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1. 发明授权

US10755200B2 Automated control of circumferential variability of blast furnace 有权
公开(公告)号：US10755200B2
公开(公告)日：2020-08-25
申请号：US15712255
申请日：2017-09-22
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： G06N20/00 , C21B7/00 , G05B19/4155 , G06N5/02 , C21B5/00 , F27D19/00 , C21B7/24 , F27B1/26
摘要： Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

2. 发明申请

US20190093187A1 MANUFACTURING PROCESS CONTROL WITH DEEP LEARNING-BASED PREDICTIVE MODEL FOR HOT METAL TEMPERATURE OF BLAST FURNACE 审中-公开
公开(公告)号：US20190093187A1
公开(公告)日：2019-03-28
申请号：US15813653
申请日：2017-11-15
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： C21B7/06 , G01K17/20 , G01K3/04 , G01B21/08 , G01K7/04 , G01N25/18 , G01N25/72
摘要： A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

3. 发明申请

US20190093186A1 MANUFACTURING PROCESS CONTROL WITH DEEP LEARNING-BASED PREDICTIVE MODEL FOR HOT METAL TEMPERATURE OF BLAST FURNACE 审中-公开
公开(公告)号：US20190093186A1
公开(公告)日：2019-03-28
申请号：US15716794
申请日：2017-09-27
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： C21B7/06 , G01B21/08 , G01K17/20 , G01K3/04 , G01K7/04
摘要： A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

4. 发明申请

US20140365180A1 OPTIMAL SELECTION OF BUILDING COMPONENTS USING SEQUENTIAL DESIGN VIA STATISTICAL BASED SURROGATE MODELS 审中-公开
标题翻译：使用基于统计学的现象模型使用顺序设计的最佳选择建筑组件
公开(公告)号：US20140365180A1
公开(公告)日：2014-12-11
申请号：US13910251
申请日：2013-06-05
申请人： Carnegie Mellon University , International Business Machines Corporation
发明人： Khee Poh Lam , Young Min Lee , Fei Liu , Jane L. Snowdon , Jeaha Yang , Rui Zhang
IPC分类号： G06F17/50
CPC分类号： G06F17/5004
摘要： A surrogate model to a building simulation model is built and used for finding a combination of building components that optimize energy use in a building. The surrogate model may be built iteratively using design points comprising a different combination of building product properties that maximize a predefined expected improvement function.
摘要翻译：建立了建筑模拟模型的替代模型，用于寻找优化建筑物能源使用的建筑构件的组合。可以使用包括建立产品特性的不同组合的设计点迭代地构建替代模型，以使预定义的预期改进功能最大化。

5. 发明申请

US20130124251A1 OPTIMAL PLANNING OF BUILDING RETROFIT FOR A PORTFOLIO OF BUILDINGS 审中-公开
标题翻译：建筑改造建筑改造的最佳规划
公开(公告)号：US20130124251A1
公开(公告)日：2013-05-16
申请号：US13732745
申请日：2013-01-02
申请人： International Business Machines Corporation
发明人： Young Min Lee , Chandrasekhara K. Reddy
IPC分类号： G06Q10/06
CPC分类号： G06Q10/06313 , G06Q10/04 , G06Q50/06 , G06Q50/16
摘要： Generating an optimal planning of building retrofit for a portfolio of buildings may include providing a plurality of objective functions that may be selected for maximizing cost reduction, maximizing green house gas emission reduction, or maximizing energy reduction, or combinations thereof. The objective function may be solved based on information including at least a retrofit cost for retrofitting a building, payback period specifying the length of time needed to recover the retrofit cost, a budget available for retrofitting the building, expected price of energy, estimated energy savings from retrofitting and estimated green house gas emission from retrofitting. The planning of building retrofit may be generated based on the solutions of one or more of the objective functions, which may provide for an optimal plan of building retrofit.
摘要翻译：为建筑物组合生成建筑物改造的最佳规划可以包括提供可以选择用于最大化成本降低，最大化温室气体排放减少或最大化能量减少或其组合的多个目标函数。目标功能可以基于至少包括改造建筑物的改造成本的信息来解决，回收期限指定恢复改造成本所需的时间长度，可用于改造建筑物的预算，能源的预期价格，估计的能量节约从改造和估计温室气体排放改造。可以基于一个或多个目标函数的解决方案来生成建筑物改造的规划，其可以提供建筑改造的最佳计划。

6. 发明授权

US11242575B2 Manufacturing process control with deep learning-based predictive model for hot metal temperature of blast furnace 有权
公开(公告)号：US11242575B2
公开(公告)日：2022-02-08
申请号：US16787670
申请日：2020-02-11
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： C21B7/06 , G01B21/08 , G01K17/20 , G01K7/04 , G01K3/04 , G01K7/42 , G06N20/00 , C21B5/00 , G06N3/00 , G01N25/72 , G01N25/18
摘要： A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

7. 发明申请

US20200172989A1 MANUFACTURING PROCESS CONTROL WITH DEEP LEARNING-BASED PREDICTIVE MODEL FOR HOT METAL TEMPERATURE OF BLAST FURNACE 审中-公开
公开(公告)号：US20200172989A1
公开(公告)日：2020-06-04
申请号：US16787670
申请日：2020-02-11
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： C21B7/06 , G01K3/04 , G01K7/04 , G01K17/20 , G01B21/08 , G06N3/00 , C21B5/00 , G06N20/00 , G01K7/42
摘要： A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

8. 发明申请

US20150006123A1 CONSTRUCTING AND CALIBRATING ENTHALPY BASED PREDICTIVE MODEL FOR BUILDING ENERGY CONSUMPTION 有权
公开(公告)号：US20150006123A1
公开(公告)日：2015-01-01
申请号：US13930536
申请日：2013-06-28
申请人： International Business Machines Corporation
发明人： Lianjun An , Young T. Chae , Young Min Lee , Fei Liu , Rui Zhang
IPC分类号： G06N7/00
CPC分类号： G06Q50/06 , G06Q10/04
摘要： A predictive model for building energy consumption may be constructed and run to predict energy consumption in a building or to detect anomaly in energy consumption in a building or combinations thereof. Historic energy consumption data associated with energy consumed in a building may be received. Enthalpy of air outside the building may be determined. An energy consumption model may be calibrated based on the historic energy consumption data and the enthalpy of air outside the building. The energy consumption model incorporates enthalpy difference between a balance enthalpy and the enthalpy of air outside the building.

9. 发明授权

US10713595B2 Method for automated control of circumferential variability of blast furnace 有权
公开(公告)号：US10713595B2
公开(公告)日：2020-07-14
申请号：US15811759
申请日：2017-11-14
申请人： International Business Machines Corporation
发明人： Young Min Lee , Kyong Min Yeo
IPC分类号： G06N20/00 , C21B7/00 , G05B19/4155 , G06N5/02 , C21B5/00 , F27D19/00 , C21B7/24 , F27B1/26
摘要： Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

10. 发明申请

US20190265685A1 ORCHESTRATION OF LEARNING AND EXECUTION OF MODEL PREDICTIVE CONTROL TOOL FOR MANUFACTURING PROCESSES 审中-公开
公开(公告)号：US20190265685A1
公开(公告)日：2019-08-29
申请号：US16410044
申请日：2019-05-13
申请人： International Business Machines Corporation
发明人： Young Min Lee , Edward Pring , Kyong Min Yeo , Nam H Nguyen , Jayant R. Kalagnanam , Christian Makaya , Hui Qi , Dhaval Patel
IPC分类号： G05B19/418 , G06N20/00 , G06N5/04
摘要： Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

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