Work

Dynamic optimization for SP of control loops using adaptive APC techniques

Year: 2024

Type: article

Abstract: Background Advanced Process Control (APC) is widely applied in the industrial chemical sector. Dynamic Matrix Control (DMC) is favored for its ability to handle complex optimization control problems involving multiple variables and constraints. However, changes in working conditions over long periods of industrial operation can lead to a mismatch between the predictive model and the actual system. To achieve adaptivity to working conditions, this paper proposes a setpoint (SP) dynamic optimization method for control loops based on adaptive APC techniques. Methods Firstly, an identification algorithm with Nussbaum gain is employed to update the system model in real-time. Secondly, the predictive model is adaptively updated through digital testing after changes in working conditions. Subsequently, the weight coefficients and constraints of the performance index are adaptively adjusted. Finally, this paper employs the Memory-GA-PSO (MGAPSO) algorithm to solve the performance index with varying constraints and dimensions with the goal of optimizing the SP trajectory efficiently. Significant findings The experimental results validated the accuracy of the identification algorithm and the effectiveness of the adaptive APC techniques. The adaptive APC techniques proposed in this paper achieve self-adaptation to working conditions, ensuring the long-term effectiveness of advanced control schemes and effectively avoiding model mismatch.

Source: Journal of the Taiwan Institute of Chemical Engineers

DOI: 10.1016/j.jtice.2024.105858

Authors: Zhu Wang, Hehui Zhang, Donghui Liu

Institution: China University of Petroleum, Beijing

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,650