Evolutionary polymorphic neural networks in chemical engineering modeling
Department of Chemical Engineering, Chemistry and Environmental Science
Doctor of Philosophy
Loney, Norman W.
Barat, Robert Benedict
Knox, Dana E.
Blackmore, Denis L.
Wasser, Daniel Joseph
Evolutionary Polymorphic Neural Network (EPNN)
Evolutionary Polymorphic Neural Network (EPNN) is a novel approach to modeling chemical, biochemical and physical processes. This approach has its basis in modern artificial intelligence, especially neural networks and evolutionary computing. EPNN can perform networked symbolic regressions for input-output data, while providing information about both the structure and complexity of a process during its own evolution.
In this work three different processes are modeled: 1. A dynamic neutralization process. 2. An aqueous two-phase system. 3. Reduction of a biodegradation model. In all three cases, EPNN shows better or at least equal performances over published data than traditional thermodynamics /transport or neural network models. Furthermore, in those cases where traditional modeling parameters are difficult to determine, EPNN can be used as an auxiliary tool to produce equivalent empirical formulae for the target process.
The developed EPNN approach provides alternative methods for efficiently modeling complex, dynamic or steady-state chemical processes. EPNN is capable of producing symbolic empirical formulae for chemical processes, regardless of whether or not traditional thermodynamic models are available or can be applied. The EPNN approach does overcome some of the limitations of traditional thermodynamic /transport models and traditional neural network models.
njit-etd2001-100 (147 pages ~ 5,261 KB pdf)
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Created December 19, 2003