Huang Y F, Wang G Q, Huang G H, Xiao H N, Chakma A
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China.
Environ Pollut. 2008 Feb;151(3):460-9. doi: 10.1016/j.envpol.2007.04.010. Epub 2007 Jun 4.
To date, there has been little or no research related to process control of subsurface remediation systems. In this study, a framework to develop an integrated process control system for improving remediation efficiencies and reducing operating costs was proposed based on physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks. Process control for enhanced in-situ bioremediation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. Application of the proposed approach to a bioremediation process in a pilot-scale system indicated that it was effective in dynamic optimization and real-time process control of the sophisticated bioremediation systems.
迄今为止，几乎没有与地下修复系统过程控制相关的研究。在本研究中，基于物理和数值模型、逐步聚类分析、非线性优化和人工神经网络，提出了一个开发集成过程控制系统的框架，以提高修复效率并降低运营成本。通过将开发的预测器和优化器与遗传算法和神经网络建模方法相结合，实现了强化原位生物修复的过程控制。将所提出的方法应用于中试规模系统中的生物修复过程表明，它在复杂生物修复系统的动态优化和实时过程控制中是有效的。
