Data-Driven Predictive Model for Mixed Oil Length Prediction in Long-Distance Transportation Pipeline

2021 ◽  
Author(s):  
Xu Li ◽  
Wenxue Han ◽  
Weiming Shao ◽  
Lei Chen ◽  
Dongya Zhao
Keyword(s):  
Predictive Model ◽  
Data Driven ◽  
Long Distance ◽  
Mixed Oil

A novel predictive model of mixed oil length of products pipeline driven by traditional model and data

2021 ◽  
Vol 205 ◽  
pp. 108787
Author(s):  
Lei Chen ◽  
Ziyun Yuan ◽  
JianXin Xu ◽  
Jingyang Gao ◽  
Yuhan Zhang ◽  
...  
Keyword(s):  
Predictive Model ◽  
Traditional Model ◽  
Products Pipeline ◽  
Mixed Oil

scholarly journals Study on Asymmetry Concentration of Mixed Oil in Products Pipeline

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6398
Author(s):  
Yi Wang ◽  
Baoying Wang ◽  
Yang Liu ◽  
Yongtu Liang
Keyword(s):  
Energy Consumption ◽  
Concentration Curve ◽  
Adsorption Coefficient ◽  
Long Distance ◽  
Estimation Errors ◽  
Mean Velocity ◽  
Adsorption Effect ◽  
The Asymmetry ◽  
Products Pipeline ◽  
Mixed Oil

Long-distance pipelines transporting multiple product oils such as gasoline, diesel and jet fuel, are important facilities for transporting fossil energy. One major concern in operation is the energy consumption of the pipeline. Energy consumption should be made optimized tracking batches of oils and cutting mixed oil, which requires an accurate prediction of concentration curve. In engineering, the concentration curve is usually assumed to be symmetric, but it is actually asymmetric, which may lead to estimation errors. Thus, the asymmetric concentration of mixed oil should be studied. The formation mechanism of the asymmetry of concentration curve has not been clearly clarified. A new method is proposed to measure the asymmetry of the concentration curve. Quantitative analysis is carried out for each factor on the asymmetry distribution of concentration curve. Based on the convection–diffusion equation, a modified oil-mixing model considering near wall adsorption effect is established. The model shows a good agreement with the Jablonski empirical formula. The error, compared with the experimental results, is less than 5%. The main findings are: (1) deviation volume has a negative correlation with pipe diameter and mean velocity; (2) adsorption coefficient has a greater impact on the length ratio of front and tail oil than diffusion coefficient; (3) the influence of all factors considered on the total length of mixed oil, front oil, tail oil and trail oil are basically the same; (4) if the limit of adsorption concentration in adsorption layer is 1, the reasonable value of adsorption coefficient a and b should be around 0.4. The results reveal the mechanism of asymmetric concentration of product oils and can provide practical suggestions to deal with the mixed oil.

scholarly journals A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

2021 ◽  
Vol 7 ◽  
Author(s):  
Awino M. E. Ojwang' ◽  
Trevor Ruiz ◽  
Sharmodeep Bhattacharyya ◽  
Shirshendu Chatterjee ◽  
Peter S. Ojiambo ◽  
...  
Keyword(s):  
Downy Mildew ◽  
Data Driven ◽  
Disease Spread ◽  
Data Sets ◽  
Multiple Sources ◽  
Modeling Framework ◽  
Long Distance ◽  
Cucurbit Downy Mildew ◽  
Temporal Models ◽  
Spatio Temporal

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.

Fuzzy Value-at-Risk Forecasts Using a Novel Data-Driven Neuro Volatility Predictive Model

2019 ◽  
Author(s):  
Aerambamoorthy Thavaneswaran ◽  
Ruppa K Thulasiram ◽  
Zimo Zhu ◽  
Mohammed Erfanul Hoque ◽  
Nalini Ravishanker
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Value At Risk ◽  
Data Driven

scholarly journals Development of a Data-Driven Predictive Model of Clothing Thermal Insulation Estimation by Using Advanced Computational Approaches

2019 ◽  
Vol 11 (20) ◽  
pp. 5702 ◽  
Author(s):  
Lee ◽  
Choi ◽  
Choi ◽  
Kim
Keyword(s):  
Predictive Model ◽  
Thermal Conditions ◽  
Data Driven ◽  
Training Dataset ◽  
Relevant Parameter ◽  
Practical Application ◽  
Additional Test ◽  
Clothing Insulation ◽  
Climate Conditions ◽  
Study Results

Clothing condition was selected as a key human-subject-relevant parameter which is dynamically changed depending on the user’s preferences and also on climate conditions. While the environmental components are relatively easier to measure using sensor devices, clothing value (clo) is almost impossible to visually estimate because it varies across building occupants even though they share constant thermal conditions in the same room. Therefore, in this study we developed a data-driven model to estimate the clothing insulation value as a function of skin and clothing surface temperatures. We adopted a series of environmental chamber tests with 20 participants. A portion of the collected data was used as a training dataset to establish a data-driven model based on the use of advanced computational algorithms. To consider a practical application, in this study we minimized the number of sensing points for data collection while adopting a wearable device for the user’s convenience. The study results revealed that the developed predictive model generated an accuracy of 88.04%, and the accuracy became higher in the prediction of a high clo value than in that of a low value. In addition, the accuracy was affected by the user’s body mass index. Therefore, this research confirms that it is possible to develop a data-driven predictive model of a user’s clo value based on the use of his/her physiological and ambient environmental information, and an additional study with a larger dataset via using chamber experiments with additional test participants is required for better performance in terms of prediction accuracy.

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