Abstract: Devices, methods, and systems for predictive asset maintenance using a predictive emissions machine learning model are described herein.

Abstract: Techniques for instantaneous leak detection of a compound associated with an asset are described. In one aspect, data streams from multiple sensing elements associated with the asset are received, where the multiple sensing elements are to detect leak attributes corresponding to the compound, a leak attribute being indicative of a potential leak of the compound. A first set of data streams including at least one leak attribute from amongst a pre-defined set of leak attributes are identified and monitored in real-time to identify a change in value of the said leak attribute. The change in value of the leak attribute is analysed to determine presence of a leak. Further, a data stream associated with the at least one leak attribute is identified to trace sensing elements amongst the multiple sensing elements associated with said data stream, based on which the leak is contextualized and provided to a user interface.

Abstract: Systems and methods for generating industrial process flow diagrams using generative AI and image recognition are described herein. In certain embodiments, a system includes memory devices that stores a cognitive services model trained to determine whether image data represents process information; and a generative model trained to generate process diagram information from the image data. The system also includes processors that receive the image data; and execute the cognitive services model to determine whether the image data contains information associated with a process. When the cognitive services model determines that the image data contains the information associated with the process, the processors are also execute the generative model using the image data to generate the process diagram information. Further, the processors provide the process diagram information to a diagram visualization program, wherein the diagram visualization program generates a diagram from the process diagram information.

Abstract: Continuous emission level monitoring and detection using unmanned aerial vehicles is described herein. One embodiment includes capturing, by a plurality of sensors located at a site, emission levels at the site, receiving, by a computing device, the captured emission levels from the plurality of sensors, determining, by the computing device based on the emission levels received from the plurality of sensors, whether to trigger an unmanned aerial vehicle (UAV) to fly to a location at the site to detect whether an emission level at the location exceeds a pre-determined threshold, and triggering, by the computing device responsive to a result of the determination, the UAV to fly to the location at the site to detect whether an emission level at the location exceeds the pre-determined threshold.

Abstract: Various embodiments described herein relate to providing and/or employing a system and a method for managing a plurality of market-based instruments in a facility. In this regard, emission factors corresponding to a plurality of assets in the facility are stored in an emission factor repository. As a result, total actual emissions corresponding to the plurality of assets is calculated based on the stored emission factors. Then the total actual emissions are compared with a carbon emission target, wherein the carbon emission target corresponds to a specific period. Furthermore, total emissions are predicted in the facility for the specific period based on the stored emission factors and a result of the comparison. Accordingly, the predicted total emissions in the facility is displayed via a user interface of a display device.

Abstract: Various embodiments described herein relate to systems and methods for managing emissions in a facility. In this regard, a set point at which each asset of a plurality of assets is operating in the facility is determined. An emission level associated with each asset of the plurality of assets is determined as well. The emission level associated with each of the plurality of assets is correlated to determine if the correlated emission level exceeds a predefined threshold. If the correlated emission level exceeds the predefined threshold, one or more corrective actions for at least one asset is identified. Also, the one or more corrective actions are rendered on a display. An input is received from an operator that corresponds to a selection of one of a corrective action. Based on the corrective action, the at least one asset is operated at a first set point different from the set point.

Abstract: Approaches for estimating emission intensity based on a proposed production plan are described. In an example, the emission intensity may be determined based on a weighted distribution of greenhouse gas emissions corresponding to an emission source. Emission source may further include a contributing category, with the contributing category further comprising a source level category. In an example, the weighted distribution may correspond to each of the contributing category and the source level category. In such a case, a first weighted distribution corresponding to a source level category indicates the contribution of greenhouse gas emissions by the source level category to the contributing category. In a similar manner, a second weighted distribution corresponding to the contributing category may depict the contribution of greenhouse gas emissions to the emission source.