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Structural and parametric identification of the model for the process of obtaining hop extract at the rotary pulsation machine

机译:用于在旋转脉动机上获取跳跃提取物过程的模型的结构和参数识别

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摘要

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.
机译:以传统方式跳跃需要大的能量,时间和原料支出。作者提出了一种新的啤酒麦汁跳跃方式,它允许更有效地使用原料植物材料,加剧过程,降低功耗。该方法需要在旋转脉动机(RPM)中获得的跳跃提取物跳跃啤酒麦克风。啤酒花提取物具有较高含量的isohumulone-跳跃麦芽汁中苦味物质的主要成分。 RPM中技术过程的数学建模将允许获得Isohumulone提取的最佳结果。尽管现有的研究,RPM动力学中的过程可靠地测量过程仍然模糊不清;因此,RPM中的动态过程的问题是目前的兴趣。本文介绍了技术过程的结构和参数识别中的持续分数的非传统算法装置,这使得构建具有最小数量的实验数据的跳跃提取物的动态模型,并且没有先验信息。发现获得跳跃提取物的过程的实验数据近似于具有可变系数的第一阶的非周期性链路的加速特性。允许的模型在给定介质的给定介质的给定限制内选择最佳技术参数,其中异腔输出最大。建模误差为0.6%-6%,可用于工程计算(<10%)。建议的方法不需要选择试验模型或进行众多测量。

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  • 来源
    《Journal of Food Processing and Preservation》 |2020年第7期|e14546.1-e14546.10|共10页
  • 作者单位

    Department of Applied Mathematics Institute of Fundamental Sciences Kemerovo State University Kemerovo Russi;

    Department of Technological Design of Food Production Institute of Engineering Technology Kemerovo State University Kemerovo Russia;

    Department of Applied Mathematics Institute of Fundamental Sciences Kemerovo State University Kemerovo Russi;

    Department of Technological Design of Food Production Institute of Engineering Technology Kemerovo State University Kemerovo Russia;

    Department of Applied Mathematics Institute of Fundamental Sciences Kemerovo State University Kemerovo Russi;

    Department of Technological Design of Food Production Institute of Engineering Technology Kemerovo State University Kemerovo Russia;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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