神经网络逆软测量方法的拓展及在生物浸出过程中的应用

Improvement of the ANN inversion based soft-sensing method and its application in bioleaching process

在前期工作中,提出了基于"内含传感器"的逆软测量方法,其中逆软仪表的构造仅仅是基于直接可测的状态来实现的。对该方法进行了拓展,首先将用于构造逆软仪表的直接可测量由直接可测的状态拓展为函数变量,然后对逆软仪表的建模算法进行了改进。这种拓展和改进不仅增加了逆软仪表构造成功的可能性,而且可以降低构造逆软仪表所需的直接可测量的导数的阶数,便于工程实现和应用。另外,采用神经网络来逼近理论上存在的逆软仪表,并得到了神经网络逆软仪表,从而解决了解析逆软仪表难以实现的工程应用瓶颈。最后将神经网络逆软仪表应用于生物浸出过程,实现了其不直接可测状态的在线软测量。仿真结果表明神经网络逆软仪表的软测量值与真实值非常接近,从而验证了该方法的有效性。

The soft-sensing method based on ＂assumed inherent sensor＂ inversion（AISI） is presented in our previous work where the AISI as a soft-sensor is constructed only by using the information of directly-measurable state variables.In this paper,the AISI based soft-sensing method is greatly improved by the following two means： firstly,the directly-measurable variables used to construct the AISI are extended from the state variables to the so-called functional variables that are the nonlinear functions of states;secondly,the previous modeling algorithm used to construct the AISI is also improved.These improvements not only significantly increase the probability of the successful construction of the AISI,but also make it possible to lower the orders of the derivatives of the directly measurable variables used to construct the AISI,thereby facilitating its practical use.In addition,a static artificial neural network（ANN） is used to approximate the AISI and then the ANN AISI is obtained,which overcomes the difficulty in constructing the AISI by analytic means,therefore making it more practical in engineering uses.Finally,the improved ANN AISI is applied to the bioleaching process,and the on-line soft-sensing（or estimation） of the directly-immeasurable state variables is achieved.The simulation results show that the estimation values of the ANN AISI well approximate to the actual ones,which verifies the validity of the ANN AISI.