基于改进INFO-Bi-LSTM模型的SO_(2)排放质量浓度预测

Prediction of SO_(2) Emission Mass Concentration Based on Improved INFO-Bi-LSTM Algorithm

针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。

In view of the problem that it is difficult to accurately predict the SO_(2) emission mass concentration of thermal power units due to numerous influencing factors,a combined model named as improved INFO-Bi-LSTM model was proposed with the combination of improved weighted mean of vectors(INFO)algorithm and bi-directional long short term memory(Bi-LSTM)neural network.The high quality initial population was generated by adopting Circle chaotic mapping and reverse learning,while the ability of jumping from local optimal solution and global searching of INFO algorithm was improved with the application of adaptive t-distribution.Improved INFO-Bi-LSTM model and several other prediction models for a combined desulfurization process inside and outside the furnace were selected to predict the SO_(2) emission concentrations under four typical conditions,after which,verifications and comparisons were conducted on the prediction results.Results show that,the optimization ability of INFO algorithm is improved,while improved INFO-Bi-LSTM model has a higher accuracy,and which is more suitable for the application of SO_(2) mass concentration prediction.This can provide a reference for control theory in flue gas desulfurization process under variable conditions.