Understand, track and develop enterprise workplace safety, and sustainability in the industrial park

Xu, Hengjie; Mei, Qiang; Liu, Suxia; Zhang, Jingjing; Khan, Muhammad Aamir Shafique

doi:10.1016/j.heliyon.2023.e16717

Cited by 3 publications

(1 citation statement)

References 85 publications

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“…Equipment and devices in industrial production are often influenced by equipment failures, environmental changes, and human errors, which may lead to abnormal process parameters. Abnormal process parameters can negatively impact production efficiency, product quality, and operational safety [1]. Industrial processes involve many process parameters, such as temperature, pressure, and flow rate, and their ability to remain stable is crucial for product quality, safety, and efficiency [2,3].…”

Section: Introductionmentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

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Section: Introductionmentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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Investment in enhancing resilience safety of chemical parks under blockchain technology: From the perspective of dynamic reward and punishment mechanisms

Su,

Deng,

et al. 2025

Journal of Loss Prevention in the Process Industries

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Research on the Game Strategy of Mutual Safety Risk Prevention and Control of Industrial Park Enterprises under Blockchain Technology

Su,

Deng,

et al. 2024

Systems

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Systematic management of corporate safety risks in industrial parks has become a hot topic. And risk prevention and control mutual aid is a brand-new model in the risk and emergency management of the park. In the context of blockchain, how to incentivize enterprises to actively invest in safety risk prevention and control mutual aid has become a series of key issues facing government regulators. This paper innovatively combines Prospect Theory, Mental Accounting, and Evolutionary Game Theory to create a hypothetical model of limited rationality for the behavior of key stakeholders (core enterprises, supporting enterprises, and government regulatory departments) in mutual aid for safety risk prevention and control. Under the static prize punishment mechanism and dynamic punishment mechanism, the evolutionary stabilization strategy of stakeholders was analyzed, and numerical simulation analysis was performed through examples. The results show: (1) Mutual aid for risk prevention and control among park enterprises is influenced by various factors, including external and subjective elements, and evolves through complex evolutionary paths (e.g., reference points, value perception). (2) Government departments are increasingly implementing dynamic reward and punishment measures to address the shortcomings of static mechanisms. Government departments should dynamically adjust reward and punishment strategies, determine clearly the highest standards for rewards and punishments, and the combination of various incentives and penalties can significantly improve the effectiveness of investment decisions in mutual aid for safety risk prevention and control. (3) Continuously optimizing the design of reward and punishment mechanisms, integrating blockchain technology with management strategies to motivate enterprise participation, and leveraging participant feedback are strategies and recommendations that provide new insights for promoting active enterprise investment in mutual aid for safety risk prevention and control. The marginal contribution of this paper is to reveal the evolutionary pattern of mutual safety risk prevention and control behaviors of enterprises in chemical parks in the context of blockchain.

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Understand, track and develop enterprise workplace safety, and sustainability in the industrial park

Cited by 3 publications

References 85 publications

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Investment in enhancing resilience safety of chemical parks under blockchain technology: From the perspective of dynamic reward and punishment mechanisms

Research on the Game Strategy of Mutual Safety Risk Prevention and Control of Industrial Park Enterprises under Blockchain Technology

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