Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mythily Mani ◽  
Manamalli Deivasigamani ◽  
Rames Chandra Panda ◽  
Raja Nandhini Ramasami
Keyword(s):  
Predictive Control ◽  
Catalytic Cracking ◽  
Pi Controller ◽  
Generalized Predictive Control ◽  
Yield Model ◽  
Yield Optimization ◽  
Gasoline Demand ◽  
Fluidized Catalytic Cracking ◽  
Pi Controllers ◽  
Predictive Controller

Abstract As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.

Fuzzy Adaptive Direct Generalized Predictive Control Algorithm in the Application of AWC Control

2014 ◽  
Vol 945-949 ◽  
pp. 2529-2532
Author(s):  
Fang Chen Yin ◽  
Geng Sheng Ma ◽  
Ya Feng Ji ◽  
Jia Xue Yu ◽  
De Hao Gu ◽  
...  
Keyword(s):  
Predictive Control ◽  
Generalized Predictive Control ◽  
Continuous Mill ◽  
Control Performance ◽  
Hot Strip ◽  
Predictive Controller ◽  
Feedback Correction ◽  
Rolling Optimization ◽  
Pure Delay ◽  
Fuzzy Adaptive

Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on generalized predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that generalized predictive controller achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the generalized predictive controller is not influenced by model parameter variations .

scholarly journals ANÁLISE EM MALHA FECHADA DO CONTROLADOR GENERALIZED PREDICTIVE CONTROL (GPC)

2009 ◽  
Vol 3 ◽  
pp. 119 ◽  
Author(s):  
Anderson Luiz Cavalcanti
Keyword(s):  
Stability Analysis ◽  
Predictive Control ◽  
Robust Stability ◽  
Closed Loop ◽  
Generalized Predictive Control ◽  
Loop Analysis ◽  
Closed Loop Systems ◽  
Robust Stability Analysis ◽  
Predictive Controller ◽  
Simulation Results

RESUMO O presente trabalho tem o objetivo de apresentar uma análise em malha fechada do controlador Generalized Predictive Control (GPC). Esta análise visa observar, com detalhes, as características deste tipo de controlador. Os detalhes apresentados são de extrema importância na análise de estabilidade robusta. Alguns resultados de simulação são apresentados. PALAVRAS-CHAVE: Controle preditivo, sistemas em malha fechada. CLOSED-LOOP ANALYSIS OF GENERALIZED PREDICTIVE CONTROL (GPC) ABSTRACT This paper presents a closed loop analysys of Generalized Predictive Control GPC. This analysis observes, in details, the features of this kind of predictive controller The details showed are very important in robust stability analysis. Simulation results are shown. KEY-WORDS: Predictive control, closed-loop systems.

Research on Chlorine Dosing Control Based on Adaptive Generalized Predictive Control

2013 ◽  
Vol 415 ◽  
pp. 89-94 ◽  
Author(s):  
Pei Zhang Xie ◽  
Xing Peng Zhou
Keyword(s):  
Time Delay ◽  
Predictive Control ◽  
Mimo System ◽  
System Simulation ◽  
Smith Predictor ◽  
Generalized Predictive Control ◽  
Time Varying ◽  
Varying Time Delay ◽  
Predictive Controller ◽  
System With Time Delay

Chlorine dosing is a complicated system with time delay, time-varying, non-linear and coupling. In this paper, multivariable adaptive generalized predictive controller based on Smith predictor is proposed. Instead of the optimal predictor, the Smith predictor with adaptive identifying parameters can increase the robustness of the MIMO system. Simulation and application in water-works at Suzhou (China) shows that the algorithm can overcome time-varying, time delay and disturbance.

A New Adaptive Generalized Predictive Control Algorithm for Nonlinear Processes

2010 ◽  
Author(s):  
Ma’moun Abu-Ayyad ◽  
Rickey Dubay ◽  
Bambang Pramujati
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Strategies ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Steel Cylinder ◽  
Unique Method ◽  
Predictive Controller ◽  
Process Prediction ◽  
Sampling Instant

This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems. This method is termed adaptive generalized predictive control which uses a multi-dimensional surface of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The adaptive generalized predictive controller was tested in simulation and its control performance compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results both demonstrate that the adaptive generalized predictive controller demonstrated improved closed-loop performance. The formulation of the nonlinear surface provides the mechanism for the adaptive approach to be readily applied to other advanced control strategies making the methodology generic.

The application of model predictive control of ammonia nitrogen in an activated sludge process

2011 ◽  
Vol 64 (5) ◽  
pp. 1115-1121 ◽  
Author(s):  
D. Vrečko ◽  
N. Hvala ◽  
M. Stražar
Keyword(s):  
Activated Sludge ◽  
Prediction Interval ◽  
Ammonia Nitrogen ◽  
Pi Controller ◽  
Ammonia Removal ◽  
Activated Sludge Process ◽  
Pi Controllers ◽  
Predictive Controller ◽  
Control Criteria ◽  
Improved Accuracy

In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.

Fluidized Catalytic Cracking Catalyst Microstructure as Determined by A Scanning Ion Microprobe

1990 ◽  
Vol 48 (2) ◽  
pp. 302-303
Author(s):  
J.K. Lampert ◽  
G.S. Koermer ◽  
J.M. Macaoy ◽  
J.M. Chabala ◽  
R. Levi-Setti
Keyword(s):  
Catalytic Cracking ◽  
Secondary Ion Mass Spectrometry ◽  
Fuel Oil ◽  
Ion Microprobe ◽  
Fluidized Catalytic Cracking ◽  
Ion Probe ◽  
Silica Alumina ◽  
Resolution Imaging ◽  
The University ◽  
High Spatial Resolution Imaging

We have used high spatial resolution imaging secondary ion mass spectrometry (SIMS) to differentiate mineralogical phases and to investigate chemical segregations in fluidized catalytic cracking (FCC) catalyst particles. The oil industry relies on heterogeneous catalysis using these catalysts to convert heavy hydrocarbon fractions into high quality gasoline and fuel oil components. Catalyst performance is strongly influenced by catalyst microstructure and composition, with different chemical reactions occurring at specific types of sites within the particle. The zeolitic portions of the particle, where the majority of the oil conversion occurs, can be clearly distinguished from the surrounding silica-alumina matrix in analytical SIMS images.The University of Chicago scanning ion microprobe (SIM) employed in this study has been described previously. For these analyses, the instrument was operated with a 40 keV, 10 pA Ga+ primary ion probe focused to a 30 nm FWHM spot. Elemental SIMS maps were obtained from 10×10 μm2 areas in times not exceeding 524s.

An analytical microscopic study of metals in fluidized catalytic cracking catalyst

1986 ◽  
Vol 44 ◽  
pp. 854-855
Author(s):  
Clifford S. Rainey
Keyword(s):  
Electron Microscopy ◽  
Spatial Distribution ◽  
Catalytic Cracking ◽  
Catalyst Deactivation ◽  
Coke Formation ◽  
Acid Sites ◽  
Y Zeolite ◽  
Fcc Catalyst ◽  
Fluidized Catalytic Cracking ◽  
High Regeneration

The spatial distribution of V and Ni deposited within fluidized catalytic cracking (FCC) catalyst is studied because these metals contribute to catalyst deactivation. Y zeolite in FCC microspheres are high SiO2 aluminosilicates with molecular-sized channels that contain a mixture of lanthanoids. They must withstand high regeneration temperatures and retain acid sites needed for cracking of hydrocarbons, a process essential for efficient gasoline production. Zeolite in combination with V to form vanadates, or less diffusion in the channels due to coke formation, may deactivate catalyst. Other factors such as metal "skins", microsphere sintering, and attrition may also be involved. SEM of FCC fracture surfaces, AEM of Y zeolite, and electron microscopy of this work are developed to better understand and minimize catalyst deactivation.

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