Dynamic optimization in the presence of uncertainty: From off-line nominal solution to measurement-based implementation

2007 ◽  
Vol 17 (5) ◽  
pp. 389-398 ◽  
Author(s):  
J.V. Kadam ◽  
M. Schlegel ◽  
B. Srinivasan ◽  
D. Bonvin ◽  
W. Marquardt
Keyword(s):  
Dynamic Optimization ◽  
Nominal Solution

QoS Differentiation Based Adaptive p-Persistent MAC Scheme for Dynamic Optimization of the Channel Utilization

2009 ◽  
Vol 20 (3) ◽  
pp. 608-619 ◽  
Author(s):  
Xiang BAI ◽  
Yu-Ming MAO ◽  
Su-Peng LENG ◽  
Jian-Bing MAO ◽  
Jun XIE
Keyword(s):  
Dynamic Optimization ◽  
Channel Utilization ◽  
Qos Differentiation ◽  
Mac Scheme

Dynamic Optimization of Switching States of an Hybrid Power Network

2021 ◽  
Author(s):  
Aristide Tolok Nelem ◽  
Pierre Ele ◽  
Papa Alioune Ndiaye ◽  
Salomé Ndjakomo Essiane ◽  
Mathieu Jean Pierre Pesdjock
Keyword(s):  
Dynamic Optimization ◽  
Power Network ◽  
Hybrid Power

Optimization studies of low-density polyethylene process: effect of different interval numbers

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ashraf Azmi ◽  
Suhairi Abdul Sata ◽  
Fakhrony Sholahudin Rohman ◽  
Norashid Aziz
Keyword(s):  
Dynamic Optimization ◽  
Monomer Conversion ◽  
Polymerization Process ◽  
Low Density Polyethylene ◽  
Quadratic Program ◽  
Flow Index ◽  
Low Density ◽  
Orthogonal Collocation ◽  
Interval Numbers ◽  
Optimization Study

AbstractThe highly exothermic nature of the low-density polyethylene (LDPE) polymerization process and the heating-cooling prerequisite in tubular reactor can lead to various problems particularly safety and economic. These issues complicate the monomer conversion maximization approaches. Consequently, the dynamic optimization study to obtain maximum conversion of the LDPE is carried out. A mathematical model has been developed and validated using industrial data. In the dynamic optimization study, maximum monomer conversion (XM) is considered as the objective function, whereas the constraint and bound consists of maximum reaction temperature and product melt flow index (MFI). The orthogonal collocation (OC) on finite elements is used to convert the original optimization problems into Nonlinear Programming (NLP) problems, which are then solved using sequential quadratic program (SQP) methods. The result shows that five interval numbers produce better optimization result compared to one and two intervals.

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