In order to detect the degree of deterioration of one catalyst with a simple configuration, a catalyst deterioration judging device for an internal combustion engine includes an upstream catalyst disposed in an engine exhaust passage, a downstream catalyst disposed in the engine exhaust passage downstream of the upstream catalyst, an inflow air-fuel ratio sensor for detecting the air-fuel ratio of an inflow exhaust gas to the upstream catalyst, an outflow air-fuel ratio sensor for detecting the air-fuel ratio of an outflow exhaust gas from the downstream catalyst, and an electronic control unit. The maximum oxygen storage amount of the upstream catalyst is detected based on the output of the inflow air-fuel ratio sensor and the output of the outflow air-fuel ratio sensor when the upstream catalyst is in the active state and the downstream catalyst is in the inactive state.

A system of diagnosing a deterioration of a catalyst, which includes a precious metal component and a ceramic part and purifies an exhaust gas emitted from an engine, includes: a diagnosis means comparing a diagnosis object value corresponding to an output from an NOx sensor provided on a downstream side with respect to the catalyst and a predetermined diagnosis threshold value stored in a storage means, thereby diagnosing a degree of deterioration of the catalyst, wherein the diagnosis threshold value is previously determined based on a correlationship between the diagnosis object value and a total weight value of NOx and THC in the exhaust gas through the catalyst in a state where the vehicle travels under a constant threshold value setting condition, and the diagnosis means diagnoses that a deterioration occurs in the precious metal component when the diagnosis object value exceeds the diagnosis threshold value.

A method for operating an internal combustion engine includes combusting a fuel and air mixture within a combustion chamber of an internal combustion engine, thereby forming an exhaust gas, passing the exhaust gas out of the combustion chamber, performing a startup procedure, the startup procedure including passing the exhaust gas from the combustion chamber to a storage unit, capturing criteria pollutants of the exhaust gas with the storage unit, passing the exhaust gas from the storage unit to an aftertreatment system, heating the aftertreatment system to an activation temperature with the exhaust gas from the storage unit, and subsequent to heating the aftertreatment system to the activation temperature, performing a secondary procedure, the secondary procedure including passing the exhaust gas from the combustion chamber to the aftertreatment system thereby forming a treated exhaust gas, and passing the treated exhaust gas to the storage unit.

A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.

A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.

A catalyst deterioration diagnosis device includes: a unit configured to obtain a temperature of a catalyst; a unit configured to obtain a sensor output from a gas sensor disposed between the catalyst and an exhaust port; and a unit configured to determine the catalyst to be faulty when the temperature of the catalyst obtained when the sensor output becomes a preset evaluative output is equal to or higher than an evaluative catalyst temperature.

A chemical species mass flow meter measurement system for use in fluid mixture streams includes a chemical species concentration detection analyzer physically located within a fluid volume flow rate sensing probe along with bulk temperature and pressure sensing devices for relating to standard conditions. The system uses concentration detection analyzers specifically suited to the intended application. Applications include the measurement of exhaust mass emissions from vehicles, the fuel economy of vehicles, as well as the measurement of the mass flow rate of chemical species of interest in general industrial processes.

A apparatus 70 for measuring combustible-gas concentration includes an electromotive force acquisition section 75 configured to acquire information about an electromotive force of a mixed potential cell 55 while a detection electrode 51 is exposed to a target gas, an oxygen concentration acquisition section 76 configured to acquire information about oxygen concentration p.sub.O2 in the target gas, and a control section 72. The control section 72 derives combustible-gas concentration p.sub.THC in the target gas from the acquired information about the electromotive force EMF, the acquired information about the oxygen concentration p.sub.O2, and the relationship represented by formula (1):
EMF= log.sub.a(p.sub.THC) log.sub.b(p.sub.O2)+B(1)
where , , and B each represent a constant, and a and b each represent any base (provided that a1, a>0, b1, and b>0).

In order to provide an exhaust gas measuring system capable of more accurately correcting errors in measurement results caused by response delays of exhaust gas measuring devices, and the like, the exhaust gas measuring system is adapted to include: a sampling pipe adapted to, from a lead-out port, lead out exhaust gas introduced from an introduction port; one or more types of exhaust gas measuring devices that are connected to the lead-out port and measure predetermined physical quantities related to the exhaust gas flowing through the sampling pipe; a correction device adapted to correct measurement results by the exhaust gas measuring devices; and a pressure sensor adapted to measure the pressure inside the sampling pipe, in which the correction device corrects errors in the measurement results, which are caused by response delays, with the measured pressure by the pressure sensor as a parameter.

A learning use data set showing relationships among an engine speed, an engine load rate, an air-fuel ratio of the engine, an ignition timing of the engine, an HC or CO concentration of exhaust gas flowing into an exhaust purification catalyst and a temperature of the exhaust purification catalyst is acquired. The acquired engine speed, engine load rate, air-fuel ratio of the engine, ignition timing of the engine, and HC or CO concentration of the exhaust gas flowing into the exhaust purification catalyst are used as input parameters of a neural network and the acquired temperature of the exhaust purification catalyst is used as training data to learn a weight of the neural network. The learned neural network is used to estimate the temperature of the exhaust purification catalyst.