Integrating real-time optimization and control for optimal operation: Application to the bio-ethanol process

2010 ◽  
Vol 53 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Silvia Ochoa ◽  
Jens-Uwe Repke ◽  
Günter Wozny
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Time Optimization ◽  
Optimization And Control ◽  
And Control ◽  
Real Time Optimization

Dynamic real-time optimization and control of a hybrid energy system

AIChE Journal ◽  
2014 ◽  
Vol 60 (7) ◽  
pp. 2546-2556 ◽  
Author(s):  
Milana Trifkovic ◽  
W. Alex Marvin ◽  
Prodromos Daoutidis ◽  
Mehdi Sheikhzadeh
Keyword(s):  
Real Time ◽  
Energy System ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Time Optimization ◽  
Optimization And Control ◽  
And Control ◽  
Real Time Optimization

scholarly journals A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181
Author(s):  
Xiaochen Li ◽  
Lei Xie ◽  
Xiang Li ◽  
Hongye Su
Keyword(s):  
Real Time ◽  
Exothermic Reaction ◽  
Wait Time ◽  
Optimal Operation ◽  
Hierarchical Architecture ◽  
Least Squares Regression ◽  
Time Optimization ◽  
And Control ◽  
The Impact ◽  
Real Time Optimization

To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method.

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