Modeling and Control of Distillation Column in a Petroleum Process

Author:

Minh Vu Trieu1ORCID,Abdul Rani Ahmad Majdi1

Affiliation:

1. Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan, Malaysia

Abstract

This paper introduces a calculation procedure for modeling and control simulation of a condensate distillation column based on the energy balance structure. In this control, the reflux rate and the boilup rate are used as the inputs to control the outputs of the purity of the distillate overhead and the impurity of the bottom products. The modeling simulation is important for process dynamic analysis and the plant initial design. In this paper, the modeling and simulation are accomplished over three phases: the basic nonlinear model of the plant, the full-order linearised model, and the reduced-order linear model. The reduced-order linear model is then used as the reference model for a model-reference adaptive control (MRAC) system to verify the applicable ability of a conventional adaptive controller for a distillation column dealing with the disturbance and the model-plant mismatch as the influence of the plant feed disturbances.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Generalized Predictive Control Design of Benchmark Distillation Columns: A Case Study for Multi-input Multi-output System;State Estimation and Stabilization of Nonlinear Systems;2023

2. H-matrix Based Stability Analysis of Chemical Process;2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2022-11-16

3. Modeling and Simulation of an Energy Integrated Distillation Column in a Bond Graph Approach;Entropy;2022-08-25

4. Modeling and Control of Rectification Column;Proceedings of the Technical University of Sofia;2022-06-14

5. Optimization of Distribution Control System in Oil Refinery by Applying Hybrid Machine Learning Techniques;IEEE Access;2022

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