Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system

Li, Xiaoyong; Yi, Xiaohui; Liu, Zhenghui; Liu, Hongbin; Chen, Tao; Niu, Guoqiang; Yan, Bo; Chen, Chen; Huang, Mingzhi; Ying, Guang‐Guo

doi:10.1016/j.jclepro.2021.126343

Cited by 53 publications

(14 citation statements)

References 38 publications

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“…The commonly used parameter Settings for deep learning model training are shown in Table 1. [10] Tab. 1…”

Section: Parameter Settingsmentioning

confidence: 99%

Identification of ship medium voltage direct current power quality disturbances based on spatio-temporal fusion

Zhang

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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With the increasing number of electrical equipment used in ships, the requirements for ship power system are also getting higher and higher, so the stability of ship power system becomes more and more important. As the core of ship power system, ship microgrid has been specially studied worldwide, and the identification of dc power quality disturbance characteristics is the key to improve the dc power quality in ship microgrid. In this study, a new improved hybrid CLSTM deep learning model based on spatio-temporal fusion is proposed for real-time classification and identification of DC power quality disturbances in ship microgrid. And through the simulation of DC voltage deviation, DC voltage fluctuation, DC voltage ripple, dynamic voltage sag, line fault sag and normal six categories of DC power quality disturbance signals for ablation experiments, this study proposes an improved deep learning CLSTM model. Compared with the single LSTM model and CNN model after ablation, the average recall rate, average accuracy rate and total accuracy rate increased by 3.8%, 9.2%, 8.8% and 1.9%, 3.7%, 4.3%. Compared with other latest classification models proposed in other latest references, CLSTM model has also improved in accuracy. Therefore, the CLSTM model proposed in this paper is the ship micro grid DC power quality disturbance identification system provides a new type of improved deep learning model with ultra-high accuracy, stronger robustness and better stability.

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“…The commonly used parameter Settings for deep learning model training are shown in Table 1. [10] Tab. 1…”

Section: Parameter Settingsmentioning

confidence: 99%

Identification of ship medium voltage direct current power quality disturbances based on spatio-temporal fusion

Zhang

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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“…, 2021), optimize wastewater treatment (Niu et al. , 2020; Li et al. , 2021), estimate regional air quality (Zhang et al.…”

Section: Future Directions For DL Applications In Manufacturing Opera...mentioning

confidence: 99%

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

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PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

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“…Apeksha et al found that deep learning models could predict air quality more accurately than traditional machine learning models, which was consistent with the findings of ref . Li et al customized a novel CLSTMA model for monitoring water quality in paper industry wastewater treatment systems. Lu et al customized a deep learning model to classify recyclable waste.…”

Section: Introductionmentioning

confidence: 99%

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

Zhang,

Zhu,

Jia

et al. 2023

ACS Omega

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The detergency of motor gasoline is closely related to vehicle exhaust emissions and fuel economy. This paper proposed an improved method for the rapid detection of gasoline detergency based on the deposit images of test gasoline on aluminum plates produced by a multichannel gasoline detergency simulation test (MGST). The detection algorithm system was structured to recognize the deposit plate images by computer vision based on the convolutional neural networks (CNNs). Compared with the traditional simulation test, the improved MGST method resulted in significant reductions in fuel consumption, cost, and test time. The performance of three transfer learning models (Inception-ResNet-V2, Inception-V3, and ResNet50-V2) and a customized CNN was evaluated in the detection algorithm system, and their detection accuracies reached 94, 94, 88, and 82%. Inception-RsNet-V2 was selected due to its higher accuracy and better robustness. Based on the model interpretation, it is evident that the model undergoes feature extraction from the sediment deposits on the deposit plate. Subsequently, it employed the acquired deposit features to accurately detect gasoline samples that failed to meet detergency standards. This approach was proved to be effective in enhancing the detection process and ensuring reliable results for gasoline detergency evaluation. It is beneficial to environmental protection regulators for managing market gasoline detergency and urban mobile source pollution. In addition, a deposit plate image database should be established to further improve the detection model performance during the environmental regulation.

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Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system

Cited by 53 publications

References 38 publications

Identification of ship medium voltage direct current power quality disturbances based on spatio-temporal fusion

Identification of ship medium voltage direct current power quality disturbances based on spatio-temporal fusion

Deep learning applications in manufacturing operations: a review of trends and ways forward

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

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