scholarly journals Single and Multiobjective optimal control of epidemic models involving vaccination and treatment

2021 ◽  
Author(s):  
Lakshmi N. Sridhar
Keyword(s):  
Optimal Control ◽  
Dynamic Optimization ◽  
Treatment Strategies ◽  
Epidemic Models ◽  
Nonlinear Program ◽  
Orthogonal Collocation ◽  
Optimal Control Strategy ◽  
Weighting Functions ◽  
Collocation Points ◽  
Multiobjective Optimal Control

Introduction: A rigorous multiobjective optimal control strategy (that does not require the use of weighting functions) of the epidemic models that consider vaccination and treatment strategies is presented. Modifications of the standard susceptible-infectious-removed, susceptible-exposed-infectious-removed, and the modified susceptible-infectious-removed models are dynamically optimized to minimize the number of infected individuals while, controlling the rate at which the individuals are vaccinated and treated. Method:The optimization program, Pyomo , where the differential equations are automatically converted to a Nonlinear Program using the orthogonal collocation method is used for performing the dynamic optimization calculations. The Lagrange-Radau quadrature with three collocation points and 10 finite elements are chosen. The resulting nonlinear optimization problem was solved using the solver BARON 19.3, accessed through the Pyomo-GAMS27.2 interface. Results: The computational results how that the multiobjective optimal control profiles generated by this strategy are very similar to those produced when weighting functions are used. Conclusion: The main conclusion of this work is to demonstrate that one can perform a rigorous dynamic optimization of epidemic models without the use of weighting functions that have the potential to produce some uncertainty and doubt in the results, in addition to dealing with unnecessary additional variables.

A STRATEGY OF OPTIMAL EFFICACY OF T11 TARGET STRUCTURE IN THE TREATMENT OF BRAIN TUMOR

2019 ◽  
Vol 27 (02) ◽  
pp. 225-255 ◽  
Author(s):  
SUBHAS KHAJANCHI ◽  
SANDIP BANERJEE
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Transforming Growth Factor ◽  
Treatment Strategies ◽  
Target Structure ◽  
Optimal Control Strategy ◽  
Immunosuppressive Cytokine ◽  
Quadratic Optimal Control ◽  
The Mathematical Model

We report a mathematical model depicting gliomas and immune system interactions by considering the role of immunotherapeutic drug T11 target structure (T11TS). The mathematical model comprises a system of coupled nonlinear ordinary differential equations involving glioma cells, macrophages, activated cytotoxic T-lymphocytes (CTLs), immunosuppressive cytokine transforming growth factor-[Formula: see text] (TGF-[Formula: see text]), immunostimulatory cytokine interferon-[Formula: see text] (IFN-[Formula: see text]) and the concentrations of immunotherapeutic agent T11TS. For the better understanding of the circumstances under which the gliomas can be eradicated from a patient, we use optimal control strategy. We design the objective functional by considering the biomedical goal, which minimizes the glioma burden and maximizes the macrophages and activated CTLs. The existence and the characterization for the optimal control are established. The uniqueness of the quadratic optimal control problem is also analyzed. We demonstrate numerically that the optimal treatment strategies using T11TS reduce the glioma burden and increase the cell count of activated CTLs and macrophages.

Application of Optimal Control Strategy to Converter Gas Recovery System

2012 ◽  
Vol 38 (6) ◽  
pp. 1017 ◽  
Author(s):  
Jia-Yan ZHANG ◽  
Zhong-Hai MA ◽  
Xiao-Bin QIAN ◽  
Shao-Ming LI ◽  
Jia-Hong LANG
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Optimal Control Strategy ◽  
Gas Recovery ◽  
Recovery System ◽  
Converter Gas

Optimal control strategy for cancer remission using combinatorial therapy: A mathematical model-based approach

2021 ◽  
Vol 145 ◽  
pp. 110789
Author(s):  
Parthasakha Das ◽  
Samhita Das ◽  
Pritha Das ◽  
Fathalla A. Rihan ◽  
Muhammet Uzuntarla ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Control Strategy ◽  
Optimal Control Strategy ◽  
Combinatorial Therapy ◽  
Model Based ◽  
Cancer Remission

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.

scholarly journals Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 271
Author(s):  
Yusung Lee ◽  
Woohyun Kim
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Predictive Performance ◽  
Building Energy ◽  
Data Driven ◽  
Energy Usage ◽  
Optimal Control Strategy ◽  
Save Energy ◽  
Time Required ◽  
Optimal Control Methods

In this study, an optimal control strategy for the variable refrigerant flow (VRF) system is developed using a data-driven model and on-site data to save the building energy. Three data-based models are developed to improve the on-site applicability. The presented models are used to determine the length of time required to bring each zone from its current temperature to the set point. The existing data are used to evaluate and validated the predictive performance of three data-based models. Experiments are conducted using three outdoor units and eight indoor units on site. The experimental test is performed to validate the performance of proposed optimal control by comparing between conventional and optimal control methods. Then, the ability to save energy wasted for maintaining temperature after temperature reaches the set points is evaluated through the comparison of energy usage. Given these results, 30.5% of energy is saved on average for each outdoor unit and the proposed optimal control strategy makes the zones comfortable.

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