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Abstract
Desulfurization control of natural gas has long been a challenging industrial issue owing to its inherent difficulty in establishing accurate mathematical model for the nonlinear and strong coupling process. In this paper, a data-based adaptive dynamic programming (ADP) algorithm is presented to solve optimal control for natural gas desulfurization. First, neural network (NN) is used to reconstruct the dynamics of the desulfurization system via the input and output production data. Then, an improved unscented Kalman filter (IUKF) aided ADP method is presented to solve optimal control problem for desulfurization system, where IUKF algorithm is developed as a new weight-updating strategy for the action network and the critic network. The IUKF aided algorithm can improve the convergence speed as well as the anti-interference ability of the ADP controller. Furthermore, the proposed IUKF-ADP algorithm is implemented using the heuristic dynamic programming (HDP) structure. Finally, the effectiveness of the proposed IUKF-ADP algorithm is demonstrated through experiments of the natural gas desulfurization system.
Highlights
Natural gas known as a high-quality clean energy has significant economic benefits
These results demonstrate that the proposed improved unscented Kalman filter (IUKF)-adaptive dynamic programming (ADP) method guarantees the convergence speed and ensures the anti-interference ability simultaneously
This paper investigates the data-based desulfurization control problem of natural gas under the framework of ADP
Summary
Natural gas known as a high-quality clean energy has significant economic benefits. many natural gas fields are contaminated with several impurities such as hydrogen sulfide (H2S), carbon dioxide (CO2) and other sulfur compounds. W. Zhou et al.: Data-Based Optimal Tracking Control for Natural Gas Desulfurization System abilities of ADP, many successful industrial applications have been presented in recent decades [14]–[16]. Many model-based control methods cannot be used to the natural gas desulfurization system To address this problem, data-based control approach based on ADP is developed for industrial applications with unknown mathematical model [26]–[28]. In [33], a model-free data-based algorithm was proposed to obtain the optimal control law of the industrial flotation process, this method does not consider the noise interference. The key contributions of this paper are summarized as follows: 1) A data-based ADP algorithm is developed to solve optimal control problem of the nature gas desulfurization process, where the system model is unnecessary. Only the key variables are considered in this paper
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