Systems, methods, and machine-executable coded instruction sets are disclosed for fully- and/or partly automated handling of goods. The disclosure provides improvements in the storage and retrieval of storage and delivery containers in order processing systems.

The present disclosure generally relates to systems and methods utilizing regenerative agriculture for the procurement, production, refinement and/or transformation of low carbon intensity transportation fuels, including low carbon intensity biodiesel and/or renewable diesel, low carbon intensity biogasoline, low carbon intensity aviation, marine and kerosene fuels as well as fuel oil blends, low carbon intensity ethanol, and low carbon intensity hydrogen, that may be beneficially commercialized directly to consumers. In further aspects, the systems and methods of the present disclosure advantageously generate low carbon intensity comestibles, including sustainably-sourced meal and/or feed. The disclosed systems and methods may be utilized and optimized such that the resulting fuels and foodstuffs are characterized by a reduction in greenhouse gas production and a diminution in the fertilizer, pesticide and water required for producing the associated crop feedstocks.

A manufacturing system including an automated storage and retrieval system (ASRS) structure with a three-dimensional array of storage locations distributed throughout a two-dimensional footprint of the ASRS structure at multiple storage levels; workpieces stored within the storage locations of the ASRS structure; robotic storage/retrieval vehicles (RSRVs) navigable within the ASRS structure in three dimensions to access the storage locations, and multiple manufacturing cells positioned outside the ASRS structure, is provided. The manufacturing system includes a track structure attached to the ASRS structure and defining one or more travel paths traversable by the RSRVs from the ASRS structure. The same fleet of RSRVs that is navigable within the ASRS structure is operable to deliver the workpieces to the manufacturing cells. One or more of the manufacturing cells are positioned along the track structure, thereby receiving convenient access to the workpieces along with associated tool pieces and workpiece supports for manufacturing goods.

A processing line includes a conveyor for conveying product from upstream processing equipment to downstream processing equipment. Data structures stored in a memory of a controller comprise a backlog set point for a product type to be processed. A product sensor for the conveyor is enabled to generate signals representative of a number of the products moving on the conveyor from the upstream processing equipment for delivery to the downstream processing equipment. A conveyor speed sensor for the conveyor is enabled to generate signals representative of a speed of the conveyor. A backlog measurement based upon the product sensor signals and the conveyor speed sensor signals is determined. The backlog measurement is compared to the backlog set point to determine a difference in backlog. The controller is enabled to generate signals for controlling the processing line based upon the difference in backlog and additional information related to the product type.

A controller includes a simulator configured to simulate a final model for converting at least one of 1-1.sup.st processors and 1-2.sup.nd processors into a down state, in a virtual space that is a virtual space, so that a filling rate, which is a ratio of a used capacity of the storage to a total capacity of the storage, is equal to or less than a first value, and a process controller configured to control a first process line according to the final model.

A neural network-based error compensation method for mass transfer of mini-light-emitting diode (Mini-LED) chips includes the following steps. (S1) An automated optical re-inspection result is obtained as a first result. (S2) The first result is sorted and normalized to obtain a second result. (S3) Nearest-neighbor interpolation is performed on a Mini-LED chip with a transfer status identifier being abnormal, and a differential of path variables of each Mini-LED chip is calculated. (S4) A multi-layer neural network model is defined. A loss function is constructed. A weight of the multi-layer neural network model is updated with the loss function, until no overfitting is observed. (S5) A chip transfer path is generated and input into the multi-layer neural network model to obtain a predicted transfer error value, and mass transfer of the Mini-LED chip is performed based on the predicted transfer error value.

A method for operating a conveyor system, in which assembly line trays, which each serve to hold a component, can be conveyed (moved) along a conveyor line by stationary conveyor elements, said method comprising: recording a respective conveying moment for the respective conveyor while conveying the respective assembly line trays; recording at least one respective parameter value by a sensor device for the respective conveyor element at the respective conveying moment; forming a respective data telegram for the respective conveyor element at the respective conveying moment by a respective control device of the respective conveyor element: compiling the data telegrams into a dataset by a central electronic computing device; evaluating the dataset by an anomaly model; if the evaluation reveals that an anomaly of at least one of the parameter values is above a threshold value, issuing a service instruction.

A linear motor system includes a track and at least one movable member coupled to the track and configured to move along the track. The at least one movable member includes a first element, a second element relatively movable with respect to the first element, and at least one movement detector configured to transmit a movement signal. A processing unit is configured to calculate a movement of the movable member, the second element and/or the first element as a function of the movement signal received from the movement detector.

There is provided a configuration that includes: at least one transfer mechanism configured to transfer a substrate and at least one processing mechanism configured to process the substrate; an earthquake detector configured to detect an earthquake; and a controller configured to control the at least one transfer mechanism and the at least one processing mechanism according to a detection result of the earthquake detector, wherein the controller is configured to be capable of performing a stopping operation of the at least one transfer mechanism according to a P wave (initial tremor wave) and an S wave (principal fluctuation wave).

Systems and methods to provide low carbon intensity (CI) ethanol through one or more targeted reductions of carbon emissions based upon an analysis of carbon emissions associated with a combination of various options for feedstock procurement, feedstock refining, processing, or transformation, and ethanol distribution pathways to end users. Such options are selected to maintain the total CI (carbon emissions per unit energy) of the ethanol below a pre-selected threshold that defines an upper limit of CI for the ethanol.