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Applications of neural networks in chemical engineering

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  1. Introduction
  2. Neural networks as simplified models of biological nervous system
  3. The literature review
    1. The theory
    2. Models of artificial neural networks
  4. Materials and methods of the study
  5. Specific remarks
  6. Conclusion
  7. List of nomenclature
  8. References

Neural networks, which are simplified models of the biological nervous system, is a massively parallel distributed processing system made up of highly interconnected neural computing elements that have the ability to learn and there by require knowledge and make it available for use. Neural networks are simplified imitations of the central nervous system, and obviously therefore have been motivated by the kind of computing performed by the human brain. The structural constituents of the human brain termed Neurons are the entities, which perform computations such as cognition, logical inference, pattern recognition?.etc. Hence the technology which has been built on a simplified imitation of computing by neurons of a brain, has been termed Artificial Neural systems technology or Artificial Neural Networks or simply Neural Networks.

[...] Estimation of mass transfer parameters in fast fluidized beds of fine solids: In this study back-propagation, feed-forward neural networks are applied to estimate mass-transfer parameters in fast fluidized needs of fine solids. These networks are trained to predict mass-transfer rates using measurements of the sublimation rate of coarse naphthalene balls in fast fluidized needs of fine glass beads at several solid-to-gas mass flow rates within the relevant superficial gas-velocity range. When tested to predict the effective diffusivities fro a coarse particle to the bulk of the fast bed of fine solids, trained neural networks calculated the Sherwood number with high accuracy. [...]

[...] However the behavior of a artificial neuron can be captured by a simple model as shown in fig1. the model which forms the basis of artificial neural network x1 w1 SUMMATION UNIT x2 w2 x3 w3 xn wn THRESHOLDING UNIT Fig1: Simple model of an Artificial Neuron Here x1,x2,x3 xn are the N-inputs to the artificial neurons, w1,w2,w3 .wn are the weights attached to the input links. Recollect that a biological receives all inputs through the dendrites, sum them and produces an output if the sum is greater than the threshold value. [...]

[...] MIMO continuous systems: The extension of use of RNN in modeling continuous MIMO systems is straight forward. As an example a non-isothermal CSTR(fig) with a first order reaction A B is considered. The variables Fi,CAi and Ti are considered as the input of the system while CA, and T are the outputs. After training the RNN output data of CA, and T based on the corresponding inputs are given in the figure: Fig8: Non Isothermal CSTR Fig9: Desired(-) and RNN( ) outputs after completion of dynamic training for nonisothermal CSTR. [...]

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