Identification of Unknown Abnormal Conditions in Catalytic Cracking Process Based on Two-Step Clustering Analysis and Signed Directed Graph

Hong, Juan; Qu, Jiapeng; Tian, Wende; Cui, Zhe; Liu, Zijian; Yang, Lin; Li, Chuankun

doi:10.3390/pr9112055

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…It is generally applied in many fields such as pattern identification, image processing, artificial intelligence, and bioengineering. The calculation process [7] of this clustering algorithm is shown in Figure 4. The observed objects are initially classified, and then adjusted to get the final classification.…”

Section: Kmeans Clustering Methodsmentioning

confidence: 99%

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Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Hong

Tian

2023

Processes

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Machine learning for online monitoring of abnormalities in fluid catalytic cracking process (FCC) operations is crucial to the efficient processing of petroleum resources. A novel identification method is proposed in this paper to solve this problem, which combines cyclic two-step clustering analysis with a convolutional neural network (CTSC-CNN). Firstly, through correlation analysis and transfer entropy analysis, key variables are effectively selected. Then, the clustering results of abnormal conditions are subdivided by a cyclic two-step clustering (CTSC) method with excellent clustering performance. A convolutional neural network (CNN) is used to effectively identify the types of abnormal operating conditions, and the identification results are stored in the sample database. With this method, the unknown abnormal operating conditions before can be identified in time. The application of the CTSC-CNN method to the absorption stabilization system in the catalytic cracking process shows that this method has a high ability to identify abnormal operating conditions. Its use plays an important role in ensuring the safety of the actual industrial production process and reducing safety risks.

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Section: Kmeans Clustering Methodsmentioning

confidence: 99%

“…Hong et al proposed an abnormal operating condition identification method using TSC. By comparison, the two-step clustering method has a better identification effect than traditional clustering methods like Kmeans clustering method [7]. In actual industrial data clustering, simple TSC analysis will omit some abnormal conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Hong

Tian

2023

Processes

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“…The standardization of data processing involves converting the original data into a small specific interval, such as 0-1 or −1 to 1, through certain mathematical transformations to eliminate differences in the properties, orders of magnitude, and dimensions, making it easy to comprehensively analyze and compare indicators of different units or orders of magnitude. Z-Scores standardization is used to eliminate the influence of dimensions, as shown in Equation ( 6) [26].…”

Section: Standardized Processingmentioning

confidence: 99%

“…The collection time of all variables in the device is the same. Figure 6 shows the process of the catalytic cracking unit [26]. It mainly consists of R-1 reactor, R-2 regenerator, B-1 raw material buffer tank, C-1 fractionation column, C-2 diesel stripper, C-3 absorption column, C-4 desorption column, C-5 stabilization column, and C-6 reabsorption column.…”

Section: Process Descriptionmentioning

confidence: 99%

Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM

Hong

Tian

2023

Processes

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Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorithm are two excellent algorithms in swarm intelligent optimization. The PSO algorithm has the advantage of being a simple algorithm with fast convergence speed, fewer requirements on optimization function, and easy implementation. The CS algorithm also has these advantages, using the simulation of the parasitic reproduction behavior of cuckoo birds during their breeding period. The SIM-LSTM model is constructed in this paper, and some hyperparameters of LSTM are optimized by using the PSO algorithm and CS algorithm with a wide search range and fast convergence speed. The optimal parameter set of an LSTM is found. The SIM-LSTM model achieves high prediction accuracy. In the prediction of the main control variables in the catalytic cracking process, the predictive performance of the SIM-LSTM model is greatly improved.

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“…For anomaly detection, the LSTM model is utilized. The key features of LSTM-NN involve the usage of gating modules to optimize the problem of RNN being incapable of transmitting prior data for a longer time, resolve the problems of gradient exploding and vanishing at longer sequence training, and accomplish long-term time series learning (Hong and Tian, 2023). It includes forgetting, input, and output gates that could selectively learn input data and store relevant data in the storage unit.…”

Section: Anomaly Detection Modulementioning

confidence: 99%

Assisting Visually Impaired People Using Deep Learning-based Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems on Remote Sensing Images

Alotaibi¹,

Al‐Wesabi²,

Motwakel³

et al. 2023

Journal of Disability Research

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Anomaly detection in pedestrian walkways of visually impaired people (VIP) is a vital research area that utilizes remote sensing and aids to optimize pedestrian traffic and improve flow. Researchers and engineers can formulate effective tools and methods with the power of machine learning (ML) and computer vision (CV) to identifying anomalies (i.e. vehicles) and mitigate potential safety hazards in pedestrian walkways. With recent advancements in ML and deep learning (DL) areas, authors have found that the image recognition problem ought to be devised as a two-class classification problem. Therefore, this manuscript presents a new sine cosine algorithm with deep learning-based anomaly detection in pedestrian walkways (SCADL-ADPW) algorithm. The proposed SCADL-ADPW technique identifies the presence of anomalies in the pedestrian walkways on remote sensing images. The SCADL-ADPW techniques focus on the identification and classification of anomalies, i.e. vehicles in the pedestrian walkways of VIP. To accomplish this, the SCADL-ADPW technique uses the VGG-16 model for feature vector generation. In addition, the SCA approach is designed for the optimal hyperparameter tuning process. For anomaly detection, the long short-term memory (LSTM) method can be exploited. The experimental results of the SCADL-ADPW technique are studied on the UCSD anomaly detection dataset. The comparative outcomes stated the improved anomaly detection results of the SCADL-ADPW technique.

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Identification of Unknown Abnormal Conditions in Catalytic Cracking Process Based on Two-Step Clustering Analysis and Signed Directed Graph

Cited by 6 publications

References 24 publications

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM

Assisting Visually Impaired People Using Deep Learning-based Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems on Remote Sensing Images

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