Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

Zhou, Wei; Li, Zuojin; Liu, Huachao; Liu, Juan; Shi, Jianyang

doi:10.1109/access.2019.2949143

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…This shows that V * (x) is a Lyapunov function for system (13) with the control u * , whenever x (t) lies outside the compact set x. Therefore, the optimal control u * (t) developed in (20) can ensure the trajectory of system (13) to be UUB.…”

Section: B Problem Transformationmentioning

confidence: 92%

“…In [19], an event-triggered approximate optimal control structure is proposed for a nonlinear continuous time system with control constraints. In [20] addressed the challenging industrial problem of natural gas desulfurization control, and proposed an improved unscented kalman filter assisted ADP method to solve the optimal control problem of the desulfurization system. Hence, data-driven adaptive control method, which can accurately identify the complex system and achieve optimal control, is widely used in actual industrial system [21].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

et al. 2020

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Optimal control of aluminum electrolysis production process (AEPP) has long been a challenging industrial issue due to its inherent difficulty in establishing an accurate dynamic model. In this paper, a novel robust optimal control algorithm based on adaptive dynamic programming (ADP) is proposed for the AEPP, where the system subjects to input constraints. First, to establish an accurate dynamic model for the AEPP system, recursive neural network (RNN) is employed to reconstruct the system dynamic using the input-output production data. To ensure input constraints are not to exceed the bound of the actuator, the optimal control problem of the AEPP is formulated under a new nonquadratic form performance index function. Then, considering the perturbation of the AEPP, the robust control problem is effectively converted to the constrained optimal control problem via system transformation. Furthermore, a single critic network framework is developed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Finally, the proposed ADP controller is applied to the AEPP system to validate the effectiveness and performance. INDEX TERMS Adaptive dynamic programming (ADP), optimal control, input constraints, aluminum electrolysis.

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Section: B Problem Transformationmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

et al. 2020

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“…At present, the commonly used methods for controlling the slurry pH value in thermal power plants are manual control and PID control. The manual control requires good working experience from power plant staffs, 4 while PID algorithm cannot tackle the complex control object such as the slurry pH of absorption tower. In recent years, many advanced control algorithms have been developed to improve the WFGD process.…”

Section: Introductionmentioning

confidence: 99%

Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process

Dong

Wang

2021

Optim Control Appl Methods

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In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti‐model of Wiener nonlinearity unit is adopted to transform the output setting values and sampling values. Similarly in the control process, the controller output is applied to the actual controlled object after inverse transformation of the static nonlinear Hammerstein model. Through the above two inverse transformations, the controller output is identical with the input of linear link in the closed‐loop system. In this article, the inverse model compensation method is utilized to transform nonlinear process control into linear system control, avoiding the large computation of nonlinear model optimization. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.

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“…They then presented an improved adaptive dynamic programming method to solve the optimal control problem for the desulfurization system. 7 Monedero et al designed and developed a decision system based on a module of two neural networks using past operating points for energy optimization of a petrochemical plant. According to the results, the potential of increasing energy efficiency in the selected plant was around 7%.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al proposed a data‐driven optimal tracking control for a natural gas desulfurization system using neural networks to reconstruct the dynamics of the gas sweetening unit (GSU). They then presented an improved adaptive dynamic programming method to solve the optimal control problem for the desulfurization system 7 . Monedero et al designed and developed a decision system based on a module of two neural networks using past operating points for energy optimization of a petrochemical plant.…”

Section: Introductionmentioning

confidence: 99%

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

2020

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This study proposes a data-driven operational control framework using machine learning-based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi-stage framework is composed of the following steps: (a) a clustering algorithm based on Density-Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high-accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data-driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm 3 of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.

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Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

Cited by 4 publications

References 41 publications

Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

Constrained nonlinear model predictive control of pH value in wet flue gas desulfurization process

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

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