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公开(公告)号：US20240160166A1
公开(公告)日：2024-05-16
申请号：US18505638
申请日：2023-11-09
发明人： Kerry Clayton Ridley , Michael R. Keenan , Qingsheng Quinn Zheng , Yizhou Fang , Samantha LaCombe
IPC分类号： G05B13/04
CPC分类号： G05B13/048
摘要： Embodiments generate real-time industrial process guidance. One such embodiment receives, in memory of a processor, an operator question relating to a user-specified process variable of a model predictive control (MPC) controller of an industrial process. Next, a real-time simulation is performed of operational scenario(s) of the industrial process using a steady-state optimization problem of the MPC controller to determine operational characteristics of the industrial process in each of the operational scenario(s). Performing the real-time simulation includes, for each operational scenario, modifying a constraint variable of the steady-state optimization problem and, using the modified constraint variable, determining an updated value of the user-specified process variable. The determined operational characteristics of the industrial process include the determined updated value of the user-specified process variable. In turn, based on the determined operational characteristics of the industrial process, a recommendation is generated and output responsive to the operator question.
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公开(公告)号：US20240004355A1
公开(公告)日：2024-01-04
申请号：US17809640
申请日：2022-06-29
CPC分类号： G05B13/048 , G05B13/0265 , G05B23/0221
摘要： Embodiments control and optimize batch processes. An embodiment obtains and standardizes historical operating data from a plurality of batch production runs of an industrial process. For each batch production run, the standardized operating data corresponding to the batch is partitioned into one or more stages and one or more signature for each stage is determined using the partitioned standardized data. Each determined signature is associated with a class label based upon whether output of a batch run corresponding to the signature conforms or does not conform with operational standards. A model is trained, with at least a subset of the signatures as inputs and associated class labels as outputs, to predict, based on operating data from a real-world batch process, whether output of the process will conform or not conform with the operational standards. Online predictions can be automatically or manually applied to control and optimize a batch production run.
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公开(公告)号：US11663546B2
公开(公告)日：2023-05-30
申请号：US16855668
申请日：2020-04-22
IPC分类号： G06Q10/06 , G06Q10/0637 , G06N20/00 , G06N5/04 , G06Q10/0631 , G06Q10/067 , G06Q30/0201 , G06Q50/06
CPC分类号： G06Q10/06375 , G06N5/04 , G06N20/00 , G06Q10/067 , G06Q10/06315 , G06Q30/0206 , G06Q50/06
摘要： Computer tool determines target feedstock for a refinery, process complex, or plant. The tool receives a dataset of market conditions and preprocesses the data based on properties of the plant. Using the preprocessed data and machine learning, the tool trains predictive models. Each predictive model calculates a breakeven value of a candidate feedstock for the given plant under an individual market condition. Different predictive models optimize for different market conditions. A trained predictive model is selected based on a current market condition. The tool applies the selected predictive model and determines whether a candidate feedstock is a target feedstock for the refinery under the current market condition.
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公开(公告)号：US20230376012A1
公开(公告)日：2023-11-23
申请号：US17746548
申请日：2022-05-17
发明人： Jiangsheng You , Mikhail Noskov
IPC分类号： G05B19/418
CPC分类号： G05B19/4184 , G05B19/4183 , G05B19/41835
摘要： Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.
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公开(公告)号：US11774924B2
公开(公告)日：2023-10-03
申请号：US17111374
申请日：2020-12-03
发明人： Nihar Sahay , Dimitrios Varvarezos
CPC分类号： G05B13/042 , G05B13/0265
摘要： A computer-implemented method and system for process schedule reconciliation receives a scheduling model and an initial schedule for reconciliation, where the initial schedule includes projected plant data. Current plant data is imported into the system. The current plant data and projected plant data is processed using mathematical modeling techniques to identify event boundaries, stream flowrates associated with tanks and process units. The system builds an optimization model applying identified event boundaries, stream flowrates and pre-determined constraints along a period of time that includes priority slots to reconcile the projected plant data of the initial schedule with the current plant data, and then solves the optimization model to develop a reconciled schedule.
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公开(公告)号：US11614733B2
公开(公告)日：2023-03-28
申请号：US15967099
申请日：2018-04-30
IPC分类号： G05B23/02 , G05B13/04 , G05B19/418 , G06K9/62
摘要： Embodiments include a computer-implemented method (and system) for performing automated batch data alignment for modeling, monitoring, and control of an industrial batch process. The method (and system) loads, scales, and screens plant historian batch data for an industrial batch process. The method (and system) selects a reference batch as basis of the batch alignment, defines and adds or modifies one or more batch phases, and selects one or more batch variables based on one or more profiles and corresponding curvatures of the batch data. The method (and system) estimates one or more weightings, adjust one or more tuning parameters and uses a sliding time window combined with DTW, DTI and GSS algorithms, performs the batch alignment in offline mode or online mode.
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公开(公告)号：US11934159B2
公开(公告)日：2024-03-19
申请号：US16174641
申请日：2018-10-30
IPC分类号： G05B13/04
CPC分类号： G05B13/048
摘要： A controller has improved closed-loop step testing of a dynamic process of an industrial processing plant. The controller performs economic optimization relaxation on process variables, such that operating range of the variables (MVs and CVs) during the testing are not skewed by variations in optimization cost factors. The controller employs computer-implemented methods and systems that receive a user-defined giveaway tolerance representing an allowable range between a current process variable value and a target process variable value. In response to the variables not meeting the giveaway tolerance, embodiments adjust the MPC controller configuration to drive the variables inside the tolerance, while relaxing optimization of the variables already meeting the giveaway tolerance. Using the adjusted configuration, embodiments calculate a new set of targets and generate a dynamic move plan from the new target. Embodiments add perturbation signals for the testing to the move plan in accordance with the adjusted configuration.
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公开(公告)号：US11853032B2
公开(公告)日：2023-12-26
申请号：US16868183
申请日：2020-05-06
IPC分类号： G05B19/4155 , G06N20/00 , B01J19/00 , G06N5/04
CPC分类号： G05B19/4155 , B01J19/0033 , G06N5/04 , G06N20/00 , B01J2219/00243 , G05B2219/32287
摘要： Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.
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公开(公告)号：US11630446B2
公开(公告)日：2023-04-18
申请号：US17176882
申请日：2021-02-16
IPC分类号： G05B19/418 , G06N20/00
摘要： Computer implemented methods and systems generate an improved predicted model of an industrial process or process engineering system. The model is a function of measurable features of the subject process and selected first principle features. First principle features are selected that capture linearities in a residual of a linear model constructed using a received dataset of the subject process. The model can further be a function of a scaled spline. The scaled spline is generated by computing a spine for a measurable feature of the subject process, fitting the computer spline to the residual of the constructed linear model, and scaling the fitting spline with a scaling factor. The model results in improved predictions of behavior of the subject process by relying primarily on the data of the measurable features of the subject process.
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10.发明公开公开(公告)号：US20240241511A1
公开(公告)日：2024-07-18
申请号：US18411421
申请日：2024-01-12
发明人： Ajay Krishnalal Modi , Héctor Luis Borras , John Carl Campbell , Dorian F. Snyder , Brian Dias Barros , Patrick Daniel Aguiar Simoes
IPC分类号： G05B23/02
CPC分类号： G05B23/0283 , G05B23/0216
摘要： Computer system and method builds and deploys a custom industrial (chemical) processing plant asset failure prediction engine that integrates disparate calculation methods and information (data) sources. The diverse calculation methods improve quality and accuracy of the asset failure predictions by embedding domain knowledge and providing a holistic assessment of plant assets.
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