Integrated Dynamic Simulation and Control Strategy Optimization in Rectisol Gas Purification Systems

Abstract The Rectisol process plays a pivotal role in syngas purification for clean coal utilization. Feedstock variations during gasification induce syngas flow and composition fluctuations, necessitating adaptive process control. This study develops both proportional–integral–derivative (PID) and model predictive control (MPC) strategies for the CO 2 absorption column. The MPC controller was developed in MATLAB Simulink and innovatively integrated with Aspen Plus Dynamics through the AMSimulation interface module for co‐simulation. The control strategies were evaluated under ±10 % flow and ±2 % CO 2 concentration disturbances, with purified gas quality assessed through response time (RT), overshoot (OS), and the integral of absolute error multiplied by time (ITAE). The data‐driven MPC strategy exhibits enhanced robustness against flow and composition variations compared to conventional PID control. The study offers actionable guidance for control system design in process industries.

Authors

Institutions

Publication Details

Journal
Chemical Engineering & Technology
Published
2025-11-01
DOI
https://doi.org/10.1002/ceat.70126
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated Dynamic Simulation and Control Strategy Optimization in Rectisol Gas Purification Systems

Z. X. Zhang, 朋幸 疋田, Chao Ren, Xiaogang Hao et al.
Chemical Engineering & Technology
Advanced Control Systems Optimization
article

Integrated Dynamic Simulation and Control Strategy Optimization in Rectisol Gas Purification Systems

Z. X. Zhang, 朋幸 疋田, Chao Ren, Xiaogang Hao, Dongliang Chen, Guochao Meng
article en

Abstract

Abstract The Rectisol process plays a pivotal role in syngas purification for clean coal utilization. Feedstock variations during gasification induce syngas flow and composition fluctuations, necessitating adaptive process control. This study develops both proportional–integral–derivative (PID) and model predictive control (MPC) strategies for the CO 2 absorption column. The MPC controller was developed in MATLAB Simulink and innovatively integrated with Aspen Plus Dynamics through the AMSimulation interface module for co‐simulation. The control strategies were evaluated under ±10 % flow and ±2 % CO 2 concentration disturbances, with purified gas quality assessed through response time (RT), overshoot (OS), and the integral of absolute error multiplied by time (ITAE). The data‐driven MPC strategy exhibits enhanced robustness against flow and composition variations compared to conventional PID control. The study offers actionable guidance for control system design in process industries.

Chemical Engineering & TechnologyVol. 48(11)
Supor (China) (CN), Shanghai Electric (China) (CN), Shanxi Science and Technology Department (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 56%
Advanced Control Systems Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.