@inproceedings{scholars7395,
       publisher = {Elsevier Ltd},
            year = {2016},
         journal = {Procedia Engineering},
            note = {cited By 11; Conference of 4th International Conference on Process Engineering and Advanced Materials, ICPEAM 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:131138},
           pages = {1104--1111},
             doi = {10.1016/j.proeng.2016.06.601},
           title = {Control of Depropanizer in Dynamic Hysys Simulation Using MPC in Matlab-Simulink},
          volume = {148},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014035602&doi=10.1016\%2fj.proeng.2016.06.601&partnerID=40&md5=bf3dd9787982706a1f3b8693c3332d1e},
        abstract = {Control of depropanizer process is challenging due to the interaction caused by the multivariable nature of the process and significant non-linearity of the system. In-situ plant tests are mandatory for application of MPC in a real industrial environment due to the necessity of the model for the design of the controller. However, these tests are costly for most chemical industries due to process complexities, significant disturbances and unavoidable process interruptions. In this study, Aspen Hysys is used to simulate the real plant, generate input-output data to develop the plant model and to conduct performance tests. Matlab-Simulink is used to conduct model identification, design the MPC and implement the multivariable control action. The control objective is to reduce variation of product purity due to operation constraints. The paper demonstrates how Aspen Hysys and Matlab-Simulink can be effectively used for control study. {\^A}{\copyright} 2016 The Authors.},
          author = {Tuan, T. T. and Tufa, L. D. and Mutalib, M. I. A. and Abdallah, A. F. M.},
        keywords = {Chemical industry; MATLAB; Model predictive control; Predictive control systems; Process engineering, ARX model; Closed loop test; Control objectives; Industrial environments; Model identification; Multivariable control; Operation constraint; Process complexity, Process control},
            issn = {18777058}
}