Model predictive control of vinyl chloride monomer process by Aspen Plus Dynamics and MATLAB/Simulink co-simulation approach

In this paper, the computer-aided vehicle dynamic analysis of a 6x6 heavy military truck is presented and examined. For the analysis, a MATLAB/Simulink® platform is used to design and model a truck. The vehicle configuration taken into account for the analysis is the powertrain (engine, gear box, transfer gear, differential), suspension, steering system and tire model according to the Pacekja 89’ formulation. In addition, the effect of the rolling resistance and drag is considered, in order to represent the vehicle behavior as real as possible. The longitudinal dynamic and lateral dynamic are formulated. First, the longitudinal dynamic model is established by means of implementation of the weight transfer function. The vehicles are considered as rigid bodies with 1 degree of freedom. Second, the vehicular planar model with three wheels, well known as bicycle model, is applied following the North Atlantic Treaty Organization double line change maneuver test reaching 3 degree of freedom. The driver behavior is represented by using an adaptive model predictive control varying the longitudinal velocity. The forces for braking, inertia of the rotating components, the energy lost in the powertrain, and the effect of dive squat and rollover. The numerical simulation results are shown and compared with a full-vehicle model formed by using Mechanical Simulation Corporation’s truckSIM®. There were chosen simulation scenarios applied to the model to observe the effects of different parameters concerning the dynamic behavior, and also prepared in truckSIM® environment. The main contributions of this article are the development of the vehicular model, through the use of block diagrams in a reliable and relatively simple programming code such as MATLAB/Simulink®, with innovative tools used in the control of autonomous vehicle driving and the flexibility to adapt said model to different environmental conditions and different vehicle parameters.

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Model Predictive Control for the Process of MEA Absorption of CO2 Based on the Data Identification Model

Processes ◽

10.3390/pr9010183 ◽

2021 ◽

Vol 9 (1) ◽

pp. 183

Author(s):

Qianrong Li ◽

Wenzhao Zhang ◽

Yuwei Qin ◽

Aimin An

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Aspen Plus ◽

Open Loop ◽

Operating Conditions ◽

Effective Control ◽

Subspace Identification ◽

Capture Process ◽

The Stability ◽

Intractable Problem

The absorption process of CO2 by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO2 chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO2 related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model combined with the state-space equation using Matlab/Simulink to analyze the changes of the system under two different disturbances. The simulation results show that the control performance of the MPC algorithm is significantly better than that of the traditional proportion integral differential (PID) system, with excellent setpoint tracking ability and robustness, which improve the stability and flexibility of the system.

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