Abstract
Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study.
Highlights
Energy production process is important to ensure national economic development and resident’s lives quality [1]
The centers of Radial basis function (RBF) are determined via unsupervised clustering using k-means algorithm, and improved genetic algorithm (GA) algorithm is adopted to optimize the determination of the spread factors for Radial Basis Function Neural Network (RBFNN)
We demonstrate that mechanism analysis is conducive to fully utilize the prior knowledge, and obtain the strong- relevant features of objective variate from high dimensionality of process data
Summary
Energy production process is important to ensure national economic development and resident’s lives quality [1]. Cost and difficulty of sensors installation and debugging for vital parameters are increasing [3]. Due to wicked installation demands and complex work conditions in industrial process, hard sensor is limited to obtain the fast and accurate sensing result. Soft sensors have been widely used for online estimation of process parameters thanks to their rapid response, low maintenance costs, and accurate prediction. RBF wasof first proposed by Powell and has been widely actual distance from the origin. Was first proposed by Powell and has been widely used in the analysis of clustering and pattern recognition [26]. A typical RBFNN consists of three layers: neural network with as an activation function.
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