综合循环发电厂的可持续运行使用基于人工智能的功率预测
Adeel Asghar1, Tahir Abdul Hussain Ratlamwala1, Khurram Kamal1
1National University of Sciences and Technology (NUST), Karachi, Pakistan.
Heliyon
|October 9, 2023
概括
这项研究预测了使用人工智能 (反向传播神经网络) 的联合循环发电厂 (CCPP) 发电. 结果显示,AI模型准确地估计了CCPP输出,为复杂的热力学计算提供了替代方案.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能在发电中的作用
- 热力学和发电厂的性能
背景情况:
- 联合循环发电厂 (CCP) 提供高效率和低排放,但需要准确的容量预测.
- 预测发电能力对于有效投资和中央对手的运营至关重要.
- 人工智能 (AI) 为准确的功率输出预测提供了一个潜在的解决方案.
研究的目的:
- 使用反向传播神经网络 (BPNN) 预测747兆瓦联合循环发电厂 (CCPP) 的输出功率.
- 将AI预测模型的准确性与实际CCPP操作数据和热力学模型进行比较.
- 调查运行参数和燃料类型对CCPP性能的影响.
主要方法:
- 开发和培训一个反向传播神经网络 (BPNN) 模型,使用环境温度,压力和燃料质量流速等输入特征.
- 使用EES创建热力学模型,以进行详细的能源分析和比较.
- 参数研究,以评估运行条件和燃料特性 (例如,低加热值) 对发电的影响.
主要成果:
- 具有10个隐藏神经元的BPNN模型在验证数据集上实现了0.0063237的平均平方误差 (MSE).
- 实际,预测和模拟模型显示热效率分别为27.541%,28.238%和28.201%.
- 实际,预测和模拟模型分别产生了667.32兆瓦,683.48兆瓦和683.16兆瓦,预测值与模拟值紧密相匹配.
结论:
- 人工智能驱动的预测模型,特别是BPNN,在估计CCPP发电时表现出高准确度.
- 整个燃气轮机的温度显著影响CCPP的性能.
- 燃料的低加热值 (LHV) 是最大化功率输出的关键因素,这表明AI可以指导燃料选择和操作调整.
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