### Abstract

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

Original language | English |
---|---|

Pages (from-to) | 942-949 |

Number of pages | 8 |

Journal | Korean Journal of Chemical Engineering |

Volume | 21 |

Issue number | 5 |

Publication status | Published - 2004 Sep 1 |

### Fingerprint

### Keywords

- Constraint on Input Movement
- k-Nearest Neighbor Method
- Neural Network
- pH Neutralization Process
- The NDP (Neuro-Dynamic Programming)

### ASJC Scopus subject areas

- Chemistry(all)
- Chemical Engineering(all)

### Cite this

*Korean Journal of Chemical Engineering*,

*21*(5), 942-949.

**Control of pH neutralization process using simulation based dynamic programming.** / Kim, Dong Kyu; Lee, Kwang Soon; Yang, Dae Ryook.

Research output: Contribution to journal › Article

*Korean Journal of Chemical Engineering*, vol. 21, no. 5, pp. 942-949.

}

TY - JOUR

T1 - Control of pH neutralization process using simulation based dynamic programming

AU - Kim, Dong Kyu

AU - Lee, Kwang Soon

AU - Yang, Dae Ryook

PY - 2004/9/1

Y1 - 2004/9/1

N2 - The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

AB - The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

KW - Constraint on Input Movement

KW - k-Nearest Neighbor Method

KW - Neural Network

KW - pH Neutralization Process

KW - The NDP (Neuro-Dynamic Programming)

UR - http://www.scopus.com/inward/record.url?scp=10044286133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=10044286133&partnerID=8YFLogxK

M3 - Article

VL - 21

SP - 942

EP - 949

JO - Korean Journal of Chemical Engineering

JF - Korean Journal of Chemical Engineering

SN - 0256-1115

IS - 5

ER -