Design of experiments for the optimization of a new process for the stabilization of air pollution control residues

This work is part of the Cosmos-rice project (http://www.cosmos-rice.csmt.eu/), which was funded by the European Union under the Life+ program (LIFE11/ENV/IT000256). The aim of the Cosmos-rice project is to develop and optimise a new process, named Cosmos-rice process, for the stabilisation of Air Pollution Control (APC) residues coming from Municipal Solid Waste Incineration (MSWI) using rice husk ash as stabilizing agent (Bosio et al., 2014). The objective of this work is to present and discuss the optimisation of the Cosmos-rice process parameters by means of the statistical Design of Experiments (DoE) methodology. The DoE refers to the process of planning the experiments so that appropriate data that can be analysed by statistical methods can be collected, resulting in valid and objective conclusions (Montgomery, 2001). In the first part of this work, after a presentation of the Cosmos-rice process, the selection of the appropriate input and output factors (i.e. the variables that can influence the process performance and the variables selected to represent the process performance) is discussed. In particular, the input factors include the percentage of rice husk ash, the mixing temperature and time and the environmental conditions (air temperature and humidity) during the seasoning phase, while the output factors include the concentrations of Pb and Zn in the leachate of the treated APC residues. In the second part, the plan of experiments set up for the optimisation of the Cosmos-rice process is presented. In particular, after showing the levels adopted for each of the input parameters, the resulting plan of 16 experiments is shown. Finally, the results obtained from the experiments, carried out by the Chem4Tech laboratory of the University of Brescia (Italy), are analysed by means of the Multivariate Analysis of Variance (MANOVA). The MANOVA represents an extension of the univariate analysis of variance to the case of multiple dependent variables and allows for a direct test of the null hypothesis with respect to all the dependent variables in an experiment. Thanks to the statistical analysis, the relationships between input and output parameters have been identified, along with the best combination of the input factors levels. In particular, the environmental conditions during the seasoning phase have been found to be the parameter most affecting the effectiveness of the stabilisation process.