摘要
针对目前在钨碱煮浸出过程中,难以实现WO3浸出率的在线检测,依据钨矿碱煮浸出过程的化学反应机理以及影响浸出率的因素,提出软测量方案,采用人工神经网络建模方法,建立简便快速检测的软测量模型.根据工业现场采集的样本数据,运用Matlab工具训练好神经网络软测量模型,结果显示该软测量模型能够反应矿物浸出过程的实际状况,测量相对误差均值小于0.5%,能满足工业生产要求.该研究为实现钨浸出率的在线检测提供了一种新的方法.
The online detection of WO3 Leaching Rate in tungsten hydrometallurgy is difficult to accomplish. A soft measurement plan with a convenient and fast detecting soft measurement model is devised by using MATLAB and the data collected from industrial field train neural network according to the chemical reaction mechanism of tungsten alkali hot leaching and the factors which influenced the leaching rate. Simulation results manifested the soft measurement model could well reflect actual reaction situation. The measuring relative error is less than 0.5%. The model fit the industrial requirement. This research provides a new method for online detection of tungsten leaching rate.
出处
《有色金属科学与工程》
CAS
2013年第5期117-121,共5页
Nonferrous Metals Science and Engineering
基金
国家自然科学基金资助项目(61364014)
江西省对外科技合作项目(2010EHA01400)
关键词
钨湿法冶炼
神经网络
浸出率
软测量
tungsten hydrometallurgy
neural network
leaching rate
soft measurement
