In spite of continuing advances in optimal solution techniques for optimization and control problems, many practical combinatorial problems remain too large or too complex to be solved by these known techniques. Thus, a heuristic approach (Neural Network Model) is often the only viable alternative. Neural Network Models offer the most unified approach to building truly intelligent systems which can provide good optimal solution for many applications. In this work we propose a hybrid (Kalkoh) neural network algorithm which is being used to model and solve the continuous stirred tank mixer (CSTM) problem. The hybrid algorithm is robust and converges fast without being trapped into a local minimal as is the case with the popular back-propagation neural network. The characteristic equations governing the dynamics of the Continuous Stirred Tank Mixer/Reactor and the controller were formulated and tested and found to be consistently stable.

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