Data-Driven Modeling Using Improved Multi-Objective Optimization Based Neural Network for Coke Furnace System

Zhang, Ridong; Tao, Jili

doi:10.1109/tie.2016.2645498

Cited by 66 publications

(24 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process flowchart Figure 1 displays the detailed process flowchart of the coke furnace. 31 First, branches FRC8103 and FRC8104 are divided from the residual oil, and then they are delivered to the corresponding convention room (F101/3) for heat treatment. When the two branches are heated approximately to 330°C, they will converge and exchange heat with the gas oil which comes from coke towers T101/5,6 in fractionating tower T102.…”

Section: Coke Furnacementioning

confidence: 99%

An improved constrained predictive functional control for industrial processes: A chamber pressure process study

Zou

2020

Measurement and Control

View full text Add to dashboard Cite

An improved constrained predictive functional control for the pressure of a coke furnace is proposed in this article. In conventional constrained model predictive control, a quadratic programming problem is usually constructed to replace the original cost function and constraints to obtain the optimal control law. Under strict constraints, however, the relevant quadratic programming problem may have no feasible solutions. Unlike conventional approaches, there are several effective relaxations introduced for the constraints in the proposed scheme; then, a new cost function and the new transformed constraints are generated. With the improved constraints and cost function, there are always acceptable solutions for the quadratic programming problem under various conditions. The validity of the presented constrained model predictive control algorithm is evaluated through the regulation of the pressure of the coke furnace.

show abstract

Section: Coke Furnacementioning

confidence: 99%

An improved constrained predictive functional control for industrial processes: A chamber pressure process study

Zou

2020

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…It is always a difficult task to set up a precise mathematical model in an optimization problem since the model parameters are full of uncertainty and it is not easy to specify parameter values [17][18][19][20]. Meanwhile, establishing a precise mathematical model is computationally expensive [21][22][23][24].…”

Section: Data-driven Optimizationmentioning

confidence: 99%

A Data-driven Approach to Solve a Production Constrained Build-order Optimization Problem

Wang

Zeng

Chen

et al. 2019

2019 Chinese Control Conference (CCC)

View full text Add to dashboard Cite

Production constrained build-order optimization problems challenge artificial intelligence research in computer game applications due to an uncertain set of constraints. Traditional approaches provide subjective values in the constraint formulation therefore resulting in unexpected performance of the optimal build-order in a game. In this article, we propose a data-driven approach to solve a build-order optimization problem in StarCraft. We formulate the constraint by learning the parameter values from game replay data, which complements more precise problem formulation. To solve the optimization, we use the improved genetic algorithm by learning initial solutions from the data. We show the performance of the data-driven methods in a StarCraft simulation platform.

show abstract

“…The study showed that the neural network model provides a more accurate estimate than the linear model and brought the system to its optimum with an advance of 20 days on the linear model. In addition, due to its capacity of generalization, the neural network model is able to provide an accurate approximation of the system behavior [17], starting with only a limited set of experimental data.…”

Section: B Modeling the Function Using A Neural Network Modelmentioning

confidence: 99%

Extremum-Seeking Control With Adaptive Excitation: Application to a Photovoltaic System

Kebir

Woodward

Akhrif

2018

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

-The objective of this study is to improve the performance of the extremum-seeking control ( ) technique in terms of time and accuracy of convergence towards the optimum operating point of a dynamic system subject to the effect of external disturbances. More precisely, the idea is to reduce the undesirable effect of time scale separation in on the performance of the closed loop system. The method consists in adaptively controlling the excitation signal amplitude using a neural network (NN) model, which gives a real-time estimate of the optimal operating point based on the measurement of the external disturbances. Stability of the proposed with adaptive excitation, referred to in the following as , is demonstrated. The superiority of compared to in terms of accuracy and time of convergence to the optimum is demonstrated both theoretically and experimentally, in the case of the optimization of a photovoltaic panel system (PV).

show abstract

Data-Driven Modeling Using Improved Multi-Objective Optimization Based Neural Network for Coke Furnace System

Cited by 66 publications

References 22 publications

An improved constrained predictive functional control for industrial processes: A chamber pressure process study

An improved constrained predictive functional control for industrial processes: A chamber pressure process study

A Data-driven Approach to Solve a Production Constrained Build-order Optimization Problem

Extremum-Seeking Control With Adaptive Excitation: Application to a Photovoltaic System

Contact Info

Product

Resources

About