Citation: Du, J.; Zhang, J.; Yang, L.;
Li, X.; Guo, L.; Song, L. Mechanism
Analysis and Self-Adaptive RBFNN
Based Hybrid Soft Sensor Model in
Energy Production Process: A Case
Study. Sensors 2022, 22, 1333.
https://doi.org/10.3390/s22041333
Academic Editors: Yangquan Chen,
Subhas Mukhopadhyay,
Nunzio Cennamo, M. Jamal Deen,
Junseop Lee and Simone Morais
Received: 4 January 2022
Accepted: 21 January 2022
Published: 10 February 2022
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Article
Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid
Soft Sensor Model in Energy Production Process: A Case Study
Junrong Du
1,2,†
, Jian Zhang
1,2,†
, Laishun Yang
3
, Xuzhi Li
1
, Lili Guo
1
and Lei Song
1,
*
1
Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization,
Chinese Academy of Sciences, Beijing 100094, China; dujunrong18@csu.ac.cn (J.D.); zj@csu.ac.cn (J.Z.);
xzhli@csu.ac.cn (X.L.); guolili@csu.ac.cn (L.G.)
2
School of Computer and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Civil Engineering and Architecture, Shandong University of Science and Technology,
Qingdao 266590, China; yangls@sdust.edu.cn
* Correspondence: songlei@csu.ac.cn; Tel.: +86-189-1039-5350
† These authors contributed equally to this work.
Abstract:
Despite hard sensors can be easily used in various condition monitoring of energy pro-
duction 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.
Keywords:
soft sensor; coal mill ventilation; mechanism analysis; radial basis function neural
network; genetic algorithm
1. Introduction
Energy production process is important to ensure national economic development
and resident’s lives quality [
1
]. To achieve real-time control, operation optimization and
process prediction, a significant number of hardware sensors are placed to supply data
for intelligent monitoring [
2
]. As a result, cost and difficulty of sensors installation and
debugging for vital parameters are increasing [
3
]. However, due to wicked installation
demands and complex work conditions in industrial process, hard sensor is limited to
obtain the fast and accurate sensing result. Recently, soft sensors have been widely used for
online estimation of process parameters thanks to their rapid response, low maintenance
costs, and accurate prediction. Soft sensors can predict the difficult-to-measure parameters
by building predictive mathematical models based on hard sensor process variables that
are easy to measure [4–6].
Basically, soft sensor models are divided into two main categories, model-driven and
data-driven methods [
3
]. Model-driven method, also known as white box model, employs
first principle modeling based on system’s physical knowledge. This kind of approach can
work well if detailed and accurate mechanism model or a wealthy of priori knowledge
about process is available. However, increasing complexity of industrial process makes
Sensors 2022, 22, 1333. https://doi.org/10.3390/s22041333 https://www.mdpi.com/journal/sensors
