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modeling hot strip rolling process under framework of generalized additive model

Journal of Central South University 2019年第9期26卷 2379-2392页

作者： LI Wei-gang YANG Wei ZHAO Yun-tao YAN Bao-kang LIU Xiang-hua Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China National-provincial Joint Engineering Center of High Temperature Materials and Lining Technology Wuhan University of Science and TechnologyWuhan 430081China Research Institute of Science and Technology Northeastern UniversityShenyang 110819China

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and ***,the proposed modeling method includes the following ***,the influence factors are screened using mechanism knowledge and data-mining ***,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the ***,the interactions between the various factors are explored,and the binary GAM with interactions is *** relationships among the sub-models are analyzed,and the integrated model is ***,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are *** actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,*** experimental results suggest that the proposed method offers a new approach to industrial process modeling.

关键词： industrial big data generalized additive model mechanical property prediction deformation resistance prediction

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Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

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CHEMOSPHERE 2023年第1期325卷

作者： Gao, Ziqi Ivey, Cesunica E. Blanchard, Charles L. Do, Khanh Lee, Sang-Mi Russell, Armistead G. Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Univ Calif Riverside Dept Chem & Environm Engn Riverside CA USA Ctr Environm Res & Technol Riverside CA USA Envair Albany CA USA Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA USA South Coast Air Qual Management Dist Diamond Bar CA USA

Annual fine particulate matter (PM2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 mu g/m3 to 11 mu g/m3 between 2000 and 2013 but rose from 11 mu g/m3 to 13 mu g/m3 between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control pol-icies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM2.5 and to assess factors leading to the increase. The GAM had an R2 = 0.99 and root mean square error (RMSE) = 0.7 mu g/m3 for the annual average PM2.5 concentrations. The two-step method had an R2 = 0.93 and RMSE = 4.07 mu g/m3 for the 98th percentile 24-hr average PM2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH3 emissions with NOx and SO2 emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM2.5 explained the majority of the daily PM2.5 fluctuations. Emission changes (increases in SO2 and PM2.5) led to increases in predicted PM2.5 between 2014 and 2018. Predicted future PM2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 mu g/m3 (annual) and 30 mu g/m3 (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM2.5. Meteorological impacts on the predicted PM2.5 in future years lead to variations of +/- 2 mu g/m3 for the annual average and +/- 5 mu g/m3 for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM2.5 levels in some years exceed the standard.

关键词： PM2 5 generalized additive model Meteorological impact Emission impact South coast air basin

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Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine

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ATMOSPHERIC ENVIRONMENT 2021年 255卷 118437-118437页

作者： Cheng, Bowen Ma, Yuxia Feng, Fengliu Zhang, Yifan Shen, Jiahui Wang, Hang Guo, Yongtao Cheng, Yifan Lanzhou Univ Coll Atmospher Sci Key Lab Semiarid Climate Change Minist Educ Lanzhou 730000 Peoples R China

Particulate matter with a diameter of <2.5 mu m (PM2.5) is a critical air pollutant that adversely affects human health and the ecological environment. Using the data of daily air pollutant concentration and meteorological elements from 2013 to 2020 in Lanzhou, China, we employed the generalized additive model (GAM) and gradient boosting machine (GBM) approaches to analyze the relationship between PM2.5 concentration and environmental factors. The results revealed that the annual average PM2.5 concentration in Lanzhou, China, decreased by 3.98 mu g/m3 per year during the study period and the variation of PM2.5 concentration was influenced by many factors. Mean temperature, air pressure, relative humidity, and O3 concentration had significantly negative effects on PM2.5 concentration, whereas CO and NO2 concentrations had significant positive effects. PM2.5 concentration was also high during static winds or high-speed winds. Among these influencing factors, the interactions of meteorological factors and air pollution also had strong correlations with PM2.5 concentration. We found that the multi-factor model could better explain the influence of environmental factors on PM2.5 concentration than the single-factor model. The fitting degree of the GBM was better than that of the GAM model;their mean absolute errors were 11.30 and 11.85, respectively. We also found that the GBM model was more precise for estimating daily PM2.5 concentration, whereas the GAM was more suitable for long-term trend analysis.

关键词： PM2 5 Meteorological factors Air pollution generalized additive model Gradient boosting machine

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Combining multiple nondestructive inspection images with a generalized additive model

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MEASUREMENT SCIENCE AND TECHNOLOGY 2008年第8期19卷

作者： Liu, Zheng Ramuhalli, Pradeep Safizadeh, Saeed Forsyth, David S. Natl Res Council Canada Inst Aerosp Res Ottawa ON K1A 0R6 Canada Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

In this paper, multiple nondestructive inspection (NDI) images are combined with a generalized additive model to achieve a more precise and reliable assessment of hidden corrosion in aircraft lap joints. Two inspection techniques are considered in this study. One is the conventional multi-frequency eddy current testing technique and the other is the pulsed eddy current technique. To characterize the thickness loss or equivalently to achieve a quantitative measure of corrosion, multiple NDI images are fused to produce a thickness map that reflected the amount of corrosion damage. These results are further compared with corresponding digital x-ray thickness maps, which are obtained by mapping the remaining thickness after the specimen is dissembled and all the corrosion products are cleaned. Experimental results demonstrate that the proposed algorithms outperform the traditional calibration method aligned with a single testing approach.

关键词： nondestructive inspection eddy current testing corrosion quantification generalized additive model data fusion

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Day-ahead heat load forecasting during the off-season in the district heating system using generalized additive model

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ENERGY AND BUILDINGS 2023年 278卷

作者： Bujalski, Maciej Madejski, Pawel Fuzowski, Krzysztof AGH Univ Sci & Technol Mickiewicz 30 Av PL-30059 Krakow Poland PGE Energ Ciepla SA Ul Zlota 59 PL-00120 Warsaw Poland

The paper presents the results of heat demand forecasting during the off-season (summertime and tran-sitional period) in a real district heating system. The hourly heat demand in the analyzed system ranges from 16 to 28 MW, depending on the month and hour of the day. Short-term prediction of the heat demand in the day-ahead horizon is needed for effective operation of production units in the Cogeneration Heat and Power Plant (CHP). The method of an hourly heat load predictions using a data-driven model is presented. The algorithm is based on the generalized additive model with hour of the day, day of the week and day of the year as predictors. A way of increasing the accuracy of forecasts using the autoregressive model for residuals of the model was discussed. The influence of the training dataset size on the model error was also examined. The several versions of the model were evaluated by comparing forecasted results with real values during the district heating system's operation from June to the end of August. For the most accurate one, Root Mean Square and Mean Average Percentage Error were 0.84 MW and 3.3 %, respectively. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Heat demand forecasting generalized additive model Statistical learning District heating system Combined heat and power plant

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Calibrating nonparametric cellular automata with a generalized additive model to simulate dynamic urban growth

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ENVIRONMENTAL EARTH SCIENCES 2017年第14期76卷 496-496页

作者： Feng, Yongjiu Tong, Xiaohua Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China Shanghai Ocean Univ Key Lab Sustainable Exploitat Ocean Fisheries Res Shanghai 201306 Peoples R China Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China

Understanding factors that drive urban growth is essential to cellular automata (CA) based urban modeling. Multicollinearity among correlated factors may cause negative effects when building CA transition rules, leading to a decrease in simulation accuracy. We use a nonparametric generalized additive model (GAM) to evaluate these relationships through flexible smooth functions to capture the dynamics of urban growth. A GAM-based CA (termed GAM-CA) model was then developed to simulate the rapid urban growth in Shanghai, China from 2000 to 2015. GAM highlights the significance of each candidate factor driving urban growth during the past 15 years. Compared to logistic regression, the GAM-CA transition rules fitted the observed data better and yielded improved overall accuracy and hence more realistic urban growth patterns. The new CA model has great potential for capturing key driving factors to simulate dynamic urban growth, and can predict future scenarios under various spatial constraints and conditions.

关键词： Urban growth Cellular automata Nonparametric transition rules generalized additive model Shanghai

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A Kernel Density Estimation Approach and Statistical generalized additive model of Western North Pacific Typhoon Activities

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ATMOSPHERE 2022年第7期13卷 1128页

作者： Wang, Xiang Wahiduzzaman, Md Yeasmin, Alea Nanjing Univ Informat Sci & Technol Inst Climate & Applicat Res Nanjing 210044 Peoples R China Federat Univ Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

This paper presents a development of a statistical model of typhoon genesis, tracks based on kernel density estimation and a generalized additive model (GAM). modeling of typhoon activity is ultimately beneficial to the people living in coastal zones, insurance/re-insurance companies, policy, planning and decision departments. A 50-year record (1972-2021) of typhoon track observations from the International Best Track Archive Climate Stewardship have been used to observe the distribution of typhoon genesis by kernel density estimation. The tracks are simulated through the development of a GAM. It reproduces the observation well. A distance calculation approach between observed and simulated tracks' landfall have been used to validate the model and the model shows a very good skill (approximately 75%).

关键词： kernel density estimation generalized additive model typhoon Western North Pacific

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Combined generalized additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

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ECOLOGICAL INDICATORS 2021年 130卷 108082-108082页

作者： Zhang, Jinpeng Zhi, Mengmeng Zhang, Ying Shandong Normal Univ Coll Geog & Environm Jinan 250358 Shandong Peoples R China Chinese Res Inst Environm Sci Beijing 100012 Peoples R China Xian Univ Architecture & Technol Sch Environm & Municipal Engn Xian 710055 Shaanxi Peoples R China

The over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods including generalized additive model (GAM) and Random Forest (RF) algorithm were employed. A long-term monitoring dataset of 8 sampling sites ranging from January 1996 to December 2010 was applied to explore the driven factors and response relationships of phytoplankton biomass from the different scales in Lake Okeechobee. Results showed a spatially heterogeneous distribution of phytoplankton biomass there, and the western and southern littoral zones occurred heavier algal blooms than the central pelagic zones. Spearman's correlation results denoted Chlorophyll a (Chla) was negatively correlated with phosphorus in different scales. In the aspect of temporal variations, phytoplankton biomass appeared a three-peak-two-valley variation trend there, which peaked in 1997, 2003, and 2010, respectively. On the lake-wide scale, the RF model indicated that the inorganic nutrients were the primary predictors of phytoplankton biomass, while underwater light availability factors were followed to play essential roles in the prediction. On the local scale, total nitrogen (TN) was the top predictor in the western near-shore zone, while other zones were nitrate and nitrite (NO23) or phosphate (PO4). GAM results suggested that phytoplankton biomass had positive response to TN increase in both lake-wide and local scales. However, its response to water temperature (WT) appeared spatial heterogeneity. This study provided a new perspective to evaluate the primary predictors of phytoplankton biomass with their response relationships from lake-wide scale and local scale in the large shallow eutrophic lake, and the importance of taking spatial heterogeneity into account for lake water quality mana

关键词： Eutrophication Lake Okeechobee Random Forest Nutrients generalized additive model

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Source attribution of air pollution using a generalized additive model and particle trajectory clusters

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 780卷 146458-146458页

作者： de Foy, Benjamin Heo, Jongbae Kang, Ji-Yoon Kim, Hwajin Schauer, James J. St Louis Univ Dept Earth & Atmospher Sci St Louis MO 63103 USA Busan Dev Inst Busan South Korea Korea Inst Sci & Technol Ctr Environm Hlth & Welf Res Seoul South Korea Univ Wisconsin Environm Chem & Technol Program Madison WI USA Wisconsin State Lab Hyg Madison WI USA

Speciated hourly measurements of fine aerosols were made for more than two years at an urban, an industrial and a port site in Busan, Korea. A generalized additive model (GAM) was designed to deconvolve factors contributing to the pollutant concentrations at multiple scales. The model yields estimates of source contributions to pollution by separately identifying the signals in the time series due to meteorology, vertical mixing, horizontal wind transport and temporal variations such as diurnal, weekly, seasonal and annual trends. The GAM model was expanded to include FLEXPART back trajectory clusters generated using fuzzy c-means clustering. This made it possible to quantify the impact of long-range transport using the Trajectory Cluster Contribution Function (TCCF). TCCF provides a development of methods such as Concentration Field Analysis and Potential Source Contribution Function by providing numerical estimates of concentration changes associated with different air mass transport patterns while accounting for possible confounding factors from meteorology. The GAM simulations identified the importance of local transport for primary pollutants and long-range transport from China for secondary pollutants. Local factors accounted for up to 72% of the variance in concentrations of NO2 and elemental carbon whereas large-scale/seasonal factors accounted for up to 56% of PM2.5 and 80% of inorganic species. The algorithm further identified the importance of the weekend effect and the holiday effect at the different sites in Busan. The residual from the analysis was used to estimate the impact of the COVID-19 pandemic. The signature of the pandemic was different between the pollutants as well as from site to site. The model was able to distinguish small impacts from local pollutants at the residential site;short-lived acute impacts from industrial changes;and longer-term changes due to the early pandemic response in China. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Aeorosols Chemical speciation Lockdown effect Weekend effect generalized additive model Particle trajectories

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A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年

作者： Chen, Tao Gao, Ciwei Hui, Hongxun Cui, Qiushi Long, Huan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China Arizona State Univ Dept Elect & Comp Engn Tempe AZ 85287 USA

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

关键词： Lithium-ion battery battery capacity prediction generalized additive model data-driven solution energy storage system

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