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.

The present disclosure describes systems, methods, and computer readable media that provide a hybrid approach that uses machine learning techniques and phenomenological reactor models for optimization of steam cracker units. While the phenomenological model allows capturing the physics of a steam cracker using molecular kinetics, the machine learning methods fill the gap between the phenomenological models and more detailed radical kinetics based steam cracker models. Also, machine learning based models can capture actual plant information and provide insight into the variation between the models and plant running conditions. The proposed methodology shows better interpolation and extrapolation capabilities as compared to stand-alone machine learning methods. Also, compared to detailed radical kinetics based models, the approach utilized in embodiments requires much less computational time in order to carry out whole plant-wide optimization or can be used for planning/scheduling purposes.

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value: a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale: at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information: (d) determining, from the simulation, one or more source trajectories for the process parameters), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajector

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system can comprise a plurality of distinct data-handling layers. The plurality of distinct data-handling layers can comprise an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that facilitates the coordinated development and deployment of intelligent systems in the IIoT system; and an industrial management application platform layer that includes a plurality of applications and that manages the platform in a common application environment. The adaptive intelligent systems layer can include a robotic process automation system that develops and deploys automation capabilities for one or more of the plurality of industrial entities in the IIoT system.

The present disclosure describes systems for self-organized, network-sensitive data collection in an industrial environment. A system may include an industrial system with a plurality of components operatively coupled to sensors, a sensor communication circuit to interpret the data values from the sensors, and a system collaboration circuit to communicate at least a portion of the data values over a network to a storage target computing device according to a sensor data transmission protocol. A transmission environment circuit may determine transmission feedback corresponding to the communication of the data values over the network, and a network management circuit to update the sensor data transmission protocol in response to the transmission feedback.

The present disclosure describes systems and methods for data collection in an industrial environment. A method can include providing a plurality of sensors to components of an industrial system, interpreting the data values from the sensors in response to a sensed parameter group, the group including a fused plurality of sensors. The method may also include determining a recognized pattern value comprising a secondary value determined in response to the data values, updating the sensed parameter group in response to the recognized pattern value, and adjusting the interpreting the data values in response to the updated sensed parameter group.

A fault diagnosis apparatus acquires, by using an acquisition unit, for each of a plurality of medical devices, installation environment information including a plurality of items about an installation environment in which each of the plurality of medical devices is installed; classifies, by using a classification unit, the plurality of medical devices into a plurality of groups on the basis of the installation environment information; extracts, by using an extraction unit, an item in the installation environment information representing a feature of a group to which a device in which a fault has occurred among the plurality of medical devices belongs, the feature being different from that of the other groups; and performs control, by using a display control unit, to cause a display unit to display an extraction result obtained by the extraction unit.

In embodiments of the present invention improved capabilities are described for a system for process monitoring through data collection in an industrial drilling environment comprising a data collector communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the industrial drilling environment; a data storage structured to store collected data from the plurality of input channels; a data acquisition circuit structured to interpret the plurality of process parameter values from the collected data; and a data analysis circuit structured to analyze the plurality of process parameter values to detect a process condition associated with the industrial drilling environment, wherein an operational process for the industrial drilling environment is altered based on the analysis of the plurality of process parameter values.

A biogas collection and purification system that includes a plurality of sources of biogas and a network of conduits configured to convey the biogas from the sources to a central processing facility for processing the biogas into methane. The central processing facility removes impurities to convert biogas to biomethane and may include an H.sub.2S removal stage; an activated carbon scrubber; a gas drier; and a carbon dioxide removal stage. The facility also has a biomethane gas compressor configured to deliver the biomethane for use in power plants, for CNG production. Ancillaries to the system include fuel cells for direct electricity generation from biogas/biomethane.

A system including biogas purification and provides biogas as feedstock to a solid oxide fuel cell. The biogas purification treatment process provides a polished biogas that is substantially free of carbonyl sulfides and hydrogen sulfide. The system uses a biogas treatment apparatus, that includes apparatus such as a packed columns, comprising copper oxide or potassium permanganate packing material, and an activated carbon component configured to treat the biogas by polishing it to remove carbonyl sulfides and deleterious trace residues, such as hydrogen sulfide, that were not removed by any prior bulk H2S removal steps. In addition, an oil removal device is used to remove any entrained fine oil droplets in the biogas. A polished biogas having in the range of 60% methane is charged to the fuel cell. Electricity generated may be fed into a grid or used directly as energy to charge electrical-powered vehicles, for example. Energy credits are tracked in real time and are appropriately assigned.

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