Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling

assessment, combustion criteria, and the public regulations. Without accurate prediction of PCDD/PCDF emissions, however, reasonable assessment of the health risk and essential appraisal of the combustion criteria or public regulations cannot be achieved. Previous prediction techniques for PCDD/PCDF emissions were limited by the linear models based on a least-square-based analytical framework, such that the inherent non-linear features cannot be explored via advanced system identification techniques. Recent development of genetic algorithms and neural network models has resulted in a dramatic growth of the use of non-linear structure for optimization and prediction analyses. Such approaches with the inherent thinking of artificial intelligence were found useful in this study for the identification of non-linear structure in relation