title: Hybrid neural network modeling of a full-scale industrial wastewater treatment process.
abstract: 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.
authors: Department of Chemical Engineering, School of Environmental Science and Engineering, POSTECH, San 31, Hyoja-dong, Pohang, Kyungbuk 790-784, Korea.
authors: Dae Sung
authors: DS
authors: Lee
authors: Che Ok
authors: CO
authors: Jeon
authors: Jong Moon
authors: JM
authors: Park
authors: Kun Soo
authors: KS
authors: Chang
mesh_terms: D001673:Biodegradation, Environmental; D003077:Coke; D007220:Industrial Waste; D007723:Korea; D016571:Neural Networks, Computer; D012498:Sanitary Engineering; D012722:Sewage / Q000382:microbiology; D014865:Waste Disposal, Fluid / Q000379:methods* / Q000706:statistics & numerical data; D014874:Water Pollutants, Chemical
chemical_list: D003077:Coke; D007220:Industrial Waste; D012722:Sewage; D014874:Water Pollutants, Chemical