Operational Predictive Model for a Municipal Waste Incinerator: A Spanish Case Study

This paper describes a study of operational parameters by using the multivariate data analysis and neural networks for a municipal waste incinerator located in Majorca (Spain). The basis of the study also includes the chemometric techniques: linear multivariate regression to develop a model with certain predictive capabilities; linear principal component analysis, which allow the number of variables to be reduced from 17 to 4, thus fostering visualization in a low-dimension space; and linear discriminant analysis to categorize plant data accordingto the month (probability ≈ 70%). Neural network predictive capability was good, with relative errors around 6-8%. These techniques allow all the variables to be analysed simultaneously and focus on the variables which have a significant impact. In this way, the interrelationships between sets of variables, causal relations among input/output variables, seasonal motivated deviations as well as observation variations have been identified.