Modeling chemical process systems via neural computation

  title={Modeling chemical process systems via neural computation},
  author={N. Bhat and P. A. Minderman and T. J. McAvoy and N. S. Wang},
  journal={IEEE Control Systems Magazine},
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… CONTINUE READING


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