Abstract: A computer-implemented method is provided.

Abstract: A method of material flow optimization in an industrial process by using an integrated optimizing system is described. The integrated optimizing system includes: a high-level optimizer module describing the material flow by coarse high-level process parameters and including an optimization program for the high-level process parameters, the optimization program being dependent on high-level model parameters and including an objective function subject to constraints; a low-level simulation module for simulating the material flow, the low-level simulation module including a low-level simulation function adapted for obtaining detailed low-level material flow data based on the high-level process parameters; and an aggregator module including an aggregator function adapted for calculating the high-level model parameters based on the low-level material flow data.

Abstract: A method of industrial processing of a bulk material, the industrial processing including a plurality of process steps, the method including defining a material portion of the bulk material; generating a material portion identifier associated with the material portion processing the material portion in at least two process steps of the plurality of process steps the method including for each process step of the at least two process steps: determining a cost of processing the material portion in the process step; and generating a history data set, wherein the history data set is indicative of the cost, the process step and the material portion identifier and wherein the method further includes determining an aggregated cost based on the history data sets.

Abstract: A method for monitoring a continuous industrial process is described. The industrial process includes a number of processing stations for processing material and a material flow between the number of processing stations. Each processing station dynamically provides data representing a state of the processing station. The method includes providing, for each processing station, a processing station layout of the processing station. The method further includes providing, for each processing station, an interface model of the processing station. The method further includes generating an information metamodel from the processing station layout and the interface model of the number of processing stations. The method further includes generating an adaptive simulation model of the industrial process by importing the data representing the state of the processing station provided by the number of processing stations into the adaptive simulation model via the information metamodel.

Abstract: A computer-implemented method of generating a training data set for training an artificial intelligence module includes providing first and second data sets, the first data set including first data elements indicative of a first operational condition, the second data set including second data elements indicative of a second operational condition that matches the first operational condition. The method further comprises determining a data transformation for transforming the first data elements into the second data elements; applying the data transformation to the first data elements and/or to further data elements of further data sets, thereby generating a transformed data set; and generating a training data set for training the AI module based on at least a part of the transformed data set.

Abstract: A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

Abstract: A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

Abstract: An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

Abstract: A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

Abstract: A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

Abstract: An asset condition monitoring method with automatic anomaly detection may include receiving local condition data from an asset fleet, identifying at least one anomaly in the received condition data, identifying a new potential failure case dependent on the identified anomaly, determining a specific condition model dependent on the identified new potential failure case, where the specific condition model is configured for predicting the new potential failure case, and providing the specific condition model to the plurality of assets and/or to digital models of the plurality of assets.

Abstract: A method for generating a dynamic model of an industrial plant having: a plurality of physical processes that are dependent such that an outcome of at least one first process is fed into at least one second process; a plurality of low-level controllers, each controller acting upon at least one physical process such that at least one process variable of the at least one physical process is controlled to match a set-point of the low-level controller; and a plurality of sensors, each sensor measuring at least one process variable of one of the physical processes, and/or of the plant as a whole, the set-points of the low-level controllers and current values of the process variables measured by the sensors being the inputs of the model, and predicted future values of the process variables that are likely to result from applying the set-points to the low-level controllers being the outputs.

Abstract: A production planning system and a system for energy supply planning are configured to determine a production schedule and an energy supply schedule, respectively, by interactively and mutually exchanging scheduling information with the respective other system and by taking the received scheduling information into account during the planning of the respective schedule. The interacting is performed in a kind of handshaking during planning of the respective schedules, where each of the two systems waits with the next iteration step until the other system delivers its updated schedule.