用数字孪生技术优化超声波啤酒发酵，提升酵母生长效率。
A Proof of Concept for a Digital Twin of an Ultrasonic Fermentation System
- 基于温控、超声频率与占空比构建酵母密度预测模型。
- 在样本有限情况下仍实现高精度发酵过程预测。
- 适合智能酿造系统研发人员参考应用。
本文提出并实现了一种用于创新超声波增强啤酒发酵系统的概念验证型数字孪生。传统发酵罐配备压电换能器，通过发射超声波对酵母生长环境施加外部非生物刺激，以加速发酵过程。数字孪生核心为一个预测模型，可根据环境条件（温度、超声频率、占空比）估计酵母培养密度随时间的变化。我们改进并扩展了Palacios等人提出的模型，在训练样本有限的情况下仍能有效建模。实验结果及模型性能评估验证了该方法的可行性。
原文摘要 · Abstract (English)
This paper presents the design and implementation of a proof of concept digital twin for an innovative ultrasonic-enhanced beer-fermentation system, developed to enable intelligent monitoring, prediction, and actuation in yeast-growth environments. A traditional fermentation tank is equipped with a piezoelectric transducer able to irradiate the tank with ultrasonic waves, providing an external abiotic stimulus to enhance the growth of yeast and accelerate the fermentation process. At its core, the digital twin incorporates a predictive model that estimates yeast's culture density over time based on the surrounding environmental conditions. To this end, we implement, tailor and extend the model proposed in Palacios et al., allowing us to effectively handle the limited number of available training samples by using temperature, ultrasonic frequency, and duty cycle as inputs. The results obtained along with the assessment of model performance demonstrate the feasibility of the proposed approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。