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Hybrid neural network modeling of a full‐scale industrial wastewater treatment process
Author(s) -
Lee Dae Sung,
Jeon Che Ok,
Park Jong Moon,
Chang Kun Soo
Publication year - 2002
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.10247
Subject(s) - artificial neural network , process (computing) , computer science , extrapolation , process modeling , work in process , biochemical engineering , process engineering , artificial intelligence , engineering , mathematics , mathematical analysis , operations management , operating system
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. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 670–682, 2002.