The disclosed embodiments include systems and methods of manufacturing a product. The system may include a non-transitory computer readable medium comprising computer readable program code for performing the method. The method may include manufacturing batches of the product according to steps of a process flow, determining output data for each batch, sequencing the batches by output data, determining a plurality of modes of output data based on grouping the batches, identifying a detrimental factor to output data in a process flow step based on a correlation between the process flow step and a mode of the plurality of modes, and correcting the detrimental factor.

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

This disclosure provides an apparatus and method for interactive batch operations system enabling operation excellence and competency transition. The method includes collecting data related to a batch process from different departments within a batch processing facility into a common data repository; transforming the collected data based on provided intelligence; exploiting the transformed data in the common data repository; determining a best possible alternative for continued operation of the batch process based on the exploited data; providing a visualization of the best possible alternative of the batch process using a digital twin; and operating the batch process using the determined best possible alternative on a physical twin corresponding to the digital twin.

A network computer system is provided to fulfill order requests. For each order request, the computer system selects a service provider from a pool of available service providers, to transport a corresponding delivery order from a corresponding supplier to a location of the requester. A provisioning level indicator is determined for a given region and time interval. Based on the provisioning level indicator, the network computer system selectively batches order deliveries.

The present invention discloses a data difference-driven self-learning dynamic batch process optimization method including the following steps: collect production process data off line; eliminate singular batches through PCA operation; construct time interval and index variance matrices to carry out PLS operation to generate initial optimization strategies; collect data of new batches; run a recursive algorithm; and update the optimization strategy. The present invention utilizes a perturbation method to establish initial optimization strategies for an optimized variable setting curve. On this basis, self-learning iterative updating is carried out for mean values and standard differences on the basis of differences in data statistics, so that the continuous improvement of optimized indexes is realized, and thereby a new method is provided for batch process optimization strategies for solving actual industrial problems. The present invention is fully based on operational data of a production process, and does not need priori knowledge about a process mechanism and a mechanism model. The present invention is applicable to the dynamic optimization of operation trajectories of batch reactors, batch rectifying towers, batch drying, batch fermentation, batch crystallization and other processes and systems adopting batch operation.

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

A method for predicting batch yield includes: accessing a batch range specification defining a set of batch ranges for characterizing batch output upon completion of a performed instance of a verified digital procedure; and, in response initiating an instance of the verified digital procedure detecting a set of manufacturing inputs in the verified digital procedure and receiving a set of parameters corresponding to the set of manufacturing inputs from the operator. The method also includes: identifying a batch yield of the instance of the verified digital procedure as corresponding to a target batch range in the set of batch ranges; interpreting a process change resulting in the batch yield based on the set of parameters and a target set of parameters; isolating a parameter associated with the process change; and initializing a new digital procedure characterizing the process change based on the verified digital procedure and the first parameter.

The present invention discloses a data difference-driven self-learning dynamic batch process optimization method including the following steps: collect production process data off line; eliminate singular batches through PCA operation; construct time interval and index variance matrices to carry out PLS operation to generate initial optimization strategies; collect data of new batches; run a recursive algorithm; and update the optimization strategy. The present invention utilizes a perturbation method to establish initial optimization strategies for an optimized variable setting curve. On this basis, self-learning iterative updating is carried out for mean values and standard differences on the basis of differences in data statistics, so that the continuous improvement of optimized indexes is realized, and thereby a new method is provided for batch process optimization strategies for solving actual industrial problems. The present invention is fully based on operational data of a production process, and does not need priori knowledge about a process mechanism and a mechanism model. The present invention is applicable to the dynamic optimization of operation trajectories of batch reactors, batch rectifying towers, batch drying, batch fermentation, batch crystallization and other processes and systems adopting batch operation.

A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by, a processor sing a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.