Wort hopping in a traditional way requires large energy, time, and raw material expenditure. The authors suggest a new way of beer wort hopping which allows using raw plant material more effectively, intensifying the process, and reducing power consumption. The method entails beer wort hopping with hop extract obtained in the rotary pulsation machine (RPM). Hop extract has a higher content of isohumulone—the main component of bitter substances in the hopped wort. Mathematical modeling of technological processes in RPM will allow obtaining the best results of isohumulone extraction. Despite the existing research, reliable measurement of the processes in RPM kinetics remains vague and understudied; therefore, the issues of dynamic processes modeling in RPM are of current interest. This paper introduces the unconventional algorithmic apparatus of continued fractions in the structural and parametric identification of the technological process, which allowed building a dynamic model of obtaining hop extract with a minimum amount of experimental data and no a priori information. The experimental data for the process of obtaining hop extract were found to approximate with acceleration characteristics of the aperiodic link of the first order with variable coefficients. The models allowed choosing the optimal technological parameters within the given limits for the treated medium, at which the isohumulone output is maximum. Modeling errors are 0.6%–6%, which is acceptable for engineering calculations (<10%). The suggested approach does not require selecting trial models or making numerous measurements.

中文翻译：

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用于在旋转脉动机上获得啤酒花提取物的过程的模型的结构和参数识别

传统的麦芽汁跳跃需要大量的能量，时间和原材料费用。作者提出了一种啤酒花麦芽汁跳跃的新方法，该方法可以更有效地利用植物原料，强化加工过程，并降低功耗。该方法需要用在旋转脉动仪（RPM）中获得的啤酒花提取物来啤酒花麦芽汁。啤酒花提取物具有较高的异hu草酮含量，这是啤酒花麦芽汁中苦味物质的主要成分。RPM中工艺过程的数学建模将使获得异hu草酮提取的最佳结果成为可能。尽管已有研究，但对RPM动力学过程的可靠测量仍然含糊不清且研究不足。因此，RPM中动态过程建模的问题是当前关注的问题。本文介绍了一种在工艺过程的结构和参数识别中采用连续分数的非常规算法装置，该方法可以建立一个动态模型，以最少的实验数据和无先验信息获得啤酒花提取物。发现用于获得啤酒花提取物的过程的实验数据近似于具有可变系数的一阶非周期性链接的加速特性。该模型允许在给定的限度内为处理过的培养基选择最佳工艺参数，在该范围内异hu草酮的产量最大。建模误差为0.6％–6％，对于工程计算而言是可以接受的（<10％）。建议的方法不需要选择试验模型或进行大量测量。