Neural modeling and control of a heat exchanger based on SPSA techniques

The aim of the paper is twofold: first, we consider a variation of the first-order simultaneous perturbation stochastic approximation (SPSA) algorithm developed by Spall (1992, 1998) which makes use of several numerical artifices, including adaptive gain sequences, gradient smoothing and a step rejection procedure, to enhance convergence and stability. Second, we present numerical studies on a non-trivial test-example, i.e., the water cooling of sulfuric acid in a two-tank system. This numerical evaluation includes the development of a neural model as well as the design of a model-based predictive neural PID controller.