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Summary To implement process analytical technology in beer manufacturing, a systematic study of the ternary system water–maltose–ethanol with respect to the critical process parameters, density, speed of sound and temperature was performed. The results are presented in the form of temperature and mass‐fraction‐dependent polynomial expressions. On average, a variation of 1% mass fraction maltose results in variations of 3.548 m s−1 ultrasound velocity and 0.0041 g cm−³ density, whereas in the case of ethanol, the variations are 8.060 m s−1 and −0.0018 g cm−3. Indeed, the relations are strictly nonlinear. Nevertheless, the determined data show the feasibility to predict online, concentrations of multicomponent mixtures of polar liquids by determining density and ultrasound velocity. With <0.1% error, the measured data show excellent agreement with reference data of binary mixtures as given in literature.

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.

This work presents an ultrasound‐based inline sensor system, which is used to monitor the alcoholic yeast fermentation. The pulse‐echo method is used to determine ultrasound velocity (USV) of the sample medium. The major aim of the paper is to highlight the importance of an accurate time‐of‐flight (TOF) estimation for accurate concentration determination and to present method immanent strategies to fulfill the requirements. An algorithm aiming at a stable and highly accurate TOF estimation in time domain was developed. The basic methods of the algorithm are a frame‐wise signal analysis based on the sensor dimensions, cross‐correlation to find complementary impulses and root estimation via polynomial fit to calculate the TOF in time domain. The analysis results of laboratory‐scale validation trials (demineralized, vented water) and of real process data (yeast propagation) are presented. In spite of the stable algorithm performance in the lab scale, the algorithm fails in a few cases of real process signals. The relevant signals and corresponding causes for failure were analyzed and future strategies for algorithm enhancement are discussed. Reviewing the results, the aimed USV accuracy of 0.075 m/s can be achieved. The maximum USV error of the used principles and applied methods in the investigated temperature range is ±0.02%.

Abstract. The review presents the fundamental ideas, assumptions and methods of non-invasive density measurements via ultrasound at solid-liquid interface. Since the first investigations in the 1970s there has been steady progress with regard to both the technological and methodical aspects. In particular, the technology in electronics has reached such a high level that industrial applications come within reach. In contrast, the accuracies have increased slowly from 1-2 % to 0.15 % for constant temperatures and to 0.4 % for dynamic temperature changes. The actual work reviews all methodical aspects, and highlights the lack of clarity in major parts of the measurement principle: simplifications in the physical basics, signal generation and signal processing. With respect to process application the accuracy of the temperature measurement and the presence of temperature gradients have been identified as a major source of uncertainty. In terms of analytics the main source of uncertainty is the reflection coefficient, and as a consequence of this, the amplitude accuracy in time or frequency domain.