Abstract: 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.

Abstract: Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings.

Abstract: A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.

Abstract: Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings.

Abstract: 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.

Abstract: A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.

Abstract: A method for advanced batch control of a batch process. The method discloses completing at least one cycle of the batch process and collecting data on at least one process variable and at least one product property. A model is created based on the data, wherein the model comprises inputs. Thereafter the batch process is initiated. At one or more decision points the model is utilized to obtain outputs. A controller utilizes the model outputs to control the batch in accordance with the model outputs. Final product properties are reached at decreased variability compared to the prior art.

Abstract: A method for advanced batch control of a batch process. The method discloses completing at least one cycle of the batch process and collecting data on at least one process variable and at least one product property. A model is created based on the data, wherein the model comprises inputs. Thereafter the batch process is initiated. At one or more decision points the model is utilized to obtain outputs. A controller utilizes the model outputs to control the batch in accordance with the model outputs. Final product properties are reached at decreased variability compared to the prior art.

Abstract: A method for extracting feature information from product images using multivariate image analysis (MIA) to develop predictive models for organoleptic and other feature content and distribution on the imaged product. The imaging system is used to monitor product quality variables in an on-line manufacturing environment. The method may also be integrated into a closed-loop feedback control system in automated systems.

Abstract: A computer implemented method for modeling and controlling batch or transitional processes is disclosed including collecting, or initiating the collection of measurements on a plurality of process variables. The method may include creating, or initiating the creation of, a latent variable model predictive controller based on the collected measurements. The method further provides for applying or initiating the application of, the model predictive controller to predict and control at least one of the process variables to track a desired trajectory, by operation of at least one computer including one or more computer processors. A related system for implementing the method is disclosed as is a computer program operable with this method.