Operational control of an integrated drum boiler of a coal fired thermal power plant

Chandrasekharan, Sreepradha; Panda, Rames C.; Swaminathan, Bhuvaneswari; Panda, Atanu

doi:10.1016/j.energy.2018.06.157

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…where K 2 , T 2 , K 1 and T 1 are the characteristic parameters obtained when modelling the main steam temperature object [2]. When the CFBB load changes between 100% and 25%, the change range of the parameters in the transfer function of the leading zone and the inertia zone is K 1 is 0.5-0.8, T 1 is 80-100 seconds, K 2 is 1-2, and T 2 is 35-50 seconds.…”

Section: The Cfbb Main Steam Temperature Object Dynamic Characteristicsmentioning

confidence: 99%

Automatic control system of boiler thermal energy in thermal power plant based on artificial intelligence technology

Zeng

Cheng

2021

THERM SCI

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Thermal processes tend to have large inertia and hysteresis, non-linearity, and slow time-varying. Therefore, the fixed-parameter proportional integral derivative conventional regulation system cannot meet the higher and higher control requirements in production. Based on this research background, the paper proposes an automatic control method for thermal boiler steam based on artificial intelligence technology. Through the real-time monitoring of the boiler, the state monitoring method is used to estimate the influence factors of the boiler, and the estimated error output is artificially supplemented to realize the accurate control of the boiler. After being put on the market, it is found that the control method proposed in the article can overcome the randomness and inertia of the temperature and accurately realize the temperature control of the boiler. Moreover, compared with the traditional proportional integral derivative control, this method is more effective.

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Section: The Cfbb Main Steam Temperature Object Dynamic Characteristicsmentioning

confidence: 99%

Automatic control system of boiler thermal energy in thermal power plant based on artificial intelligence technology

Zeng

Cheng

2021

THERM SCI

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“…In many situations (like boiler), temperature control is associated with the liquid level of the tank giving rise to inverse response or nonminimum phase behaviour. 7 Combinations with similar or different geometry give rise to interactions between input and output in controlling liquid levels. A ˚stro¨m et al 8 formulated a mathematical model and used it for controlling liquid level for a quadruple tank system with similar cylindrical storage geometry.…”

Section: Introductionmentioning

confidence: 99%

“…Successive optimal filtering/estimation for non-linear plants under theoretical and practical barriers with stochastic inputs is still in the developmental stage. 7,12 Unmodelled dynamics and changes in environmental conditions desire to update controller rather than process parameters through estimation techniques.…”

Section: Introductionmentioning

confidence: 99%

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Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Panda,

Thirunavukarasu,

Panda

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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Liquid-level control in an interacting storage-tank system exhibits major challenges due to the effect of interaction and the presence of combustible/flammable liquid as it faces huge threats from the unmeasurable disturbances, irregular geometry and unmodelled dynamics of the process plant. A combination of tanks with multiple geometries may be preferred for storing liquids using common pumps for energy conservation. Continuous improvement of risk assessment, predicting hazardous scenarios and implementation of inherently-safer-control systems ensure process safety and, as a result, ease out the safe operation. From the process analysis, it was observed that the dynamics of the interacting tank are very sensitive to effective parameters like valve coefficient and pump gain. To overcome the loss of inventory or production, we investigate the synthesis of a non-linear model predictive control scheme with an outline of simultaneous state and parameter estimation strategies. This mechanism deals with a stochastic event-trigger cubature Kalman filter scheme to estimate process states while performing servo operations. To mitigate plant uncertainty, reduce the impact of large disturbances and nullify the effect of actuator failure/sensor fault, influential model parameters are estimated simultaneously with the model state(s). Predicted values of the model states/parameters/faults are used to derive an effective control effort. An inferential non-linear model predictive control paradigm is proposed, where the primary variable(s) is (are) measured with the help of measurable secondary state(s). To demonstrate the practical utility and effectiveness of the control framework for safe operation and loss prevention, a complex industrial process (quadrupled tank) is considered for control, where the system dynamics have been structured newly. Realistic simulations such as servo-regulatory compliance and elimination of measurement noise with a state-of-the-art simulator ensures the efficacy of the proposed scheme. The results are analysed further for a comparative study with traditional non-linear model predictive control law. To guarantee convergence of the estimator and stability of the closed-loop controller, the Lyapunov theorem is well blended.

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“…Performance of an MPC scheme depends predominantly on the choice of the model, that is, how good the model can predict the dynamic behaviour of the actual plant/process. There are variety of models available in the literature which are commonly used in MPC such as Hammerstein‐Wiener based model, input‐output data based model, operator theory based linear model, ARX model, ARMAX model, second‐order Volterra series model, ARX‐Volterra model, fuzzy rule based model, NN‐based model, ARX‐NN model, grey‐box model using state and output feedback, event‐driven observer‐based output‐feedback model, models obtained using support vector regression, and support vector machine techniques. Even though these models are quite useful and well established for predicting the behaviour of the actual system/process, they do not explicitly consider the effects of stochastic uncertainties like process and measurement noises, random variation in process parameters, stochastic disturbances, and unmodelled dynamics.…”

Section: Introductionmentioning

confidence: 99%

Design and application of nonlinear model‐based tracking control schemes employing DEKF estimation

Bhadra

Panda

Bhowmick

et al. 2019

Optim Control Appl Methods

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Summary This paper deals with the design and application of nonlinear model‐based control schemes for stable and nonlinear benchmark industrial processes. The primary control objective is to facilitate set‐point (constant/time‐varying) tracking in the presence of external disturbances, process noise, measurement noise, parametric uncertainty, and model mismatch. We first propose a “noninferential‐type” model‐based control scheme which involves a finite‐dimensional, nonlinear, and deterministic process model to generate the model states. Secondly, an “inferential‐type” model‐based control scheme has been introduced particularly to take into account the stochastic uncertainties such as process noise and measurement noise. The second scheme exploits the dual extended Kalman filter for estimating the immeasurable states and the process parameters through which disturbance is injected. Unlike fixed‐parameter controllers, the proposed schemes update the controller gains at each step depending on the real‐time process gains. In order to demonstrate the usefulness of the proposed closed‐loop tracking control schemes, two exhaustive case studies have been carried out on the CSTR and Van de Vusse reactor processes, which are considered to be benchmark industrial processes due to highly nonlinear and unpredictable behaviour and due to nonminimum phase property. Finally, the performance of the proposed schemes are compared with an EKF‐based adaptive PI control framework and the simulation results reveal that the transient performance of the proposed schemes are better than that of the aforementioned PI technique especially in perturbed condition (ie, in presence of model mismatch and measurement noise).

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Operational control of an integrated drum boiler of a coal fired thermal power plant

Cited by 18 publications

References 28 publications

Automatic control system of boiler thermal energy in thermal power plant based on artificial intelligence technology

Automatic control system of boiler thermal energy in thermal power plant based on artificial intelligence technology

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Design and application of nonlinear model‐based tracking control schemes employing DEKF estimation

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