Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling

AIChE Journal ◽  
2015 ◽  
Vol 61 (12) ◽  
pp. 4126-4139 ◽  
Author(s):  
Chao Shang ◽  
Biao Huang ◽  
Fan Yang ◽  
Dexian Huang
Keyword(s):  
Representation Learning ◽  
Soft Sensor ◽  
Feature Analysis ◽  
Process Data ◽  
Slow Feature Analysis ◽  
Sensor Modeling

scholarly journals Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

2009 ◽  
Vol 15 (3) ◽  
pp. 175-187 ◽  
Author(s):  
S.K. Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Support Vector ◽  
Petrochemical Plant ◽  
Process Data ◽  
Soft Sensing ◽  
Modeling And Optimization ◽  
Sensor Modeling

Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR), a new powerful machine learning method based on a statistical learning theory (SLT) into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression - genetic algorithm (SVR-GA) for modeling and optimization of mono ethylene glycol (MEG) quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc.) is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

scholarly journals Soft Sensor Development Based on Quality-Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene Polymerization

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Miao Zhang ◽  
Le Zhou ◽  
Jing Jie ◽  
Xiaoli Wu
Keyword(s):  
Soft Sensor ◽  
Bayesian Regression ◽  
Propylene Polymerization ◽  
Polymerization Process ◽  
Feature Analysis ◽  
Quality Indices ◽  
Soft Sensors ◽  
Slow Feature Analysis ◽  
Iterative Maximum Likelihood

Data-driven soft sensors are widely used to predict quality indices in propylene polymerization processes to improve the availability of measurements and efficiency. To deal with the nonlinearity and dynamics in propylene polymerization processes, a novel soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper. The proposed method can handle the dynamics of the process better by extracting quality-relevant slow features, which present both the slowly varying characteristic and the correlations with quality indices. Meanwhile, a Bayesian inference model is developed to predict the quality indices, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. Finally, a case study is conducted with data sampled from a practical industrial propylene polymerization process to demonstrate the effectiveness and superiority of the proposed method.

Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

2020 ◽  
Vol 16 (6) ◽  
pp. 3721-3730 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Jiao Zhou ◽  
Biao Huang ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Soft Sensor ◽  
Feature Representation ◽  
Relevant Feature ◽  
Sensor Modeling

scholarly journals An application of slow feature analysis to the genetic sequences of coronaviruses and influenza viruses

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Anastasios A. Tsonis ◽  
Geli Wang ◽  
Lvyi Zhang ◽  
Wenxu Lu ◽  
Aristotle Kayafas ◽  
...  
Keyword(s):  
Mathematical Method ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Transmission Rate ◽  
Complex Structure ◽  
Mortality Rates ◽  
Influenza Viruses ◽  
Feature Analysis ◽  
Slow Feature Analysis ◽  
Genetic Sequences

Abstract Background Mathematical approaches have been for decades used to probe the structure of DNA sequences. This has led to the development of Bioinformatics. In this exploratory work, a novel mathematical method is applied to probe the DNA structure of two related viral families: those of coronaviruses and those of influenza viruses. The coronaviruses are SARS-CoV-2, SARS-CoV-1, and MERS. The influenza viruses include H1N1-1918, H1N1-2009, H2N2-1957, and H3N2-1968. Methods The mathematical method used is the slow feature analysis (SFA), a rather new but promising method to delineate complex structure in DNA sequences. Results The analysis indicates that the DNA sequences exhibit an elaborate and convoluted structure akin to complex networks. We define a measure of complexity and show that each DNA sequence exhibits a certain degree of complexity within itself, while at the same time there exists complex inter-relationships between the sequences within a family and between the two families. From these relationships, we find evidence, especially for the coronavirus family, that increasing complexity in a sequence is associated with higher transmission rate but with lower mortality. Conclusions The complexity measure defined here may hold a promise and could become a useful tool in the prediction of transmission and mortality rates in future new viral strains.

scholarly journals Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3430
Author(s):  
Jean Mário Moreira de Lima ◽  
Fábio Meneghetti Ugulino de Araújo
Keyword(s):  
Short Term Memory ◽  
Soft Sensor ◽  
Fine Tuning ◽  
Current Layer ◽  
Layer By Layer ◽  
Linear Processes ◽  
Method Performance ◽  
Modeling Approach ◽  
Ensemble Strategy ◽  
Sensor Modeling

Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases.

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