在科威特优化食物垃圾无氧消化:使用人工神经网络的实验见解和经验建模
Jean H El Achkar1, Suad Al Radhwan1, Ahmed M Al-Otaibi2
1Petroleum Engineering Department, College of Engineering, Australian University, West Mishref, Safat, Kuwait.
概括
这项研究利用人工神经网络 (ANN) 和反应堆实验优化了科威特食品废弃物中的甲生产. 在中性范围观察到最佳的生物气产量,ANN的预测与实验数据很好地一致,高达45°C.
科学领域:
- 环境科学 环境科学
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
背景情况:
- 食品废弃物在全球和科威特是一个重大废物管理挑战.
- 无氧消化是一种有前途的技术,可以将食物废弃物转化为可再生能源生物气.
- 优化甲生产需要了解各种操作参数的复杂相互作用.
研究的目的:
- 调查科威特食品废物的无氧消化,以提高甲的生产.
- 开发和验证人工神经网络 (ANN) 模型,用于预测甲产量.
- 通过实验验证,确定生物气产生的最佳运行条件.
主要方法:
- 人工神经网络 (ANN) 建模与70-20-10数据分割 (培训,验证,测试).
- 连续反应堆实验在不同的温度 (35-55°C) 下在特定的有机载荷率 (OLR) 和液压保留时间 (HRT) 下进行.
- 预测ANN的关键参数包括生物质类型,pH,OLR,HRT,温度和反应堆体积.
主要成果:
- 在预测甲百分比时,ANN模型表现出高准确度,R2值超过0.989,用于预测甲百分比.
- 实验结果表明,在介质性温度范围内 (约35-45°C) 产生最佳的甲.
- ANN的预测与高达45°C的实验数据密切匹配,在更高的热友温度 (>50°C) 时观察到偏差.
结论:
- 该研究使用ANN建模和实验验证的混合方法在科威特成功优化了食品废物中的甲生产.
- 这些发现突出显示了中性无氧消化在该地区从食物废弃物中有效产生生物气体的潜力.
- 将ANN等计算模型与实证数据相结合,可以显著提高废物转化能源系统的设计和运行.
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