Lee Dae Sung, Jeon Che Ok, Park Jong Moon, Chang Kun Soo
Department of Chemical Engineering, School of Environmental Science and Engineering, POSTECH, San 31, Hyoja-dong, Pohang, Kyungbuk 790-784, Korea.
Biotechnol Bioeng. 2002 Jun 20;78(6):670-82. doi: 10.1002/bit.10247.
In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.
近年来，将机理模型和神经网络模型相结合的混合神经网络方法受到了广泛关注。通过以一种使神经网络模型能够恰当考虑机理模型中未知和非线性部分的方式，将机理模型与神经网络模型相结合，这些方法在获得更准确的过程动态预测方面可能非常有效。在这项工作中，选择了一个全尺寸的焦化厂废水处理过程作为模型系统。首先，通过主成分分析对实际运行数据进行了过程数据分析。接下来，分别基于特定的过程知识和焦化厂废水处理过程的运行数据，开发了一个简化的机理模型和一个神经网络模型。最后，将神经网络以并行和串行配置纳入机理模型。模拟结果表明，即使在例如有毒化合物冲击负荷导致过程扰动的情况下，与其他建模方法相比，并行混合建模方法仍能实现更准确的预测，并具有良好的外推特性。这些结果表明，在缺乏其他合理准确的过程模型的情况下，并行混合神经建模方法是一种用于生物化学过程准确且经济高效建模的有用工具。
