Combining Neural Networks with Physical Model for Plant Simulation

Abstract

In waste incineration plants, a waste quality is very difficult to be identified even using many sensors. Thus, it is an important subject to build a simulation in the presence of uncertainty. An effective modeling technique that combined neural networks with physical systems is proposed for estimating future steam temperatures in a boiler system. The physical systems incorporate available prior knowledge about the process being modeled, while the neural networks compensate errors of unmeasured process that are difficult to model with the physical systems. Experimental results for real plant data show that the combined model is able to interpolate and extrapolate much more accurately and better than a neural network only model. The model requires significantly fewer training examples and is easier to analyze and interpret the trained model.