混沌增强,基于优化的可解释分类模型和食品干燥的性能评估
Cagri Kaymak1, Bilal Alatas2, Suna Yildirim3
1Department of Mechatronics Engineering, Faculty of Engineering, Firat University, Elazig 23119, Turkey.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
这项研究使用可解释的人工智能 (XAI) 来优化热空气食品干燥,确定温度和空气速度是有效干燥帕萨梨的关键因素.
科学领域:
- 农业工程 农业工程
- 食品科学 食品科学 食品科学
- 人工智能的人工智能
背景情况:
- 食品干燥对于保存至关重要,但在能源效率和质量维护方面面临挑战.
- 热空气干燥是一种常见的方法,但优化其参数对于像帕沙梨这样的区域梨需要先进的分析.
研究的目的:
- 使用可解释的人工智能 (XAI) 方法分析Paşa梨热空气干燥的实验数据.
- 开发具有自主控制的智能干燥系统,以优化能源效率和产品质量.
主要方法:
- 使用智能干燥系统,配备基于PLC的控制机制,可在50°C/65°C和0.63 m/s/1.03 m/s的温度下工作.
- 应用了振荡混沌向日优化算法 (OCSFO) 来分析多维控制和处理数据.
- 开发了可解释的规则,不需要对数据进行分类,用于性能分类.
主要成果:
- 干燥性能因工作条件而有显著差异,产品质量从450g减少到103g.
- 在将干燥性能分为高,中,低等级方面,OCSFO取得了90%以上的成功.
- 确定干燥温度和空气速度作为影响干燥效率的主导参数.
结论:
- 可解释的人工智能,特别是OCSFO,为理解复杂的干燥动态提供了强大的框架.
- 该研究表明,温度和空气速度对于高效的梨干燥至关重要,能源消耗和机温度起到辅助作用.
- 这种透明的AI方法为优化食品保存过程提供了有价值的工具.
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