Yadav Eadala Sarath, Shettigar J Prajwal, Poojary Sushmitha, Chokkadi Shreesha, Jeppu Gautham, Indiran Thirunavukkarasu
Department of Instrumentation and Control Engineering, Department of Mechatronics Engineering, and Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
ACS Omega. 2021 Jun 21;6(26):16714-16721. doi: 10.1021/acsomega.1c00087. eCollection 2021 Jul 6.
Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.
间歇过程在过程工业中起着非常关键和重要的作用。操作灵活性的提高以及向高质量、小批量化学品生产的趋势使得人们更加重视间歇过程。在这项工作中，对与间歇反应器过程相关的非线性进行了研究。利用从中试装置间歇反应器获得的开环数据识别了自回归外生（ARX）模型和非线性自回归外生（NARX）模型。由于其固有的非线性，传统线性控制器作用于间歇反应器时，其性能会导致操纵变量动作剧烈且能耗更大。在所提出的工作中，通过识别非线性模型并为中试装置间歇反应器设计非线性模型预测控制器，解决了这个问题。所提出方法的实施在仿真和实时实验中都使操纵变量以及反应器温度得到了平滑响应。
