“…But, concerning the practical feasibility, not only the suitability to represent the data counts, but also the effect of expectable variable errors are of immense importance. Corresponding to the research work of Hoche et al (2015), standard errors (SE, see Table 2) were defined for all input variables and their impact was determined via propagation of error by determining the partial derivatives of the models. Following, the results are summarised: The overall error of maltose and ethanol mass fraction due to the SE errors of the M29 and M39 models was twice as in case of the M19 models.…”
Section: Estimation Of Ethanol and Sugar Concentrationthe Direct Apprmentioning
confidence: 99%
“…The developed sensor is based upon a VARIN-LINEÒ mounting flange (GEA Tuchenhagen GmbH, B€ uchen, Germany). Concluding from the calibration and validation trials presented in Hoche et al (2015), it was decided to reduce the reflector distance to 50 mm and to use an acoustically hard reflector material. Furthermore, to deflect backscattered signals from the reflector backside, the reflector was shaped angular.…”
Section: Sensor System and Electronic Hardwarementioning
confidence: 99%
“…whereby TOF is the signal's time-of-flight within the sample liquid and l 2 the calibrated distance between buffer and reflector. The proposed method was validated based on the calibration data and the calibration set-up under ideal laboratory conditions presented by Hoche et al (2015). The found root mean square errors (RMSE) were 0.515E-3 (0.15%) in reflection coefficient and 0.05 m s À1 in sound velocity resulting in a 1.201E-3 g cm À3 (AE0.12%) density RMSE.…”
Summary
In order to implement process analytical technology in beer manufacturing, an ultrasound‐based in‐line sensor was developed which is capable to determine sound velocity and density via the multiple reflection method. Based on a systematic study of the ternary system water–maltose–ethanol, two models were established to estimate the critical process parameters: sugar and ethanol mass fraction. The sound velocity‐based model showed unreasonable high errors although temperature variations and deviations due to dissolved CO2 were corrected. In contrast, the sound velocity–density–temperature model provided an average root mean square error of 0.53%g/g sugar and 0.26%g/g ethanol content for the main fermentation. Method, sensor and model showed the capability to capture the process signature which may be related to product and process quality.
“…But, concerning the practical feasibility, not only the suitability to represent the data counts, but also the effect of expectable variable errors are of immense importance. Corresponding to the research work of Hoche et al (2015), standard errors (SE, see Table 2) were defined for all input variables and their impact was determined via propagation of error by determining the partial derivatives of the models. Following, the results are summarised: The overall error of maltose and ethanol mass fraction due to the SE errors of the M29 and M39 models was twice as in case of the M19 models.…”
Section: Estimation Of Ethanol and Sugar Concentrationthe Direct Apprmentioning
confidence: 99%
“…The developed sensor is based upon a VARIN-LINEÒ mounting flange (GEA Tuchenhagen GmbH, B€ uchen, Germany). Concluding from the calibration and validation trials presented in Hoche et al (2015), it was decided to reduce the reflector distance to 50 mm and to use an acoustically hard reflector material. Furthermore, to deflect backscattered signals from the reflector backside, the reflector was shaped angular.…”
Section: Sensor System and Electronic Hardwarementioning
confidence: 99%
“…whereby TOF is the signal's time-of-flight within the sample liquid and l 2 the calibrated distance between buffer and reflector. The proposed method was validated based on the calibration data and the calibration set-up under ideal laboratory conditions presented by Hoche et al (2015). The found root mean square errors (RMSE) were 0.515E-3 (0.15%) in reflection coefficient and 0.05 m s À1 in sound velocity resulting in a 1.201E-3 g cm À3 (AE0.12%) density RMSE.…”
Summary
In order to implement process analytical technology in beer manufacturing, an ultrasound‐based in‐line sensor was developed which is capable to determine sound velocity and density via the multiple reflection method. Based on a systematic study of the ternary system water–maltose–ethanol, two models were established to estimate the critical process parameters: sugar and ethanol mass fraction. The sound velocity‐based model showed unreasonable high errors although temperature variations and deviations due to dissolved CO2 were corrected. In contrast, the sound velocity–density–temperature model provided an average root mean square error of 0.53%g/g sugar and 0.26%g/g ethanol content for the main fermentation. Method, sensor and model showed the capability to capture the process signature which may be related to product and process quality.
“…Ultrasonic inspection is offering a useful tool for material mechanical integrity testing [1][2][3]. Pulse-echo mode of non-destructive testing is considered in this research.…”
Section: Introductionmentioning
confidence: 99%
“…Such echoes produced carry the information about the location of the defect as the time of flight (ToF) and the severity of the defect or even mechanical properties as the reflection amplitude. ToF can be converted to distance if propagation velocity is known and the reflection amplitude can be converted to mechanical impedance which in turn is related to density and velocity (stiffness) [3,4]. Estimation of the aforementioned parameters for the single echo does not present significant problem, but when several reflectors are present along the wave path the reflections pile up obscuring the reliable estimation of the echoes parameters [5].…”
Abstract.Comparison of the approximation performance of the ultrasonic reflections by simple model functions used in blind deconvolution is presented. New model function was proposed where causality of the signal is easily modelled without the need for piecewise-linear equations. Parameter estimation technique was developed. Performance investigation was done for three model functions using four experimentally collected ultrasonic signals under different signal filtering conditions.
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