Dynamic sensor operation

6 (a) Schematic drawing of the MISiC field effect-based CO2 sensor with a binary carbonate (Li2C03-BaC03) auxiliary layer (LCR meter inductance, capacitance and resistance meter), (b) and (c) Response characteristics during CO2 exposure at 400°C for the device in (a). Reprinted with permission from Electrochemical and Solid State Letters 14 1 (2010), J4-7. 2010 The Electrochemical Society (Inoue et a ., 2010). 

The most common method to reduce dimensionality is Principal Components Analysis (PCA) (Chatfield, 1980 Wold et ah, 2001). Multivariate methods such as PCA have, for example, been used in conjunction with SiC-based field effect devices to monitor the combustion process in bio mass fuelled power plants (Uneus et n/., 2005) for the estimation of ammonia concentration in typical flue gases (Andersson et al., 2004) and for fast lambda control of a gasoline engine (Larsson et al, 2002). 

This approach has been developed using commercial resistive-type metal oxide semiconductor sensors - for example, for early fire detection in coal mines (Lee and Reedy, 1999 Reimann et al, 2009). This concept is now also under development for field effect sensor devices based on SiC for the detection and quantification of, for example, NO2. It has initially been demonstrated that discrimination between different gases (such as H2, NH3 and CO) and different concentrations seems possible for both Pt and Ir gate field effect sensors (Bur et al, 2010 Bur et a/., 2011a). 

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Automatic control of purities is difficult due to the long time delays and the complex dynamics that are described by nonlinear distributed parameter models and switching of the inputs, leading to mixed discrete and continuous dynamics, small operating windows, and a pronouncedly nonlinear response of the purities to input variations. Because of the complex nonlinear dynamics of SMB processes, their automatic control has attracted the interest of many academic research groups and many different control schemes have been proposed however, few of them have been tested in experimental work for real plants with limited sensor information. 

Vasiliev et al. (1998,1999) showed that CnO/SnO heterostructures have a high sensitivity to H S. In addition Vasiliev et al. (1998) found that heterostructure-based sensors had shorter response times to 25-300 ppm H S in at 100-250 °C compared to those of SnOj(CuO) films. Operating characteristics of SnOj/CuO heterostructures sensitive to H S are presented in Fig. 2.28. It is suggested that the improvement of dynamic sensor properties of SnO /CuO heterostructures is caused by the localization of electrical barrier between CuO and SnO layers. 

The EPBA/Sortbat and Eurobatri facilities work with electro dynamic sensors. This process has already been implemented in routine operation for sorting portable battery mixtures (see Figures 19.6 and 19.7). 

Dynamic explosion detectors use a piezoresistive pressure sensor installed behind the large-area, gas-tight, welded membrane. To ensure optimum pressure transference from the membrane to the active sensor element, the space between the membrane and the sensor is filled with a special, highly elastic oil. The construc tion is such that the dynamic explosion detec tor can withstand overpressures of 10 bar without any damage or effect on its setup characteristic. The operational range is adjustable between 0 and 5 bar abs. Dynamic explo-... 

The balancing process must be in accord with the rotor dynamics, as specified by the operating environment. Unfortunately, the dynamic characteristics are often not properly recognized when the balancing procedure is specified. As a result, the unbalance distribution problem may not be identified not enough planes may be provided sensors may be located at nonoptimum positions, or critical speeds may be overlooked entirely. It is the responsibility of the machinery end user to satisfy himself that the manufacturer has considered ... 

The third block in Fig. 2.1 shows the various possible sensing modes. The basic operation mode of a micromachined metal-oxide sensor is the measurement of the resistance or impedance [69] of the sensitive layer at constant temperature. A well-known problem of metal-oxide-based sensors is their lack of selectivity. Additional information on the interaction of analyte and sensitive layer may lead to better gas discrimination. Micromachined sensors exhibit a low thermal time constant, which can be used to advantage by applying temperature-modulation techniques. The gas/oxide interaction characteristics and dynamics are observable in the measured sensor resistance. Various temperature modulation methods have been explored. The first method relies on a train of rectangular temperature pulses at variable temperature step heights [70-72]. This method was further developed to find optimized modulation curves [73]. Sinusoidal temperature modulation also has been applied, and the data were evaluated by Fourier transformation [75]. Another idea included the simultaneous measurement of the resistive and calorimetric microhotplate response by additionally monitoring the change in the heater resistance upon gas exposure [74-76]. 

In order to determine the thermal time constant of the microhotplate in dynamic measurements, a square-shape voltage pulse was applied to the heater. The pulse frequency was 5 Hz for uncoated and 2.5 Hz for coated membranes. The amplitude of the pulse was adjusted to produce a temperature rise of 50 °C. The temperature sensor was fed from a constant-current source, and the voltage drop across the temperature sensor was amplified with an operational amplifier. The dynamic response of the temperature sensor was recorded by an oscilloscope. The thermal time constant was calculated from these data with a curve fit using Eq. (3.29). As already mentioned in the context of Eq. (3.37), self-heating occurs with a resistive heater, so that the thermal time constant has to be determined during the cooHng cycle. 

For example, it is important to have large enough holdups in surge vessels, reflux drums, column bases, etc., to provide effective damping of disturbances (a much-used rule of thumb is 5 to 10 minutes). A sufficient excess of heat transfer area must be available in reboilers, condensers, cooling Jackets, etc., to be able to handle the dynamic changes and upsets during operation. The same is true of flow rates of manipulated variables. Measurements and sensors should be located so that they can be used for eflcctive control. 

In view of this, a robust scheme based on the Hoo control theory [24] is developed in the present work. The algorithm guarantees both stability and performance for a family of perturbed plants with model uncertainties and exogenous inputs (i.e., chamber disturbances and sensor noises) over a wide range of operating conditions, an advantage especially desired for combustion dynamics problems. 

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