Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

Wang, Yanzhen; Yan, Xuefeng

doi:10.2991/ijcis.d.190826.001

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…The relatively new approach is to use a heuristic approach, such as the artificial neural network (ANN) [37]. The autoencoder is the deep learning network applicable to represent the sensor behavior in industrial process control [38].…”

Section: A Appendix: Modeling Details A1 Modeling Prerequisitesmentioning

confidence: 99%

Testing and modeling of constant magnetic field cylindrical magnetoelectric sensors output characteristics

Kuczynski,

Bilski,

Bilski

et al. 2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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The paper presents the novel concept of the magnetoelectric sensor constructed using the amorphous glass ribbon. Its output characteristics (voltage pattern), conditions of work and experimental results are presented. The novel construction allows for minimizing the demagnetizing field in the core of the sensor and linearization of the characteristics between the magnetic field and obtained voltage. Conducted experiments were aimed at determining the sensor operation in the presence of the constant magnetic field (H DC ). The main concern of the tests was to verify the linear dependency between the H DC value and the amplitude of the output voltage. Next, the computer model representing the sensor behavior in the constant magnetic field is described. The model implements the parameter identification task based on the regression algorithms. The presented work shows that the proposed device can be used to measure the weak magnetic field and the dependency between the output signal amplitudes and the constant component in the measured magnetic field is approximately linear. This enables measurements of even weak fields.

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Section: A Appendix: Modeling Details A1 Modeling Prerequisitesmentioning

confidence: 99%

Testing and modeling of constant magnetic field cylindrical magnetoelectric sensors output characteristics

Kuczynski,

Bilski,

Bilski

et al. 2023

Bulletin of the Polish Academy of Sciences Technical Sciences

View full text Add to dashboard Cite

show abstract

“…With the development of deep learning technology in recent years, deep neural networks have emerged as a trending topic of discussion. Prominent among the widely used deep learning algorithms are deep belief networks (DBN) [19], convolutional neural networks (CNN) [20], stacked autoencoders (SAE) [21,22], and long short-term memory (LSTM) [23]. A hybrid prediction model based on DBN has been proposed to predict the oxygen content in boiler flue gas.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas

Wang,

Li,

Zhang

2024

Sensors

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As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm–long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm–long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization–least squares support vector machine (PSO-LSSVM), and particle swarm optimization–long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

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Multi‐manifold NIRS modelling via stacked contractive auto‐encoders

2021

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Considering different operation statuses of industrial processes, a multi‐manifold learning method that incorporates multi‐manifold assumption into near‐infrared spectroscopy (NIRS) modelling is proposed in this paper. Due to the nonlinearity, high dimension and high sensitivity problems of spectral data, the stacked contractive auto‐encoder (SCAE) is introduced to extract the multi‐manifold information from the spectral data. Then, in order to detect and evaluate the current operation status from the extracted manifold information, a heuristic criterion that compares reconstruction errors of the SCAE is proposed to evaluate the positional relation between the data point and sub‐manifold surface. By this means, ordinary least squares models are used to make appropriate predictions based on the low‐dimensional space derived from the SCAE. Finally, the NIRS data of the desalination and dehydration of crude oil are investigated to demonstrate the effectiveness and the practical application of the proposed method.

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Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

Cited by 9 publications

References 27 publications

Testing and modeling of constant magnetic field cylindrical magnetoelectric sensors output characteristics

Testing and modeling of constant magnetic field cylindrical magnetoelectric sensors output characteristics

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas

Multi‐manifold NIRS modelling via stacked contractive auto‐encoders

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