Data analysis, modeling and control performance enhancement of an industrial fluid catalytic cracking unit

2007 ◽  
Vol 62 (7) ◽  
pp. 1958-1973 ◽  
Author(s):  
Rohit Ramachandran ◽  
G.P. Rangaiah ◽  
S. Lakshminarayanan
Keyword(s):  
Data Analysis ◽  
Catalytic Cracking ◽  
Performance Enhancement ◽  
Fluid Catalytic Cracking ◽  
Control Performance ◽  
Modeling And Control ◽  
And Control

Sydney Metro and Melbourne Metro Rail Stochastic Comparison and Review

2021 ◽  
Vol 4 (2) ◽  
pp. 28-38
Author(s):  
Koorosh Gharehbaghi ◽  
Kathryn M. Robson ◽  
Neville Hurst ◽  
Matt Myers
Keyword(s):  
Performance Enhancement ◽  
Stochastic Comparison ◽  
Rail Transportation ◽  
The Novel ◽  
Key Factors ◽  
Smart System ◽  
Modeling And Control ◽  
Transportation Sector ◽  
And Control ◽  
Control Apparatus

This paper aims to review an innovative artificial intelligence (AI) apparatus to enhance the rail transportation performance. In this light, the Sydney Metro and Melbourne Metro rail will be compared, since both of these Australian rail networks employ complex AI as part of their overall performance enhancement schemes. These two case studies further highlight the novel critical aspects of AI in rail transportation sector such as recalibration through smart system design and automation, nonlinear controls and precise design, modeling and control apparatus, and so on. As a part of such a view, different aspects of AI systems such as increased reliability and safety were also investigated. This research found that with such enhancements of system performance, the overall transportation functioning would ultimately be significantly improved. Subsequently, AI in the Australian context can be further refined based on comprehensive integration of the key factors.

Modeling and Control System Design of a Marine Condensing System

2011 ◽  
Author(s):  
Zhixin Sun ◽  
Jiangfeng Wang ◽  
Yiping Dai ◽  
Danmei Xie
Keyword(s):  
Control System ◽  
Cooling Water ◽  
Pi Controller ◽  
Control System Design ◽  
Control Performance ◽  
Water Pump ◽  
Feed Forward ◽  
Modeling And Control ◽  
Valve Opening ◽  
And Control

Rapid and frequent load changes bring a number of challenges to the control system of a marine condenser. However, few studies have been published in this area. In this paper, a whole condensing system, which includes condenser, ejector, cooling water pump and its driving turbine, was modeled based on three conservation laws. Propulsion steam turbine was also modeled to simulate the load changes. A proportional integral (PI) controller was developed to regulate the condenser pressure. Opening signal of the governing valve of the propulsion turbine was added to the controller as a feed forward signal, and to improve the performance further, fuzzy algorithm was adopted to tune the gains of PI controller. Numerical experiments were conducted to study the dynamic behavior and the control performance of the condensing system. The simulation results show that employing the valve opening signal of main turbine as feed forward signal and tuning the gains of conventional PI controller by fuzzy logic are both effectual approaches to enhance the control performance. The former is good at reducing maximum overshooting while the latter is good at decreasing settling time. The combination of these two methods can improve the performance of simple PI controller further.

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