The present disclosure relates to a method for optimizing a measurement rate of a field device in a measurement system. The measurement system includes at least one second field device in which a measurement variable of the field device is correlated with the measurement variable of the second field device. The method determines a respective specific correlation pattern between the first measurement variable and the second measurement variable based on a learning phase. This makes it possible to check the measured values from the second field device for the correlation pattern during normal measurement operation and to change the measurement rate of the field device during the corresponding time window. This makes it possible to increase the service life and/or availability in the process installation.

A method for the early estimation of anaerobic degradability of organic substrates, starting from initial data acquired from tests measuring BMP (Biochemical Methane Potential). The method consists of: i) calculating the two parameters B.sub.0 and k; ii) comparing the fit of the decreasing trend of B.sub.0,est as Δt varies with a homographic function in the first quadrant; iii) evaluating the goodness of fit between a homographic function and the trend of B.sub.0,est as Δt varies, checking whether the adjusted coefficient of determination R.sup.2.sub.adj≥R.sup.2.sub.adj,min; iv) selecting the value of B.sub.0,est corresponding to a slope of less than 0.1% that occurs for three consecutive Δt; if no, acquire additional BMP measurements and repeat the previous steps.

A method for the early estimation of anaerobic degradability of organic substrates, starting from initial data acquired from tests measuring BMP (Biochemical Methane Potential). The method consists of: i) calculating the two parameters B.sub.0 and k; ii) comparing the fit of the decreasing trend of B.sub.0,est as Δt varies with a homographic function in the first quadrant; iii) evaluating the goodness of fit between a homographic function and the trend of B.sub.0,est as Δt varies, checking whether the adjusted coefficient of determination R.sup.2.sub.adj≥R.sup.2.sub.adj,min; iv) selecting the value of B.sub.0,est corresponding to a slope of less than 0.1% that occurs for three consecutive Δt; if no, acquire additional BMP measurements and repeat the previous steps.

The present invention relates to a method for characterising, by means of an electronic nose, the release kinetics of odorous compounds from a sample, comprising the following series of steps: (a) supplying a sample; (b) at a time t1, exposing the sensor array of the electronic nose to some of the gaseous medium comprising the odorous compounds released from the sample, and processing the response emitted by the sensor array of the electronic nose, after said exposure, in the form of a signal; and (c) repeating step (b) at least once, at a time t2 different from the time t1, whereby an olfactory kinetic signature characterising the sample is obtained. The present invention also relates to the use of this method for anti-counterfeiting and/or quality control purposes and for generating a data bank or database of temporal olfactory signatures. The present invention finally relates to certain devices used when implementing such methods.

The present invention relates to a method for characterising, by means of an electronic nose, the release kinetics of odorous compounds from a sample, comprising the following series of steps: (a) supplying a sample; (b) at a time t1, exposing the sensor array of the electronic nose to some of the gaseous medium comprising the odorous compounds released from the sample, and processing the response emitted by the sensor array of the electronic nose, after said exposure, in the form of a signal; and (c) repeating step (b) at least once, at a time t2 different from the time t1, whereby an olfactory kinetic signature characterising the sample is obtained. The present invention also relates to the use of this method for anti-counterfeiting and/or quality control purposes and for generating a data bank or database of temporal olfactory signatures. The present invention finally relates to certain devices used when implementing such methods.

Techniques are disclosed for using a genetic algorithm to identify a processing pipeline that transforms spectra into a form usable to generate predicted characteristics of corresponding samples. The genetic algorithm is used to generate and evaluate multiple candidate solutions specifying various pre-processing and machine-learning-processing configurations. The processing pipeline is defined based on the candidate solutions.

Techniques are disclosed for using a genetic algorithm to identify a processing pipeline that transforms spectra into a form usable to generate predicted characteristics of corresponding samples. The genetic algorithm is used to generate and evaluate multiple candidate solutions specifying various pre-processing and machine-learning-processing configurations. The processing pipeline is defined based on the candidate solutions.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

Embodiments relate to a system for predicting thermodynamic phase of a material. The system includes a phase diagram image scanning processing module configured to scan a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA). The system includes a feature computation processing module configured to generate a primary feature and an adaptive feature. The primary feature is representative of a probability that the HEA will exhibit a solid solution phase and/or an intermetallic phase. The adaptive feature is representative of a factor favoring formation of a desired intermetallic HEA phase. The system includes a prediction module configured to encode the primary feature and/or the adaptive feature with thermodynamic data associated with formation of HEA alloy phases to provide an output representation of the HEA alloy phases for a material under analysis.