  
Operation Control ◽  
Lower Loop

An electro-fused magnesia furnace (EFMF) is used to produce electro-fused magnesia. Due to the complex dynamic characteristics of the EFMF production process, it is difficult to achieve the satisfactory control performances only by the independent conventional control method. As a result, the lower loop control with manual operations is still widely used in practice. However, the manual operation cannot ensure that the actual production qualities and the energy consumption of unit production meet the technical requirements all the time. In this paper, an intelligent operation control strategy is developed for the EFMF to automatically adjust the setpoints of the lower level control system. Based on the proposed intelligent control strategy, an intelligent control system for the EFMF is built and implemented on site. Industrial application has demonstrated that the intelligent control system can achieve reliable, accurate and timely control performances.

RETRAN Application of Turbine Trip and Load Rejection of Startup Test Analysis for Lungmen ABWR

2009 ◽  
Author(s):  
Chen-Lin Li ◽  
Chiung-Wen Tsai ◽  
Chunkuan Shih ◽  
Jong-Rong Wang ◽  
Su-Chin Chung
Keyword(s):  
Control System ◽  
Water Level ◽  
Control Systems ◽  
Nuclear Power ◽  
Pressure Control ◽  
Reactor Power ◽  
Level Control ◽  
Bypass Flow ◽  
Control Rod ◽  
Recirculation Flow

This study used RETRAN program to analyze the turbine trip and load rejection transients of Taiwan Power Company Lungmen Nuclear Power Plant’s startup test at 100% power and 100% core flow operating condition. This model includes thermal flow control volumes and junctions, control systems, thermal hydraulic models, safety systems, and 1D kinetics model. In Lungmen RETRAN model, four steam lines are simulated as one line. There are four simulated control systems: pressure control system, water level control system, feedwater control system, and speed control system for reactor internal pumps. The turbine trip event, at above 40% power, triggers the fast open of the bypass valves. Upon the turbine trip, the turbine stop valves close. To minimize steam bypassed to the main condenser, recirculation flow is automatically runback and a SCRRI (selected control rod run in) is initiated to reduce the reactor power. The load rejection event causes the fast opening of the bypass valves. Steam bypass will sufficiently control the pressure, because of their 110% bypass capacity. A SCRRI and RIP runback are also initiated to reduce the reactor power. This study also investigated the sensitivity analysis of turbine bypass flow, runback rate of RIPS and SCRRI to observe how they affect fuel surface heat flux, neutron flux and water level, etc. The results show that turbine bypass flow has larger impacts on dome pressure than RIPS runback rate and SCRRI. This study also indicates that test criteria in turbine trip and load rejection transients are met and Lungmen RETRAN model is performing well and applicable for Lungmen startup test predictions and analyses.

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