A method includes, using at least one processing device, obtaining position measurements and/or tilt angle measurements associated with a tissue web and identifying a stretch measurement associated with the tissue web using the obtained measurements. Identifying the stretch measurement could include using one or more mathematical formulas to calculate the stretch measurement associated with the tissue web using the obtained measurements. The one or more mathematical formulas could be defined using laboratory stretch values of multiple training webs. Different mathematical formulas can be associated with training webs having different characteristics, and the method may further include selecting at least one of the mathematical formulas based on one or more characteristics of the tissue web.

A defibrating unit that defibrates a raw material including fibers in an atmosphere, an accumulating unit that discharges defibrated matter by rotating a drum unit in which a plurality of openings are formed, a second web forming unit that forms a second web by operating a mesh belt on which the defibrated matter is accumulated, a sheet forming unit that forms a sheet from the second web, a cutting unit that cuts the sheet into a preset size, and a control unit that executes a stop control with a cut operation of the cutting unit as a trigger in a case where an instruction to stop an apparatus is provided are included. In the stop control, the control unit stops operation of the defibrating unit after stopping rotation of the drum unit and movement of the mesh belt.

A fiber body forming apparatus including an accumulating unit that has a release unit that releases a material containing a fiber and that has an accumulating member on which the material released from the discharging unit accumulates, a sheet substrate supply unit that supplies a sheet substrate to a position vertically below the release unit, and a control unit that controls operation of the accumulating unit and the sheet substrate supply unit, in which the control unit controls the operation of the accumulating unit and the sheet substrate supply unit to selectively execute a first mode for causing the material to accumulate on the accumulating member and the second mode for supplying the sheet substrate to the position vertically below the release unit and causing the material to accumulate on the sheet substrate.

A disruptor adjustment system comprising: a disruptor assembly configured to disrupt a volume of smelt flowing from a smelt spout into a dissolving tank, wherein the disruptor assembly comprises an actuator operatively engaged to a disruptor, a sensor configured to record process data from the recovery boiler; and a control system configured to receive a sensor output signal from the sensor, wherein the sensor output signal indicates the process data at a measured time, wherein the control system is further configured to compare the sensor output signal to a programmed operation range, and to send a disruptor input signal to the disruptor assembly to adjust a disruptor operating condition if the process data of the sensor output signal is outside of the programmed operation range. In certain exemplary embodiments, the sensor is an image capture device. In certain exemplary embodiments, the disruptor assembly can be adjusted remotely.

A system for optimizing machine performance includes an optimization system and a data processing system. The data processing system records readings from data sources monitoring the performance of a machine and related aspects. The readings are processed at the optimization system and a machine learning model is trained to differentiate between data corresponding to an interval of optimal runtime performance and data corresponding to an interval of pre-error performance. The trained model is used to identify a subset of the various data sources that are most-predictive for a particular performance state of the machine, such as a pre-error performance state or an optimal runtime performance state. The subset of the various data sources are provided to a multivariate model for generating set-point recommendations for each of the most-predictive data sources. Visualizations are generated for providing real-time monitoring of deviations of the most-predictive data sources from the set-points associated therewith.

A method for monitoring an industrial plant. In a first part of the industrial plant, first parameters are provided. The industrial plant is used to produce and/or process a fibrous material web. Second parameters are provided in a second part. The parameters are stored, preferentially as time series. In the case of a web break in the second part, the second parameters are first analyzed for a second anomaly. If no second anomaly can be detected, the first parameters are analyzed for a first anomaly. During the analysis, the parameters which were stored in a time range before the web break are preferably examined. If a first or second anomaly is detected, these, and optionally measures to avoid such web breaks, are displayed to the user. Optionally, the first parameters and/or the second parameters can be set so as to avoid future web breaks.

A fiber body forming apparatus including an accumulating unit that has a release unit that releases a material containing a fiber and that has an accumulating member on which the material released from the discharging unit accumulates, a sheet substrate supply unit that supplies a sheet substrate to a position vertically below the release unit, and a control unit that controls operation of the accumulating unit and the sheet substrate supply unit, in which the control unit controls the operation of the accumulating unit and the sheet substrate supply unit to selectively execute a first mode for causing the material to accumulate on the accumulating member and the second mode for supplying the sheet substrate to the position vertically below the release unit and causing the material to accumulate on the sheet substrate.

A transporting apparatus includes a pressurizing roller that transports a web-like or sheet-like transport target object and a heating roller disposed downstream of the pressurizing roller in a transport path, a first bottom sensor and a first top sensor that are disposed between the pressurizing roller and the heating roller, a measuring section that measures a time from when the transport target object is detected by the first bottom sensor until the transport target object is detected by the first top sensor, and a rotation control section that modifies a rotation speed of the heating roller when a time measured by the measuring section is shorter than a first reference time.

An accumulating unit that discharges fibers by rotating a drum unit in which a plurality of openings are formed, a second web forming unit that forms a second web by operating a mesh belt, a sheet forming unit that forms a sheet from the second web, and a control unit that performs a start control for operating the accumulating unit and the second web forming unit from the stop state are included. In a case where the start control is performed from a state where the fibers are present in the drum unit, the control unit adjusts a thickness of the second web by controlling at least one of a timing at which rotation of the drum unit is initiated, a rotational speed of the drum unit, a timing at which movement of the mesh belt is initiated, and a movement speed of the mesh belt.

A system for optimizing machine performance includes an optimization system and a data processing system. The data processing system records readings from data sources monitoring the performance of a machine and related aspects. The readings are processed at the optimization system and a machine learning model is trained to differentiate between data corresponding to an interval of optimal runtime performance and data corresponding to an interval of pre-error performance. The trained model is used to identify a subset of the various data sources that are most-predictive for a particular performance state of the machine, such as a pre-error performance state or an optimal runtime performance state. The subset of the various data sources are provided to a multivariate model for generating set-point recommendations for each of the most-predictive data sources. Visualizations are generated for providing real-time monitoring of deviations of the most-predictive data sources from the set-points associated therewith.