Abstract: A material feeding apparatus and method for providing a controlled feed of material. In particular, there is described a material feeding control apparatus and method comprising a feed rate measurement device capable of providing an online mass and/or volume flow measurement of material and a material sensing device capable of providing an online content (e.g. composition) measurement of the material. The material being fed may, for example, be any suitable bulk material including fuel for a combustion chamber (e.g. a furnace), mined materials such as iron ore and food in a food feeding system. A particular example of the present invention is the provision of a fuel feeding apparatus capable of providing substantially stable combustion in a combustion chamber (e.g. a furnace) which may be incorporated into a cement production line.

Abstract: A method, apparatus and computer program are provided for monitoring operation of a machine having a mechanical component. the method involves receiving sensor data comprising a time series of measurements of an operational parameter of the machine corresponding to a state variable and processing the time series of measurements for the state variable using a machine-learning model pre-trained to predict normal operational behaviour of the machine based on values of the state variable observed for a time period during normal operation of the machine. The standardized residual for the state variable is calculated across the time series based on a prediction of the pre-trained machine-learning model and any deviation from normal operation of the machine is identified based on values of the standardized residual.

Abstract: A method for monitoring a condition of a system or process includes acquiring sensor data from a plurality of sensors disposed within the system (S41 and S44). The acquired sensor data is streamed in real-time to a computer system (S42 and S44). A discriminative framework is applied to the streaming sensor data using the computer system (S43 and S45). The discriminative framework provides a probability value representing a probability that the sensor data is indicative of an anomaly within the system. The discriminative framework is an integration of a Kalman filter with a logistical function (S41).

Abstract: A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).

Abstract: A thermal power plant, in particular to a steam-electric power plant or a combined cycle power plant (CCPP), and a method for operating a thermal power plant is adapted to accelerate, or to technically and/or economically optimize the start-up of the thermal power plant, in particular to accelerate/optimize a cold-start phase of the thermal power station. The thermal power plant has an auxiliary energy store integrated into the power plant. The store, during the start-up of the thermal power plant, delivers energy for heating/pre-heating components and/or media of the thermal power plant, or supplies an electrical power distribution network.

Abstract: Detection for localizing at least one particle moving in a flow includes emitting a transmission signal by a transmitter, and receiving a reflected reception signal. The reflected reception signal is frequency and phase modulated in comparison with the transmission signal. The reflected reception signal is convolved with at least one kernel representative of a conjugate estimated channel pulse response. A reconstructed particle position function is formed, and the position of the particle is determined from the reconstructed particle position function.

Abstract: A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.

Abstract: A method for predicting failure modes in a machine includes learning (31) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning (32) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming (33) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming (34) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

Abstract: The present embodiments relate to a device and a method for determining at least one concentration of coal particles in a gas flow flowing through a channel, wherein at least part of the gas flow having coal particles contained therein is measured by at least one microwave sensor and at least one measurement signal characterizing the concentration of the coal particles is provided, wherein an autocorrelation function of the measurement signal is determined and at least one distance value characterizing a distance of a point of the gas flow belonging to the concentration from the microwave sensor is determined in dependence on the autocorrelation function.

Abstract: A sensor apparatus for analyzing a gas in a process chamber, having a housing, a gas sensor for analyzing at least a part of the gas, the gas sensor being arranged at a determined position in the housing, a gas feed for connecting the housing to the process chamber to feed the part of the gas from the process chamber into the housing and to the determined position, and a gas discharge for discharging the gas from the housing, wherein the gas feed and the gas discharge are configured as tubes lying inside one another, characterized by a closure cap at the combustion chamber-side end of the tubes lying inside one another, the closure cap including an even number of at least four openings with the same area, which are connected alternately as a gas inlet and a gas outlet to the tubes lying inside one another is provided.

Abstract: A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting (620) from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving (622) the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting (624, 626) a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.

Abstract: An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.

Abstract: A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).

Abstract: A method for predicting failure modes in a machine includes learning (31) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning (32) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming (33) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming (34) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.

Abstract: Detection for localizing at least one particle moving in a flow includes emitting a transmission signal by a transmitter, and receiving a reflected reception signal. The reflected reception signal is frequency and phase modulated in comparison with the transmission signal. The reflected reception signal is convolved with at least one kernel representative of a conjugate estimated channel pulse response. A reconstructed particle position function is formed, and the position of the particle is determined from the reconstructed particle position function.

Abstract: A thermal power plant, in particular to a steam-electric power plant or a combined cycle power plant (CCPP), and a method for operating a thermal power plant is adapted to accelerate, or to technically and/or economically optimize the start-up of the thermal power plant, in particular to accelerate/optimize a cold-start phase of the thermal power station. The thermal power plant has an auxiliary energy store integrated into the power plant. The store, during the start-up of the thermal power plant, delivers energy for heating/pre-heating components and/or media of the thermal power plant, or supplies an electrical power distribution network.

Abstract: The present embodiments relate to a device and a method for determining at least one concentration of coal particles in a gas flow flowing through a channel, wherein at least part of the gas flow having coal particles contained therein is measured by at least one microwave sensor and at least one measurement signal characterizing the concentration of the coal particles is provided, wherein an autocorrelation function of the measurement signal is determined and at least one distance value characterizing a distance of a point of the gas flow belonging to the concentration from the microwave sensor is determined in dependence on the autocorrelation function.

Abstract: Properties of coal are determined from samples processed by a near-infrared spectroscopy (NIR) device that generates wavelengths dependent spectra. Target values of the properties are associated with the NIR spectra by a kernel based regression model generated from training data based on an anisotropic kernel function that is extended by defining the kernel parameters as a smooth function over the wavelengths associated with a spectrum. Like the anisotropic case each wavelength related dimension has its own kernel parameter. Adjacent dimensions are restricted to have similar kernel parameters. Measured spectra with a limited number of features are reconstructed by applying a regression model based on training data of spectra having an extended number of features. Training data are pruned based on a regression model by removing outliers.

Abstract: A sensor apparatus for analyzing a gas in a process chamber, having a housing, a gas sensor for analyzing at least a part of the gas, the gas sensor being arranged at a determined position in the housing, a gas feed for connecting the housing to the process chamber to feed the part of the gas from the process chamber into the housing and to the determined position, and a gas discharge for discharging the gas from the housing, wherein the gas feed and the gas discharge are configured as tubes lying inside one another, characterized by a closure cap at the combustion chamber-side end of the tubes lying inside one another, the closure cap including an even number of at least four openings with the same area, which are connected alternately as a gas inlet and a gas outlet to the tubes lying inside one another is provided.

Abstract: An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.