Abstract: A process for upgrading a pyrolysis oil comprising treating the pyrolysis oil with an upgrading solution to provide a mixture comprising an extract phase and a raffinate phase, wherein the upgrading solution comprises a polar organic solvent, and wherein the pyrolysis oil is a derived from the pyrolysis of plastic or rubber, or a combination thereof, and an upgraded pyrolysis oil prepared by said process.

Abstract: A process for upgrading a pyrolysis oil comprising treating the pyrolysis oil with an upgrading solution to provide a mixture comprising an extract phase and a raffinate phase, wherein the upgrading solution comprises a polar organic solvent, and wherein the pyrolysis oil is a derived from the pyrolysis of plastic or rubber, or a combination thereof, and an upgraded pyrolysis oil prepared by said process.

Abstract: A method includes obtaining training data having at least one training engineering schematic containing multiple known symbols, where different ones of the known symbols represent different types of equipment. The method also includes augmenting the training data with at least one additional training engineering schematic having at least one synthetic schematic, where the at least one synthetic schematic contains additional known symbols. The method further includes training at least one machine learning model using the training data including the at least one training engineering schematic and the at least one additional training engineering schematic.

Abstract: A method includes using templates to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets At least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets. The method also includes generating one or more optimization problems, where the constraints and the at least one objective function represent at least part of the one or more optimization problems. The method further includes generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.

Abstract: A method includes obtaining an engineering schematic containing multiple symbols and connections involving the symbols, where different ones of the symbols in the engineering schematic represent different types of equipment. The method also includes identifying visual features of the engineering schematic. The method further includes processing the visual features using at least one trained machine learning model to (i) identify boundaries around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications, where different ones of the classifications are associated with different types of symbols.

Abstract: A method includes obtaining a solution representing a proposed production schedule associated with multiple processing units in a facility, where the multiple processing units are capable of producing different products. The method also includes performing post-processing of the solution to identify a final production schedule for the processing units in the facility. The post-processing of the solution includes multiple operations each configured to modify the proposed production schedule so that the final production schedule is feasible given constraints associated with the processing units in the facility.

Abstract: A method includes obtaining information identifying (i) multiple processing units in a facility, (ii) multiple interconnections between the processing units, and (iii) constraints associated with the processing units and the interconnections. The method also includes identifying an optimization problem associated with production of multiple products by the processing units in the facility, where the optimization problem is associated with a cost function. The method further includes removing one or more terms from the optimization problem to generate a relaxed optimization problem. In addition, the method includes generating one or more solutions to the relaxed optimization problem, where each solution represents a proposed production schedule.

Abstract: A method includes obtaining information defining multiple customer orders for one or more products over time. The method also includes using machine learning to identify one or more of the customer orders that are likely to change based on classification of the customer orders. The method further includes using machine learning to estimate one or more lengths of time that the one or more customer orders are likely to change based on regression of the customer orders. In addition, the method includes generating a consumption plan for a facility based on the one or more estimated lengths of time that the one or more customer orders are likely to change.

Abstract: A process for upgrading a pyrolysis oil comprising treating the pyrolysis oil with an upgrading solution to provide a mixture comprising an extract phase and a raffinate phase, wherein the upgrading solution comprises a polar organic solvent, and wherein the pyrolysis oil is a derived from the pyrolysis of plastic or rubber, or a combination thereof, and an upgraded pyrolysis oil prepared by said process.

Abstract: The present invention relates to a process for upgrading a petroleum product comprising mixing the petroleum product with an upgrading solution to provide a two-phase mixture consisting of an extract phase and a raffinate phase, wherein the upgrading solution comprises a pyrolysis oil and optionally a polar organic solvent. In other aspects, the invention provides an upgrading solution comprising a pyrolysis oil and optionally a polar organic solvent and uses thereof. Furthermore, an upgraded petroleum product and raffinate phase is provided.