Sales gas output from a glycol dehydration unit in a gas plant is flown into a sales gas pipeline. One or more operational parameters of the glycol dehydration unit are received. One or both of an amount of glycol carryover and an amount of water carryover in sales gas output from the glycol dehydration unit in the gas plant are determined based on the one or more operational parameters of the glycol dehydration unit. An amount of glycol and water co-condensation in the sales gas pipeline is determined based on one or more operational parameters of the sales gas pipeline and the one or both of the amount of glycol carryover and the amount of water carryover. An amount of black powder formation of the sales gas is determined based on the amount of glycol and water co-condensation.

Systems and methods determine a filter efficiency of a fluid filter. A characteristic of fluid flow of a fluid is monitored at a first point and at a second point on a flow path of a fluid filter. A particle absorption level of the fluid filter is determined that is a difference in the characteristic of the fluid flow at the first point and the second point. The particle absorption level of the fluid filter is at a particle absorption saturation level based on the difference in the characteristic of the fluid flow at the first point and the second point. The particle absorption saturation level is an indicator that the particles retained by the fluid filter is decreased and replacement of the fluid filter is required to increase the particles retained by the fluid filter to be above the particle absorption saturation level.

An aftertreatment system comprises a particulate filter configured to filter PM possessing a predetermined, least effective size range included in an exhaust gas flowing through the aftertreatment system. A PM sensor assembly is positioned downstream of the particulate filter and includes a housing having an inlet, an outlet, a sidewall and defines an internal volume. A PM sensor is positioned within the internal volume. The housing is configured to redirect a flow of exhaust gas entering the PM sensor assembly around the PM sensor so that small particles included in the exhaust gas flow having a first size within or smaller than the predetermined size range are directed around the particulate matter sensor. Large particles having a second size larger than the predetermined size range impact the PM sensor. A controller is communicatively coupled to the PM sensor.

A system and method for determining a condition of a filter filtering fuel associated with an engine. Input information relating to the operation of the engine is provided by a plurality of sensors. At least some of the input information is used to determine a plurality of input variables, the plurality of input variables representing a plurality of engine operating conditions including engine run time, engine torque and engine speed. An algorithm incorporating the input variables is used to determine the condition of the filter. Information concerning the condition of the filter may be output to a user such as an operator or service provider.

A method for predicting the likelihood of coagulation of active material particles contained in a slurry for electrode preparation includes measuring rheological properties before and after the slurry is subjected to a shear. The estimation method enables a prediction of filter clogging with a slurry, and thus makes it possible to estimate the likelihood of filter clogging with a slurry without passing the slurry directly through the filter, thereby improving the efficiency of a battery manufacturing process.

The invention relates to a method for determining a loading of a soot filter with soot particles from an exhaust gas mass flow of an internal combustion engine in a motor vehicle, a control device for an internal combustion engine having a soot filter, and a computer program product for carrying out the method. In the first step 100 of the method a characteristic curve for the relationship between the exhaust gas mass flow, exhaust gas temperature, ambient pressure, and pressure drop across the soot filter without loading is determined; in the second step 200 a second exhaust gas mass flow and a second pressure drop that occurs during loading of the soot filter are determined; in the third step 300, from the characteristic curve the first pressure drop is determined for which the first and second exhaust gas mass flows have the same value; in the fourth step 400 an estimated value for the loading of the soot filter is computed via a real-time parameter estimation, preferably by use of the gradient method, based on the previously determined parameters. The method allows a reliable determination of the instantaneous loading of a particulate filter, regardless of the type of measuring signals used in each case for characterizing the loading behavior of the soot filter.

A particulate detection system detects the amount of particulates contained in filtered exhaust gas which has passed through a filter and is discharged to the outside of the vehicle. The particulate detection system includes a sensor disposed on the downstream side of the filter; a sensor circuit section which drives the sensor and obtains in real time a sensor output corresponding to the volumetric particulate amount of the particulates contained in the filtered exhaust gas; and a computation section which computes a distance particulate amount which is the weight or number of the particulates discharged per unit travel distance on the basis of the sensor output obtained by the sensor circuit section, travel distance of the vehicle, and flow rate of the exhaust gas.

A control system for a variable flow filtration system that is configured for receiving an operating condition of a variable speed impeller of the variable flow filtration system; determining a threshold filter force associated with the received operating condition to determine an increased threshold filter force proportionally to increases in the received operating condition; receiving one or more real-time environmental conditions of the variable flow filtration system; modifying the determined threshold filter force based on the received one or more real-time environmental conditions; receiving a real-time filter force imparted by a filter of the variable flow filtration system on a load cell or other filtration system component, the filter force being imparted by a flow of gas or fluid driven by the variable speed impeller; comparing the received real-time filter force to the modified determined threshold filter force; and generating a filter status notification based on the comparison.

In one embodiment, a computing device includes one or more processors configured to execute instructions that cause the one or more processors to acquire pressure data measured by at least one pressure sensor disposed proximate to a filter house in an intake of a gas turbine engine system, derive an airflow or an air mass flow through a duct of the intake using a thermodynamic model of the gas turbine engine system based at least on the pressure data, derive an intake pressure drop in the duct using at least the pressure data, derive a loss parameter of the filter house by combining the air mass or air mass flow, and the intake pressure drop, derive a pressure loss model based on the loss parameter over a period of time, and determine a condition of the filter house based on the pressure loss model.

Disclosed is a method of predicting the lifespan of a filter in an air cleaner based on machine learning. According to an embodiment of the present disclosure, a machine learning-based filter lifespan prediction method may more precisely predict the lifespan of a filter in an air cleaner by inputting fine dust concentration data and a history related to use of the air cleaner to a lifespan prediction model and determining the purifying efficiency and exchange time of the filter according to an output value. Intelligent air cleaner of the present disclosure can be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, etc.