Selection of Steam Drum Level Control Method for Multiple Drum Interacting Loops Pressure Tube-Type BWR

Gaikwad, Avinash J.; Vijayan, P.K.; Bhartiya, Sharad; Kumar, Raj; Lele, H.G.; Vaze, K.K.

doi:10.1109/tns.2011.2108666

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…In the schematic of the control scheme, the dynamics of coupling paths are represented with

C_{12} ((), s)

and

C_{21} ((), s)

C_{12} ((), s)

and

C_{21} ((), s)

are defined as follows:

C_{12} ((), s) = {(|, \frac{H_{1} ((), s)}{U_{2} ((), s)})}_{u_{1} = 0},

C_{21} ((), s) = {(|, \frac{H_{2} ((), s)}{U_{1} ((), s)})}_{u_{2} = 0} .

In practice, the CT‐MIMO system finds the application in many real‐time industries such as water purification plants, chemical plants, food processing, nuclear power plants, boilers etc. In these industries, liquid levels in the tanks are required to follow a time‐varying reference signal.…”

Section: Resultsmentioning

confidence: 99%

Disturbance observer–based adaptive boundary layer sliding mode controller for a type of nonlinear multiple‐input multiple‐output system

Gurumurthy

Das

2019

Intl J Robust & Nonlinear

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Summary In this paper, a disturbance observer–based adaptive boundary layer sliding mode controller (ABLSMC) is proposed to compensate external disturbance and system uncertainty for a class of output coupled multiple‐input multiple‐output (MIMO) nonlinear systems. To show the effectiveness of the proposed ABLMSC, a traditional adaptive sliding mode controller (ASMC) is also designed. The stability of the closed‐loop system is examined by using the Lyapunov stability approach. The proposed control approach is implemented for a class of nonlinear output coupled MIMO systems. For real‐time validation, a coupled tank system is considered for study. Finally, simulation and real‐time results show that the proposed ABLMSC gives better performance such as reduced chattering and energy efficiency than that of the ASMC and some reported works in the literature.

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“…In the schematic of the control scheme, the dynamics of coupling paths are represented with

C_{12} ((), s)

and

C_{21} ((), s)

C_{12} ((), s)

and

C_{21} ((), s)

are defined as follows:

C_{12} ((), s) = {(|, \frac{H_{1} ((), s)}{U_{2} ((), s)})}_{u_{1} = 0},

C_{21} ((), s) = {(|, \frac{H_{2} ((), s)}{U_{1} ((), s)})}_{u_{2} = 0} .

Section: Resultsmentioning

confidence: 99%

Disturbance observer–based adaptive boundary layer sliding mode controller for a type of nonlinear multiple‐input multiple‐output system

Gurumurthy

Das

2019

Intl J Robust & Nonlinear

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show abstract

“…Xiaocen Xue [26] utilized a radial basis function neural network (RBFNN) for the FTS slurry reactor control and modeling, successfully predicting the reactor temperature. Favorable control equality was reached by the introduction of a neural network and different air-water loop configurations as well [27][28][29]. In addition, the neural network is an integral part of predictive control, inverse-model-based control, and adaptive control methods [24].…”

Section: Introductionmentioning

confidence: 99%

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Liu,

Zhang,

Zhang

et al. 2024

Applied Sciences

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The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

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Fractional order PI control applied to level control in coupled two tank MIMO system with experimental validation

Roy

2016

Control Engineering Practice

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Selection of Steam Drum Level Control Method for Multiple Drum Interacting Loops Pressure Tube-Type BWR

Cited by 11 publications

References 20 publications

Disturbance observer–based adaptive boundary layer sliding mode controller for a type of nonlinear multiple‐input multiple‐output system

Disturbance observer–based adaptive boundary layer sliding mode controller for a type of nonlinear multiple‐input multiple‐output system

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Fractional order PI control applied to level control in coupled two tank MIMO system with experimental validation

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