Department of Electrical Engineering and Computer Sciences, Cinvestav del IPN, Unidad Guadalajara, Av. Cientifica 1145, Col El Bajio, Zapopan, Jalisco 45015, Mexico.
Int J Neural Syst. 2010 Feb;20(1):75-86. doi: 10.1142/S0129065710002267.
In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.
本文提出了一种用于厌氧过程的递归高阶神经网络观测器 (RHONO)。主要目标是估计完全搅拌釜式反应器 (CSTR) 中甲烷生成的变量：生物质、底物和无机碳。递归高阶神经网络 (RHONN) 结构基于双曲正切作为激活函数。学习算法基于扩展卡尔曼滤波器 (EKF)。通过仿真验证了所提出方案的适用性。还包括使用实验室规模过程的实际数据进行验证。因此，该观测器可成功用于控制目的。
