Modelling of Interactive Multivariable Systems for Control

Besta, Chandra Shekar; Chidambaram, M.

doi:10.1007/978-981-10-1633-2_31

Cited by 1 publication

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“…This section describes how the model Identification for RD-DPC process is carried out [18]. When the matching process employs optimization, a model prediction is aligned with the measured values with the use of a solver.…”

Section: Rd-dpc Model Identificationmentioning

confidence: 99%

Centralized and Decentralized Control System for Reactive Distillation Diphenyl Carbonate Process

Bandsode¹,

Besta²

2022

Distillation Processes - From Solar and Membrane Distillation to Reactive Distillation Modelling, Simulation and Optimization

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Reactive distillation (RD), a process-intensified technique, involves the integration of reaction and separation in a single unit. High non-linearities associated with the reactive distillation process constrict the control degrees of freedom and set the key challenge in the design of a robust control system. In this chapter, reactive distillation diphenyl carbonate (RD-DPC) design is optimized, and a decentralized as well as centralized feedback control configuration is applied to carry out the control studies. To execute the control scheme, a dynamic model of RD-DPC process is developed using Aspen Dynamic and interfaced with MATLAB Simulink for online control implementation. A comparative multi-loop feedback controller control performance study is done for different transfer function models obtained by using analytical- and optimization-based process identification techniques. The controller parameters obtained from the simple internal model control (SIMC) tuning relations for decentralized controller and Tanttu & Lieslehto (TL) tuning relations for centralized controller are applied to (i) the linear transfer function model and (ii) non-linear plant model. Set-point tracking, load rejection studies and robust stability analysis are carried out to compare the performance of different models and to investigate the controller performance of the non-linear model.

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Section: Rd-dpc Model Identificationmentioning

confidence: 99%