Abstract: A recovery unit configured to re-inject lost or fugitive emissions back into a pipeline. These configuration may include a pressure vessel that can hold gas (or other fluid) that might vent to atmosphere from, for example, a valve or like flow control. A pump may couple with the pressure vessel. In use, this pump can draw fluid out of the pressure vessel and pressurize it to inject back into the pipeline, typically downstream of the valve. The pump may utilize upstream pressure as a power source.

Abstract: A fluid removal system for removing fluid from a product stream is described herein. The fluid removal system comprises a table, a permeable conveyor to transport the product stream across a top surface of the table and a plenum disposed below the permeable conveyor to draw the fluid from the product stream through the permeable conveyor. At least one vibration inducing mechanism is mounted to the table to provide vibratory motion directly to the table and indirectly to the conveyor. The table is supported by oscillating mounts that provide oscillatory motion to the table.

Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: A fluid removal system for removing fluid from a product stream is described herein. The fluid removal system comprises a table, a permeable conveyor to transport the product stream across a top surface of the table and a plenum disposed below the permeable conveyor to draw the fluid from the product stream through the permeable conveyor. At least one vibration inducing mechanism is mounted to the table to provide vibratory motion directly to the table and indirectly to the conveyor. The table is supported by oscillating mounts that provide oscillatory motion to the table.

Abstract: Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.

Abstract: A fluid removal system for removing fluid from a product stream is described herein. The fluid removal system comprises a table, a permeable conveyor to transport the product stream across a top surface of the table and a plenum disposed below the permeable conveyor to draw the fluid from the product stream through the permeable conveyor. At least one vibration inducing mechanism is mounted to the table to provide vibratory motion directly to the table and indirectly to the conveyor. The table is supported by oscillating mounts that provide oscillatory motion to the table.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.