scholarly journals Urban spatial epidemic simulation model: A case study of the second COVID‐19 outbreak in Beijing, China

2021 ◽  
Author(s):  
Qiang Huang ◽  
Qiyong Liu ◽  
Ci Song ◽  
Xiaobo Liu ◽  
Hua Shu ◽  
...  
Keyword(s):  
Simulation Model ◽  
Epidemic Simulation ◽  
Spatial Epidemic ◽  
Beijing China

IMPACT OF THE SOUTH-TO-NORTH WATER DIVERSION PROJECT ON GROUNDWATER RESOURCES: A CASE STUDY IN PINGGU BASIN, BEIJING, CHINA

2013 ◽  
Vol 12 (11) ◽  
pp. 2239-2247 ◽  
Author(s):  
Guomin Li ◽  
Haizhen Xu ◽  
Ming Li ◽  
Shouquan Zhang ◽  
Yanhui Dong ◽  
...  
Keyword(s):  
Groundwater Resources ◽  
Water Diversion ◽  
The South ◽  
Water Diversion Project ◽  
North Water ◽  
Beijing China

Multi-scale flooding hazards evaluation using a nested flood simulation model: Case study of Jamuna River, Bangladesh

2021 ◽  
pp. 1-38
Author(s):  
Masakazu Hashimoto ◽  
Kenji Kawaike ◽  
Tomonori Deguchi ◽  
Shammi Haque ◽  
Arpan Paul ◽  
...  
Keyword(s):  
Simulation Model ◽  
Model Case ◽  
Flood Simulation ◽  
Multi Scale ◽  
Flooding Hazards

Estimating NOx removal capacity of urban trees using stable isotope method: A case study of Beijing, China

2021 ◽  
Vol 290 ◽  
pp. 118004
Author(s):  
Cheng Gong ◽  
Chaofan Xian ◽  
Bowen Cui ◽  
Guojin He ◽  
Mingyue Wei ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Urban Trees ◽  
Nox Removal ◽  
Removal Capacity ◽  
Isotope Method ◽  
Beijing China

scholarly journals Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

2021 ◽  
Vol 11 (8) ◽  
pp. 3487
Author(s):  
Helge Nordal ◽  
Idriss El-Thalji
Keyword(s):  
Simulation Model ◽  
Simulation Modeling ◽  
Study Data ◽  
Opportunistic Maintenance ◽  
Maintenance Strategies ◽  
Modeling Approach ◽  
Intelligent Maintenance ◽  
Diagnosis And Prognosis ◽  
Case Study Data

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

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