Publication | Closed Access
Modeling chemical process systems via neural computation
250
Citations
12
References
1990
Year
Chemical KineticsEngineeringMachine LearningNeural Networks (Machine Learning)Recurrent Neural NetworkSocial SciencesBack-propagation NetSystems EngineeringNonlinear ProcessBrain ModelingProcess DesignChemical Process SystemsNonlinear Chemical SystemsNonlinear DynamicsComputer ScienceNeural Networks (Computational Neuroscience)Deep Neural NetworksEvolving Neural NetworkComputational NeuroscienceProcess ControlNeural NetsNeuronal NetworkBiological ComputationProcess Chemistry
The use of neural nets for modeling nonlinear chemical systems is discussed. Three cases are considered: a steady-state reactor, a dynamic pH stirred tank system, and interpretation of biosensor data. In all cases, a back-propagation net is used successfully to model the system. One advantage of neural nets is that they are inherently parallel and, as a result, can solve problems much faster than a serial digit computer. Furthermore, neural nets have the ability to learn. Rather than programming neural computers, one presents them with a series of examples, and from these examples the nets learn the governing relationships involved in the training database.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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