A control cabinet for exhaust gas measuring facilities. The control cabinet includes a first cabinet body with first measuring appliances arranged therein for analyzing at least one sample gas, a door which opens and closes an open front side of the first cabinet body, the door being rotatably attached to the first cabinet body, an operating unit arranged on the door, and a second cabinet body with second measuring appliances arranged therein. The second cabinet body includes a second front side that can be closed and opened via a guided relative movement of the first cabinet body in relation to the second cabinet body. The first cabinet body is movable in a guided manner relative to the second cabinet body.

A gas sensor and methods for producing the same are disclosed. The gas sensor of the present disclosure includes a bulk silicon layer, comprising a controllable inversion layer, an oxide layer on top of the bulk silicon layer, wherein the controllable inversion layer is located at an interface of the bulk silicon layer and the oxide layer, and a sensing layer on the oxide layer, wherein a sensitivity of the sensing layer is a function the controllable inversion layer.

An apparatus for measuring a concentration of a target gas includes: a gas sensor including a sensing layer having an electric resistance that changes by an oxidation reaction or a reduction reaction between gas molecules and the sensing layer; and a processor configured to, in response to the target gas being introduced along with air into the gas sensor, monitor a change of the electric resistance of the sensing layer and determine the concentration of the target gas by analyzing a shape of the change of the electric resistance.

A partial pressure detector and methods of detecting a partial pressure are provided, in which a thermal conductivity gauge, such as a Pirani gauge, is configured to sense a pressure of a mixture of gases within a vacuum chamber. An input of the partial pressure detector is configured to receive a total pressure reading from a species-independent pressure sensor of the mixture of gases in the vacuum chamber, and a controller configured to provide an output representing an amount of a species of gas in the vacuum chamber as a function of the pressure as sensed by the thermal conductivity gauge and the received total pressure reading. The controller has a resolution, and a range of the resolution is scaled to a range of expected partial pressures of the species. The output can be a partial pressure or a weighted partial pressure of the gas species.

Provided is an online centralized monitoring and analysis system using an electronic nose instrument for multi-point malodorous gases, and the system includes an electronic nose instrument, which connects with multiple monitoring points through pipes. On-site malodorous gases in the maximum range of 2.5 km are drawn into the electronic nose instrument within 1 min by the external vacuum pump, and forced to flow through an annular working chamber of a gas sensor array for 30 s by the internal vacuum pump periodically. The modular convolution neural networks online learn the recent time-series responses of the gas sensor array and predict their coming responses, and the modular deep neural networks offline set up the relationship between the responses and multiple concentration items according to odor big data. The electronic nose instrument monitors up to 10 pollution sites cyclically and uses the cascade machine learning model to online predict one dimensionless odor-unit (OU) concentration index value and 10 specified-component concentration index values of malodorous gases.

Systems, apparatuses, and methods for monitoring an environment are provided. One system includes a monitoring unit positioned within an environment and including an acoustic sensor configured to generate detected acoustic data regarding acoustics in the environment, and a controller having one or more processors and one or more non-transitory memory devices that store instructions for controlling the one or more first processors to receive and store the detected acoustic data, determine, based on the detected acoustic data, whether a noise is above a threshold, and determine, based on the detected acoustic data and that the noise is above the threshold, an estimated source of the noise.

A method for managing air quality may include, at one or more processors, receiving sensor data comprising a plurality of air quality parameters for an environment, wherein the sensor data is generated by one or more environment quality monitoring devices located in the environment, predicting an adverse air quality event based on the sensor data, and automatically controlling one or more devices to mitigate the adverse air quality event. An environment quality monitoring device may include a housing, a plurality of sensors in the housing and configured to generate sensor data comprising a plurality of environment quality parameters, a network communication device configured to communicate the sensor data over a network, and an alert configured to indicate an environment quality score of the ambient environment, where the environment quality score is based on at least a portion of the sensor data.

Provided is an online centralized monitoring and analysis system based on an electronic nose instrument for multi-point malodorous gases, and the system includes an electronic nose instrument, which connects with multiple monitoring points through pipes. On-site malodorous gases in the maximum range of 2.5 km are drawn into odor electronic nose instrument within 1.0 min by the external vacuum pump, and forced to flow through the annular gas sensor array for 30 s by the internal vacuum pump periodically. The modular convolution neural networks online learn the recent time-series responses of the gas sensor array and predict their coming responses, and the modular deep neural networks offline set up the relationship between the responses and multiple concentration items according to odor big data. The odor electronic nose instrument monitors up to 10 pollution sites cyclically and uses the cascade machine learning model to online predict one dimensionless unit and 10 specified-component concentration index values of malodorous gases.

A smoke and temperature detection system includes a plurality of fiber optic cables terminating in a plurality of nodes positioned to monitor a fire or smoke condition at one or more protected spaces, and a temperature detection fiber optic cable having a plurality of fiber Bragg gratings arrayed along the temperature detection fiber optic cable. A control system is operably connected to the plurality of fiber optic cables and to the temperature detection fiber optic cable. The control system includes a first light sensitive device configured to receive a scattered light signal from the plurality of fiber optic cables, and a second light sensitive device configured to receive a reflected light signal from the fiber Bragg gratings. The control system is configured to detect a temperature at the fiber Bragg gratings based on one or more properties of the reflected light signal received at the second light sensitive device.

A multi-core sensor system is provided. The multi-core sensor system can intelligently determine whether the reason for an abrupt dramatic change in sensor data is a sub-sensor fault or sudden pollution, so as to increase reliability of detected data of the sub-sensor. The multi-core sensor system can automatically determine whether the repair is needed when a device fault occurs, so as to ensure the continuity of sub-sensor detected data, which has significant value for continuous monitoring required for a haze treatment operation. In addition, human and material resources for device maintenance may be saved, thereby reducing waste.