Abstract
In this paper, a novel soft sensor is developed by combining long short-term memory (LSTM) network with normalized mutual information feature selection (NMIFS). In the proposed algorithm, LSTM is designed to handle time series with high nonlinearity and dynamics of industrial processes. NMIFS is conducted to perform the input variable selection for LSTM to simplify the excessive complexity of the model. The developed soft sensor combines the excellent dynamic modelling of LSTM and precise variable selection of NMIFS. Simulations on two actual production datasets are used to demonstrate the performance of the proposed algorithm. The developed soft sensor could precisely predict the objective variables and has better performance than other methods.
Highlights
Due to technological constraints, sensor characteristics, environmental factors, etc., many variables cannot be measured or the measurement frequency is very low in actual industrial processes
All established models were simulated in the same experimental environment. e program for algorithm simulation was coded in MATLAB 2019 and run under a Windows 8.1 operating system. e simulation results are recorded with the following standards: (1) Model size (MS) means the number of candidate variables selected in the ultimate algorithm
A novel soft sensor was designed to model complex and dynamic industrial processes with time series characteristics. e long short-term memory (LSTM) network is trained by datasets taken from actual processes, and NMI is applied to select the variables related to the target variable. e proposed algorithm deletes one irrelevant variable at every step until all the variables are removed
Summary
Sensor characteristics, environmental factors, etc., many variables cannot be measured or the measurement frequency is very low in actual industrial processes. Sheela and Deepa [8] designed a synthesized model by combining self-organizing maps (SOMs) with MLP and applied it to forecast the wind speed of a renewable energy process He et al [9] developed an auto-associative hierarchical NN for a soft sensor of chemical processes, and its application to a purified terephthalic acid solvent process demonstrated the effectiveness of the algorithm. Yuan et al developed a supervised LSTM network for a soft sensor and demonstrated the superiority of the proposed soft sensor by two actual industrial datasets [19]. Sun proposed a new LSTM network by combining unsupervised feature selection and supervised dynamic modelling methods for a soft sensor and validated the network by a practical CO2 absorption column [20].
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