基于ASOS-ELM的湿式球磨机负荷软测量方法

针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。

Soft Measurement Method of Wet Ball Mill Load Based on ASOS-ELM

Aim to solve the problem that the internal load of a wet ball mill is difficult to be accurately predicted by expert experience in the grinding process, a soft measurement method of grinding load based on ameliorated symbiotic organisms search (ASOS)-extreme learning machine (ELM) is proposed. Firstly, the soft measurement model of mill load is established by the ELM algorithm. Symbiotic organisms search (ASOS) algorithm is used to optimize the hidden layer parameters of soft sensor model. The characteristic vector of mill load is constructed with the characteristic information of cylinder vibration and vibration sound signal. The mill load parameter is the output. The grinding load test experiment and comparative analysis show that the load parameter of the mill load soft measurement model has higher prediction accuracy and higher generalization ability, which provides useful guidance for the improvement of mill grinding efficiency and control optimization.