Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

Liu, Yiqi; Xiao, Hongjun; Huang, Daoping; Wang, Qilin

doi:10.1016/j.chemolab.2016.07.002

Cited by 30 publications

(10 citation statements)

References 33 publications

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“…This plant uses the oxidation ditch process, which serves a population of 480 000, with a daily treatment flow of 170 000 m 3 and a hydraulic retention of 16.5 h, as shown in Figure . The oxidation ditch process is a kind of enhanced activated sludge process that has characteristics of a simple structure, long solid residence time (SRT), and stable performance . WWTPs with this process are prone to filamentous sludge bulking due to changes in temperature or other factors during seasonal transitions.…”

Section: Case Studiesmentioning

confidence: 99%

“…The oxidation ditch process is a kind of enhanced activated sludge process that has characteristics of a simple structure, long solid residence time (SRT), and stable performance. 33 WWTPs with this process are prone to filamentous sludge bulking due to changes in temperature or other factors during seasonal transitions. However, due to the deficiency and unavailability of equipment and sensors, it is intractable to monitor filamentous sludge bulking online.…”

Section: F I Imentioning

confidence: 99%

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Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Huang

Liu

et al. 2021

Ind. Eng. Chem. Res.

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The process monitoring of industries by means of multivariate statistical methods has gained popularity in academic and industrial communities. However, nonlinearity, autocorrelation, and high dimensionality can render traditional approaches inadequate. This necessitates a more powerful method for implementing better process monitoring in reality. Therefore, a novel independent component analysis (ICA) algorithm, termed complex dynamic independent component analysis (CD-ICA), is proposed for information refinement and feature extraction in this paper. This proposed algorithm extracts dominant features from a complex-valued matrix containing raw data information and their changing rates through complex ICA and properly shifting phase operations. The novel fault detection index, based on the three traditional monitoring statistics of the ICA algorithm, is enhanced and applied to monitor real chemical and biological processes efficiently by combining the aforementioned algorithm. The performance of the presented method is assessed based on data from a simple multivariate mathematical simulation and data from a real wastewater treatment plant (WWTP). The results show that this approach provides higher efficiency and performance than traditional approaches.

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Section: Case Studiesmentioning

confidence: 99%

Section: F I Imentioning

confidence: 99%

Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Huang

Liu

et al. 2021

Ind. Eng. Chem. Res.

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“…modeling has been gained popularity in the process industries, mainly due to the fact that data-driven modeling does not need to explore the complex process mechanism exactly, but resort to the collected data [3], [4]. Among them, Principal Component Regression (PCR), Partial Least Squares (PLS) [5], Gaussian process regression (GPR) [6], Support Vector Machine (SVM) [7], Deep learning networks [8] and other models have attracted extensive attentions in industrial and academic communities.…”

Section: Introductionmentioning

confidence: 99%

Learning Adaptive Semi-Supervised Multi-Output Soft-Sensors With Co-Training of Heterogeneous Models

Liu

Huang

et al. 2020

IEEE Access

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Soft-sensors are widely utilized for predictions of important but hard-to-measure variables in industrial processes. However, significant variations, process uncertainties, negative influence of external environment and insufficient use of unlabeled data always cause the attenuation of prediction performance. Thus, this paper proposed an adaptive semi-supervised multi-output soft-sensor by co-training recursive heterogeneous models. In the proposed strategy, a linear multi-output model, called recursive partial least square (MRPLS), and a nonlinear multi-output, called long short-term memory recurrent neural network (MLSTM), are co-trained to deal with inefficient use of label data adaptively. Ensemble of both models are not only able to address the linear and nonlinear hybrid behaviors in different time scale, but also able to deal with multiple tasks learning issues. In addition, the model proposed an odd-even grouping strategy to equalize two parts of the labeled data, which is able to capture the global variations of a process. To validate the prediction performance of the proposed soft-sensor, it was verified through a simulation benchmark platform (BSM1) and a real sewage treatment plant (UCI database). The results meant that co-training MRPLS-MLSTM achieved better performance compared with other existing co-training models in terms of the hard-to-measure variables. INDEX TERMS Soft-sensors, semi-supervised, co-training, multi-output, recursive partial least square (RPLS), long short-term memory recurrent neural network (LSTM).

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“…Later on, they have gained a wide popularity within the machine learning community through the works of Rasmussen and Williams (2006). Recently, Gaussian Process (GP) models are attracting huge attention in the soft-sensing area, and have been applied either for continuous (Grbić et al, 2013;Wang et al, 2016;Liu et al, 2016) or batch processes (Jin et al, 2015), offering high prediction accuracy and tuning flexibility while requiring a relatively small set of the training data. Pioneering GP metamodels were developed in the nineties (O'Hagan et al, 1999), mainly for complex computer code emulation (O'Hagan, 2001), and afterward they become popular for dynamic modeling (Azman & Kocijan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

Shokry

Vicente

Escudero

et al. 2018

Computers & Chemical Engineering

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A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other online indirect measurements. Current data modelling techniques have been also tested within the proposed methodology to demonstrate its advantages. Two simulation casestudies and a pilot-plant case-study involving a complex batch process for wastewater treatment are used to illustrate the problem, to assess the modelling approach and to compare the modelling techniques. The results reflect a promising accuracy even when the training information is scarce, allowing significant reductions in the cost associated to batch processes monitoring and control.

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Development of multiple-step soft-sensors using a Gaussian process model with application for fault prognosis

Cited by 30 publications

References 33 publications

Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Learning Adaptive Semi-Supervised Multi-Output Soft-Sensors With Co-Training of Heterogeneous Models

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

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