Recurrent neural network based detection of faults caused byparticle attrition in chemical looping systems

Pan, Jian; Pottimurthy, Yaswanth; Wang, Dawei; Hwang, Soohwan; Patil, S. V.; Fan, Liang‐Shih

doi:10.1016/j.powtec.2020.03.038

Cited by 26 publications

(9 citation statements)

References 22 publications

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“…HNN is a variant of ANN, which demonstrates the structure of feedback and recurrent interconnected neurons with no existence of hidden layers. HNNs exhibit great performance in pattern recognition [7], fault detection [8], and clustering tasks [9]. Several distinctive features of HNNs include Content Addressable Memory (CAM), Minimization of Energy as the neuron state changed, and fault tolerance [10].…”

Section: Introductionmentioning

confidence: 99%

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

et al. 2020

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Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.

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Section: Introductionmentioning

confidence: 99%

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

et al. 2020

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“…As shown in Figure 6, the TE process includes five units: a reactor, condenser, compressor, separator, and stripper. In the revised version of the TE process, the measurement variables increase, including 22 continuous process measurements (XMEAS ), 19 component analysis measurements (XMEAS [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]), and 12 process manipulated variables (XMV [1][2][3][4][5][6][7][8][9][10][11][12]). The data from the TE process are strongly coupled, dynamic, and nonlinear.…”

Section: Dcrnn-based Fdd Modelmentioning

confidence: 99%

“…Recurrent neural networks (RNNs) have also gradually been used in fault diagnosis tasks in chemical processes. [23,27,28] Compared with machine learning, deep learning has obvious advantages in the application of FDD as it contains richer information. Although many successful cases have been reported, FDD research based on deep learning has not reached its peak.…”

Section: Introductionmentioning

confidence: 99%

Chemical process fault diagnosis based on a combined deep learning method

Bao

Guo

et al. 2021

Can J Chem Eng

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The study on fault detection and diagnosis (FDD) of chemical processes has always been the top priority of the chemical process safety. In this paper, a fault diagnosis method combining the deep convolutional with the recurrent neural network (DCRNN) is proposed. In this method, the data from chemical processes are input to the deep convolutional neural network (DCNN) to extract features in spatial domains, and then, the features are fused into the bidirectional recurrent neural network (BRNN). Due to the powerful capabilities of DCNN to extract features in spatial domains and the sensitivity to time series of RNN, the combined method can adaptively learn the dynamic information of the raw data in both spatial and temporal domains and has unique advantages in multivariate chemical processes. The application of the DCRNN model in the Tennessee Eastman (TE) process demonstrates the high accuracy of this proposal in identifying abnormal conditions for the chemical process, compared with the traditional fault identification algorithms of deep learning.

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“…Shahnazar proposed a fault detection and isolation (FDI) methodology that enables the diagnose of single, multiple, and simultaneous actuators and sensor faults without the existence of a first-principles model or plant fault history . Pan et al presented the LSTM-based RNN to detect the tendency of arching in the standpipe of a chemical looping system …”

Section: Applicationmentioning

confidence: 99%

“…148 Pan et al presented the LSTM-based RNN to detect the tendency of arching in the standpipe of a chemical looping system. 149 Although neural network-based FDD methods are really powerful in chemical process systems, some limitations still exist. The first limitation is the lack of data.…”

Section: Applicationmentioning

confidence: 99%

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Jiao

et al. 2020

ACS Chem. Health Saf.

118

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Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decisionmaking. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.

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Recurrent neural network based detection of faults caused byparticle attrition in chemical looping systems

Cited by 26 publications

References 22 publications

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Chemical process fault diagnosis based on a combined deep learning method

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

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