US12554233B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12554233-B2 |
| Application number | US-202318187873-A |
| Country | US |
| Kind code | B2 |
| Filing date | Mar 22, 2023 |
| Priority date | Dec 20, 2022 |
| Publication date | Feb 17, 2026 |
| Grant date | Feb 17, 2026 |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
Embodiments of the present disclosure provide for generating improved, feasible, and optimized pathways for optimizing operation of an industrial plant or component thereof. Embodiments utilize a multi-variate optimization model that may utilize real-time data and any number of available dynamic and static configurations to optimize for multiple optimization parameters. The multi-variate optimization model outputs an optimization pathway, for example including any number of transformation action(s) representing decarbonization step(s), that enable configuration of the plant or processing unit(s) thereof in a manner that optimizes operations of the plant or processing unit(s). For example, the optimization pathway in some contexts is optimized to generate a pathway that reduces the impact of emissions generated by the plant or processing unit(s) in a feasible manner that is most cost efficient for a particular plant in a particular location in consideration with any number of other constraints or considerations.
Opening claim text (preview).
What is claimed is: 1 . A computer-implemented method for producing an optimization pathway for a plurality of assets, the method comprising: receiving, by at least one processor, a plurality of inputs comprising at least one uncertain input and a plurality of variable inputs, wherein the at least one uncertain input is associated with an uncertainty model, and wherein the uncertainty model corresponds to a predicted accuracy of the uncertain input; generating, by the at least one processor, an optimization model, wherein the optimization model is associated with a plurality of optimization parameters, wherein each of the plurality of optimization parameters is associated with an optimization weight corresponding to a relative priority of a particular optimization parameter, and wherein the optimization model is configured based at least in part on the plurality of configuration inputs, the input uncertainty model, at least one hard constraint, and at least one soft constraint; generating, by the at least one processor, the optimization pathway comprising a plurality of decarbonization tasks associated with the plurality of assets based at least in part on the optimization model, wherein the optimization pathway is optimized according to the plurality of selected optimization parameters associated with a least optimization weight, wherein the optimization pathway conforms to each of the one or more hard constraints, and wherein the plurality of decarbonization tasks associated with the plurality of assets comprises adjusting an operation of at least one asset of the plurality of assets to optimize emission for one or more industrial processes; and generating an updated optimization pathway based at least in part on an observed value and the optimization pathway, wherein the observed value comprises at least one measured value captured by one or more sensing devices positioned to capture a physical characteristic of a monitored asset of the plurality of assets. 2 . The computer-implemented method of claim 1 , wherein the plurality of configuration inputs comprise static configuration inputs, and dynamic configuration inputs. 3 . The computer-implemented method of claim 2 , wherein the static configuration inputs comprise at least one of a geographic location of one or more assets of the plurality of assets and an operating environment of one or more assets of the plurality of assets. 4 . The computer-implemented method of claim 2 , wherein the dynamic configuration inputs comprise at least one of a cost of consumable materials, a cost of operations, a budget, regulatory requirements, resources available, and implementation timelines. 5 . The computer-implemented method of claim 1 , wherein the uncertainty model indicates the predicted accuracy of the uncertain input through a probability distribution indicating a probability of an occurrence of a particular value for the uncertain input. 6 . The computer-implemented method of claim 1 , wherein the plurality of optimization parameters are optimized according to an order defined by the optimization weight. 7 . The computer-implemented method of claim 1 , further comprising: receiving a carbon emissions target value representing the target net carbon emissions for the plurality of assets, wherein a model carbon emissions output value associated with the net carbon emissions of the optimized plurality of assets is below the carbon emissions target value. 8 . The computer-implemented method of claim 1 , wherein the plurality of optimization parameters may comprise at least one of total cost, time, risk, and total carbon emissions. 9 . The computer-implemented method of claim 1 , wherein the plurality of decarbonization tasks comprise replacing, modifying, or installing at least one asset of the plurality of assets. 10 . The computer-implemented method of claim 1 , wherein performance of the plurality of decarbonization tasks results in a reduction of a carbon emissions value of one or more assets of the plurality of assets. 11 . The computer-implemented method of claim 1 , wherein the optimization pathway provides a predicted value for each of the plurality of optimization parameters. 12 . An apparatus for producing an optimization pathway for a plurality of assets, the apparatus comprising at least one processor and at least one non-transitory memory including computer-coded instruction thereon, the computer coded instructions, with the at least one processor, cause the apparatus to: receive a carbon emissions value representing a net carbon emission for the plurality of assets; receive a plurality of configuration inputs comprising at least one uncertain input and a plurality of variable inputs, wherein the at least one uncertain input is associated with an uncertainty model, and wherein the uncertainty model represents a predicted accuracy of the uncertain input; receive a sensor data input, wherein the sensor data input represents a physical characteristic of an asset of the plurality of assets; generate an optimization model, wherein the optimization model comprises a plurality of optimization parameters associated with the plurality of variable inputs, wherein each of the plurality of optimization parameters is associated with an optimization weight corresponding to a relative priority of a particular optimization parameter, and wherein the optimization model further comprises the plurality of configuration inputs, the input uncertainty model, a hard constraint, and a soft constraint; generate the optimization pathway comprising a plurality of decarbonization tasks associated with the plurality of assets based at least in part on the optimization model, wherein the optimization pathway is optimized according to the optimization parameters associated with a least optimization weight, wherein the optimization pathway conforms to all of the one or more hard constraints, wherein the plurality of decarbonization tasks associated with the plurality of assets comprises adjusting an operation of at least one asset of the plurality of assets to optimize emission for one or more industrial processes; and update the optimization pathway based at least in part on an observed value, wherein the observed value comprises a measured value captured by one or more sensing devices positioned to capture a physical characteristic of a monitored asset of the plurality of assets. 13 . The apparatus of claim 12 , wherein the plurality of configuration inputs comprise static configuration inputs, and dynamic configuration inputs. 14 . The apparatus of claim 13 , wherein the static configuration inputs comprise at least one of a geographic location of one or more assets of the plurality of assets and an operating environment of one or more assets of the plurality of assets. 15 . The apparatus of claim 13 , wherein the dynamic configuration inputs comprise at least one of a cost of consumable materials, a cost of operations, a budget, regulatory requirements, resources available, and implementation timelines. 16 . The apparatus of claim 12 , wherein the uncertainty model indicates the predicted accuracy of the uncertain input through a probability distribution indicating a probability of an occurrence of a particular value for the uncertain input. 17 . The apparatus of claim 12 , wherein the plurality of optimization parameters are optimized according to an order defined by the optimization weight. 18 . The apparatus of claim 12 , furt
Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title
Optimize, process management, optimize production line · CPC title
Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] · CPC title
Energy or water supply · CPC title
Manufacturing · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.