Automation of tar separation plant

Sukalkar, Mayura P.

doi:10.1109/icesa.2015.7503402

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Hence, a reliable, integrated, and advanced control setup is preferable in such industrial processes. Therefore, several attempts have been made to find a suitable control methodology which would fully cover the dynamic, complex, and nonlinear characteristics of a real-time process (Kiruthika et al, 2015;Kolmare et al, 2017;Sukalkar, 2016) and one of the most widely preferred methods to achieve adequate control of fluid level in processes is to implement controllers as Distributed Control Systems (DCS). DCS is widely utilized in processes with a large number of Inputs and Outputs (I/O), which needs to be controlled automatically with faster and remote data acquisition (Bailey and Wright, 2003;Sinopoli et al, 2003;Wu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms

Aparna

2020

ADCAIJ

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This proposed work deals with the real-time implementation of a PI level controller for a nonlinear interacting multi-input multi-output (MIMO) system using YOKOGAWA CENTUM CS 3000 DCS. Some intricate algorithms were chosen to tune the PI controller, presuming the effect of disturbances in a nonlinear interacting MIMO system. Three algorithms; a classical evolution algorithm, genetic algorithm (GA); a metaheuristic optimization algorithm, particle swarm optimization algorithm (PSO); and a robust algorithm, quantitative feedback theory (QFT) were chosen to tune thecontroller offline optimally. These controllers were then implemented in the process using distributed control systems (DCS), and the simulation results resulting from the three algorithms were compared with the experimental results. The impact of the tuning algorithms in the controller performance was studied in real-time.

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Section: Introductionmentioning

confidence: 99%

Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms

Aparna

2020

ADCAIJ

View full text Add to dashboard Cite

show abstract

Automation of tar separation plant

Cited by 1 publication

References 5 publications

Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms

Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms

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