Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network

• Published in: Journal of Central South University Vol. 26; no. 1; pp. 63 - 74
• Main Authors: Chen, Ning, Zhou, Jia-qi, Peng, Jun-jie, Gui, Wei-hua, Dai, Jia-yang
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.01.2019; Springer Nature B.V
• Subjects: Chemical reactions; Computer simulation; Delay; Engineering; Gray scale; Historical metallurgy; Iron; Machine learning; Metallic Materials; Nonlinearity; Organic chemistry; Oxidation; Terminal constraints; Uncertainty; time-delay fuzzy gray cognitive network (T-FGCN); iron precipitation process; 时滞; nonlinear Hebbian learning; 非线性Hebbian 学习; 模糊灰色认知网络; 沉铁过程
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-019-3982-1

The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions, such as oxidation reaction, hydrolysis reaction and neutralization reaction. It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity, uncertainty and time-delay. A modeling method based on time-delay fuzzy gray cognitive network (T-FGCN) for the goethite iron precipitation process was proposed in this paper. On the basis of the process mechanism, experts’ practical experience and historical data, the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning (NHL) algorithm with terminal constraints. By analyzing the system in uncertain environment of varying degrees, in the environment of high uncertainty, the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.