Abstract
Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA) is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.
Highlights
The control valve is a common element in modem process control, which is used to control the flow of a fluid
This paper explores the feasibility of using the least squares support vector machine (LS-support vector machine (SVM)) model to forecast the upstream pressure of control valve
The LS-SVM is used to capture the relationship between the upstream pressure and the properties of the fluid and the control valve
Summary
The control valve is a common element in modem process control, which is used to control the flow of a fluid. This makes it is quite difficult to accurately forecast the physical variable of control valve To overcome this problem, some simulation calculation methods are used, such as bondgraph approach [3] and system identification. Several meta-heuristic algorithms have been used to determine the optimal values of these two parameters, including particle swarm optimization [11], genetic algorithm [12], chaotic differential evolution approach [20], artificial bee colony algorithm [21], and simulated annealing algorithm [22] These algorithms have some drawbacks such as being hard to understand and reaching the global optimal solution slowly. This paper attempts to use the FOA to optimize the two necessary parameters in order to improve the performance of the LSSVM model in upstream pressure forecasting of the control valve.
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