Errors, sensor parameters

Monitoring by Electromechanical Instrumentation. According to basic engineering principles, no process can be conducted safely and effectively unless instantaneous information is available about its conditions. AH sterilizers are equipped with gauges, sensors (qv), and timers for the measurement of the various critical process parameters. More and more sterilizers are equipped with computerized control to eliminate the possibiUty of human error. However, electromechanical instmmentation is subject to random breakdowns or drifts from caUbrated settings and requires regular preventive maintenance procedures. 

In general, the tracking procedure starts with an association process to combine the established track parameter with the radar sensor or radar network measurements. Errors in the association process will always lead to ghost targets. But the general requirement for automotive applications is to keep the false alarm probability as low as possible, which underlines the importance of the association process for radar networks. 

A fault is understood as an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable, usual or standard condition. A fault can stem from several origins as depicted by the Figure 1. It can be caused by an unexpected perturbation i.e., a major deviation from one input acting on the system) or by a disturbance i.e., the action of an unknown and uncontrolled input on the system). Another fault origin can be an error of any sensor or actuator, which is a deviation between the measured and the true or specified value. 

Finally, for a (bio)chemical sensor to effectively solve real problems it should require no immediate interpretation of its response (e.g. in order to alter some physical or physico-chemical parameter influencing its operation). In practice, this requires that the sensor be reliably used by unskilled personnel, who often work under stressing conditions, in order to avoid the human factor as a source of error in the results produced by (bio)chemical sensors. 

Biochemical oxygen demand (BOD) is one of the most widely determined parameters in managing organic pollution. The conventional BOD test includes a 5-day incubation period, so a more expeditious and reproducible method for assessment of this parameter is required. Trichosporon cutaneum, a microorganism formerly used in waste water treatment, has also been employed to construct a BOD biosensor. The dynamic system where the sensor was implemented consisted of a 0.1 M phosphate buffer at pH 7 saturated with dissolved oxygen which was transferred to a flow-cell at a rate of 1 mL/min. When the current reached a steady-state value, a sample was injected into the flow-cell at 0.2 mL/min. The steady-state current was found to be dependent on the BOD of the sample solution. After the sample was flushed from the flow-cell, the current of the microbial sensor gradually returned to its initial level. The response time of microbial sensors depends on the nature of the sample solution concerned. A linear relationship was foimd between the current difference (i.e. that between the initial and final steady-state currents) and the 5-day BOD assay of the standard solution up to 60 mg/L. The minimum measurable BOD was 3 mg/L. The current was reproducible within 6% of the relative error when a BOD of 40 mg/L was used over 10 experiments [128]. 

Ri is sensitive to temperature and even a relatively small error in temperature estimate can introduce a sizable discrepancy into the apparent p02 based on some PFCs. The relative error introduced into a p02 determination by a 1 °C error in temperature estimate ranges from 8 Torr/°C for PFTB [207] to 3 Torr/°C for PFOB (perflubron) [223] or 15-Crown-5-ether [218] when p02 is actually 5 Torr. HFB exhibits remarkable lack of temperature dependence and the comparative error would be 0.1 Torr/°C [224], Recognizing differential sensitivity of pairs of resonances within a single molecule to p02 and temperature, Mason et al. [207,225] patented a method to simultaneously determine both parameters by solving simultaneous equations. However, generally it is preferable for a p02 sensor to exhibit minimal response to temperature, since this is not always known precisely in vivo and temperature gradients may occur across tumors. 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

Based on the above facts, conception of the relative error of the sensor can be introduced, which can be determined as = Ax/x. This appraisal, expressing as a rule in percentage (i.e., y = (Ax/x) x 100%), is a more comprehensive characteristic of the accuracy of the sensor. The relative error y, is the function of the measuring parameter x. It is therefore impossible to point out the single exact characteristic of the sensor, which would somehow characterize the error. Consequently, the concept of the reduction error y is introduced, which represents the ratio between the maximum value of the absolute error Ax and the maximum value of the measuring parameter x, that is, y = Ax Jx . ... 

If some of the errors given above can be dominated for the real zirconia-based sensors, then the other constituents can be ignored. If only the additive constituent part takes place, then the zirconia gas sensor function assumes y = k x Aq), where the current value of the absolute sensor error, equal to A = Aq, is independent of the value of the measuring parameter. The value of the relative error y= Yq = Aq /jc is inversely proportional to the value of the measuring parameter, the sensor error growths up to 100% at X = Ag, that is, it is impossible to make a measurement in this case. The value of the measuring parameter x, equal to the value of the relative error of zero, is acceptedly called the sensitivity threshold of the sensor. 

If only the multiplicative constituent part is present, then the function of the zirconia gas sensor assumes y = fc(l y ) x, where y = Yt is the current value of the relative error. In this case, the function of the measuring parameter of the sensor is the absolute value of the error. 

Song et al (2006) proposed a multivariable purity control scheme using the m-parameters as manipulated variables and a model predictive control scheme based on linear models that are identified from nonlinear simulations. The approach proposed by Schramm, Griiner, and Kienle (2003) for purity control has been modified by several authors (Kleinert and Lunze, 2008 Fiitterer, 2008). It gives rise to relatively simple, decentralized controllers for the front positions, but an additional purity control layer is needed to cope with plant-model mismatch and sensor errors. Vilas and Van de Wouwer (2011) augmented it by an MPG controller based on a POD (proper orthogonal collocation) model of the plant for parameter tuning of the local PI controllers to cope with the process nonlinearity. 

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