CAJ | 학술논문

针对烟气制酸中的酸浓在线难以准确检测的问题,提出了一种基于软测量技术的在线检测酸浓方法。通过对现场生产数据的采集和预处理,建立了基于径向基函数神经网络的软测量模型;再利用实验平台对该模型进行了验证。实验结果表明：该模型可以得到较精准的硫酸质量分数,能有效地指导生产,具有良好的应用前景。
침대연기제산중적산농재선난이준학검측적문제,제출료일충기우연측량기술적재선검측산농방법。통과대현장생산수거적채집화예처리,건립료기우경향기함수신경망락적연측량모형;재이용실험평태대해모형진행료험증。실험결과표명：해모형가이득도교정준적류산질량분수,능유효지지도생산,구유량호적응용전경。
A new detecting method based on soft sensor technology is introduced to solve the problem of sulfuric acid concentration on-line detection. The method completes on-site data acquisition and data preprocessing, establishes the soft-sensor model based on RBF neural network and verifies the model on experimental platform. The result shows that the model obtains accurate sulfuric acid mass fraction, which guides production effectively and has good application prospect.