在小样本条件下,从厨房废物的无氧消化中预测甲产生的方法
Shipin Yang1, Yuqiao Cai1, Tingting Zhao1
1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, Jiangsu, China.
概括
本研究引入了一种新的数据驱动方法,用于模拟无氧消化 (AD) 甲生产,使用有限的数据. 该方法增强了小型数据集,并提高了有机废物处理的预测准确性.
科学领域:
- 环境科学 环境科学
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 无氧消化 (AD) 有效地处理有机废物,但缺乏准确的机械模型.
- 数据驱动型建模提供了一个解决方案,但传统方法在小样本大小方面存在困难.
- 精确的建模对于优化AD过程和甲生产至关重要.
研究的目的:
- 在小样本场景中开发一种高精度,数据驱动的模拟方法,用于无氧消化 (AD) 甲生产.
- 在处理稀缺数据时,解决传统数据驱动方法的局限性.
- 创建一个强大的数学模型来预测在公元后的甲生成.
主要方法:
- 利用TimeGAN (时间序列生成对抗网络) 来增强小样本AD甲生产数据的数据.
- 设计了一种新的混合内核极端学习机器 (HKELM),用于改进数据驱动模型结构.
- 使用Sparrow搜索算法 (SSA) 优化了HKELM规范化系数,创建了SSA-HKELM模型.
- 用增强数据训练了SSA-HKELM模型,以建立最终的TimeGAN-SSA-HKELM模型.
主要成果:
- 拟议的TimeGAN-SSA-HKELM模型证明了对AD甲生产的高精度数据驱动建模的有效性.
- 与现有方法相比,比较实验显示,甲每日生产预测错误减少.
- 该方法成功增强了有限的高质量数据,克服了AD建模中的一个关键挑战.
结论:
- 开发的TimeGAN-SSA-HKELM方法提供了一种有效的解决方案,用于以小样本大小为基础的无氧消化甲生产的数据驱动建模.
- 这种方法为AD过程的预测准确性提供了显著的改进.
- 该方法具有适应性,可以应用于其他需要使用有限数据集进行数据驱动建模的场景.
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