Model-Based Insulin Therapy Scheduling: A Mixed-Integer Nonlinear Dynamic Optimization Approach

2009 ◽  
Vol 48 (18) ◽  
pp. 8595-8604 ◽  
Author(s):  
Cheng-Liang Chen ◽  
Hong-Wen Tsai
Keyword(s):  
Insulin Therapy ◽  
Dynamic Optimization ◽  
Nonlinear Dynamic ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Model Based

A mixed-integer dynamic optimization approach for the optimal planning of distributed biorefineries

2015 ◽  
Vol 80 ◽  
pp. 37-62 ◽  
Author(s):  
José Ezequiel Santibañez-Aguilar ◽  
Martín Rivera-Toledo ◽  
Antonio Flores-Tlacuahuac ◽  
José María Ponce-Ortega
Keyword(s):  
Dynamic Optimization ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Optimal Planning

scholarly journals A New Peak-Shaving Model Based on Mixed Integer Linear Programming with Variable Peak-Shaving Order

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 887
Author(s):  
Xianliang Cheng ◽  
Suzhen Feng ◽  
Yanxuan Huang ◽  
Jinwen Wang
Keyword(s):  
Linear Programming ◽  
Power Systems ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Time Interval ◽  
Renewable Energy Resources ◽  
Load Curve ◽  
Peak Shaving ◽  
Model Based

Peak-shaving is a very efficient and practical strategy for a day-ahead hydropower scheduling in power systems, usually aiming to appropriately schedule hourly (or in less time interval) power generations of individual plants so as to smooth the load curve while enforcing the energy production target of each plant. Nowadays, the power marketization and booming development of renewable energy resources are complicating the constraints and diversifying the objectives, bringing challenges for the peak-shaving method to be more flexible and efficient. Without a pre-set or fixed peak-shaving order of plants, this paper formulates a new peak-shaving model based on the mixed integer linear programming (MILP) to solve the scheduling problem in an optimization way. Compared with the traditional peak-shaving methods that need to determine the order of plants to peak-shave the load curve one by one, the present model has better flexibility as it can handle the plant-based operating zones and prioritize the constraints and objectives more easily. With application to six cascaded hydropower reservoirs on the Lancang River in China, the model is tested efficient and practical in engineering perspective.

Dynamic optimization approach to coordinate industrial production and cogeneration operation under electricity price fluctuations

2021 ◽  
Vol 149 ◽  
pp. 107292
Author(s):  
Cristian Pablos ◽  
Alejandro Merino ◽  
Luis Felipe Acebes ◽  
José Luis Pitarch ◽  
Lorenz T. Biegler
Keyword(s):  
Dynamic Optimization ◽  
Industrial Production ◽  
Electricity Price ◽  
Optimization Approach ◽  
Price Fluctuations

Dynamic Optimization of Steel Catenary Risers Based on Reliability Using Metamodel

2010 ◽  
Author(s):  
Wenqing Zheng ◽  
Hezhen Yang
Keyword(s):  
Dynamic Optimization ◽  
Computational Cost ◽  
Minimum Cost ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Element Analysis ◽  
Steel Catenary Riser ◽  
Reliability Based Design ◽  
Minimum Cost Design ◽  
Steel Catenary Risers

Reliability based design optimization (RBDO) of a steel catenary riser (SCR) using metamodel is investigated. The purpose of the optimization is to find the minimum-cost design subjecting to probabilistic constraints. To reduce the computational cost of the traditional double-loop RBDO, a single-loop RBDO approach is employed. The performance function is approximated by using metamodel to avoid time consuming finite element analysis during the dynamic optimization. The metamodel is constructed though design of experiments (DOE) sampling. In addition, the reliability assessment is carried out by Monte Carlo simulations. The result shows that the RBDO of SCR is a more rational optimization approach compared with traditional deterministic optimization, and using metamodel technique during the dynamic optimization process can significantly decrease the computational expense without sacrificing accuracy.

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