工业锅炉水质预测对于保障生产运行的安全和效率至关重要。虽然长短期记忆神经网络(LSTM)在处理大时间尺度数据方面表现出色,但其参数选择并非直接确定,容易受困于局部最优,因此需要对其参数进行优化。本研究旨在提出一种基于麻雀搜索算法(SSA)优化LSTM的水质预测算法。通过模拟麻雀觅食行为,SSA迭代优化LSTM参数,以寻求更优的设置。优化后的LSTM算法被用于处理水质数据,构建了SSA-LSTM预测模型的核心部分。实验结果显示,相较于传统的LSTM和CNN-LSTM模型,在工业锅炉水质预测方面,该算法展现出更高的准确性和可靠性。预测准确率达到99.99%,平均绝对误差(MAE)为0.001940,相对MAE(RMAE)为0.002083。这项研究为工业领域提供了一种有效的水质预测方法,并凸显了SSA-LSTM在时间序列预测任务中的潜在应用价值。

Predicting the water quality of industrial boilers is crucial for ensuring operational safety and efficiency. While Long Short-Term Memory (LSTM) neural networks excel at handling extensive temporal data, their parameter selection is not directly determined and often susceptible to local optima. Hence, optimizing these parameters becomes essential. This study aims to propose a water quality prediction algorithm by leveraging the Sparrow Search Algorithm (SSA) to optimize LSTM.By simulating sparrow foraging behavior, SSA iteratively refines LSTM parameters to seek more optimal configurations. The optimized LSTM algorithm is employed for processing water quality data, forming the core of the SSA-LSTM predictive model. Experimental findings reveal that compared to traditional LSTM and CNN-LSTM models, this algorithm demonstrates higher accuracy and reliability in forecasting industrial boiler water quality. Achieving an accuracy of 99.99%, with a Mean Absolute Error (MAE) of 0.001940 and Relative MAE (RMAE) of 0.002083, this research presents an effective method for water quality prediction in the industrial domain. It underscores the potential application value of SSA-LSTM in time series forecasting tasks within industrial settings.

河南省锅炉压力容器安全检测研究院基本科研业务费支持项目