Modelling of the Mini Batch Distillation Column

Rohman, Arief Syaichu; Rusmin, Pranoto Hidaya; Maulidda, Renny; Hidayat, Egi; Machbub, Carmadi; Mahayana, Dimitri

doi:10.15676/ijeei.2018.10.2.11

“…In forget and input gate which denoted as f k and i k , the formulas are respectively described in Eq. ( 36) and (37). Input and recurrent data from the previous state are added up.…”

Section: A Approximate Q-learningmentioning

confidence: 99%

“…5. The boiler consists of a certain amount of solvent (water and ethanol) which is denoted as the amount of solvent (M B ), concentration (X B ), and composition of steam in boiler (Y B ) [37]. The temperature in the boiler will be increased to a certain value, wherein in this study the temperature in the boiler was set to around 78 0 to 80 0 Celsius.…”

Section: ) 2nd Case Study : Batch Distillation Systemmentioning

confidence: 99%

Output Feedback Control for Deterministic Unknown Dynamics Discrete-Time System Using Deep Recurrent Q-Networks

Putri,

Hidayat,

Mahayana

et al. 2023

IEEE Access

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The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB) scheme in the field of control systems also possesses a weakness because it requires the use of an observer. This appears rather contradictory as the use of an observer requires system dynamics information. This research proposes an optimal control scheme using Deep Recurrent Q-Networks (DRQN) to generate an optimal control signal trajectory based on a collection of input and output data from the system itself. The approach proposed in this study is based on the Q-Learning method from the Reinforcement Learning (RL) scheme. The Long-Short Term Memory (LSTM) is used to approximate the Q-function and determine the control signals for a system without a known model. The method that we proposed in this study has been tested on four case studies. The control signal trajectory generated from our proposed algorithm, is much smaller than the control signal that generated from classical Q-Learning scheme. The results of this research are certainly relevant to the aim of OPFB, namely that the controller is designed to be able to regulate (bring the state trajectory to zero) and minimize control signal energy. It is empirically discovered that the same result is proven by the norm values resulting from the Q-function trajectory. The norm of Q-function trajectory for our proposed algorithm on the 1st, 2nd, 3rd, and 4th case studies are 2.11E-08, 3.15E-06, 3.79E-09, and 1.59E-13, respectively.

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“…In batch distillation, the material is loaded into boiler just for once distillation process at the beginning. On the contrary, in continous distillation, the material is fed into feeding tray, so the distillation process occurs continously [7]. In this paper, mini batch distillation column of Honeywell Laboratory ITB is analyzed in the term of CPSbased industrial process.…”

Section: Cps-based Distillation Columnmentioning

confidence: 99%

Robust PID Control Design in CPS-based Batch Distillation Column

Ayu¹,

Rusmin²,

Hidayat³

2019

EECSI

0

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Interconnected system between computation and physical process (Cyber-Physical Systems) has been widely used in industrial processes. In CPS-based industrial process, sensors, controllers, and actuators are connected into a communication network. The communication network may introduce delay time uncertainties due to shared resources and load traffic in the network. Furthermore, the nonlinear timevarying characteristic of batch distillation column may causes another uncertainties to take into account in control system design. Parameter model and delay process uncertainty is introduced due to linearized system approximation that unmodeled high-frequency dynamics. The dynamic uncertainty on both I/O channel are also introduced to the system uncertainty. In this paper, robust PI and PID controller using AMIGO method with appropriate weighting function is designed to guarantee robust stability spesification of batch distillation column. The impact of system uncertainties to closed-loop system performances such as peak overshoot and integral error is investigated. MATLAB/Simulink simulation is used to validate the methods before its implementation in CPS-based batch distillation column. Based on simulation, the proposed robust PI/PID controller can guarantee robust stability of system compared to conventional PID controller. Furthermore, the robust PI/PID controller can improve closedloop system performances compared to conventional PID.

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“…A reflux valve is located at the top part of the column and accessible for on-off control. Among other installed sensors for monitoring and feedback purposes, temperature sensors in the heater pod, top column, and cooling water are used to monitor [7].…”

Section: Cps-based Distillation Columnmentioning

confidence: 99%

Robust PID Control Design in CPS-based Batch Distillation Column

Ayu¹,

Rusmin²,

Hidayat³

2019

EECSI

0

View full text Add to dashboard Cite

Interconnected system between computation and physical process (Cyber-Physical Systems) has been widely used in industrial processes. In CPS-based industrial process, sensors, controllers, and actuators are connected into a communication network. The communication network may introduce delay time uncertainties due to shared resources and load traffic in the network. Furthermore, the nonlinear timevarying characteristic of batch distillation column may causes another uncertainties to take into account in control system design. Parameter model and delay process uncertainty is introduced due to linearized system approximation that unmodeled high-frequency dynamics. The dynamic uncertainty on both I/O channel are also introduced to the system uncertainty. In this paper, robust PI and PID controller using AMIGO method with appropriate weighting function is designed to guarantee robust stability spesification of batch distillation column. The impact of system uncertainties to closed-loop system performances such as peak overshoot and integral error is investigated. MATLAB/Simulink simulation is used to validate the methods before its implementation in CPS-based batch distillation column. Based on simulation, the proposed robust PI/PID controller can guarantee robust stability of system compared to conventional PID controller. Furthermore, the robust PI/PID controller can improve closedloop system performances compared to conventional PID.

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Modelling of the Mini Batch Distillation Column

Cited by 12 publications

References 14 publications

Output Feedback Control for Deterministic Unknown Dynamics Discrete-Time System Using Deep Recurrent Q-Networks

Output Feedback Control for Deterministic Unknown Dynamics Discrete-Time System Using Deep Recurrent Q-Networks

Robust PID Control Design in CPS-based Batch Distillation Column

Robust PID Control Design in CPS-based Batch Distillation Column

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