First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method

We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. These include the kinetics and thermal effects of the limestone decomposition as well as the heat transfer between the gaseous and solid phases. The model consists of two coupled equation systems for the upper and lower part of the kiln. The results of the model are validated qualitatively and are used to train an artificial neural network that predicts the conversion and the temperature in the crossover channel. The artificial neural network performs very well with values of the root mean squared error that are two to three orders of magnitudes lower than the range covered within the data. Finally, we use a genetic algorithm to optimize the feed mass flows such that the conversion and the fuel efficiency are improved in a Pareto-optimal manner. The results are compared to those of a gradient-based optimization method, which shows the usefulness and validity of the approach with the genetic algorithm.