ABSTRACT
The importance of monitoring the parameters affecting the outcome of the biogas production was discussed in the previous chapter. Their contribution to the variability in the production process hence the uncertainty in biogas production was also explained. This uncertainty is mainly due to the large number of parameters affecting biogas production. Additionally the vast amount of historical/archival and/or real-time data collected throughout the whole anaerobic digestion (AD) production chain needs to be processed and applied in the decision making process for optimal biogas production efficiency. Advanced technologies have enabled the collection of large amounts of data which present the potential to discover useful information and knowledge that could not be accessed before (Ye, 2003). The data reflects the behaviour of the analysed system; therefore there is at least the theoretical potential to obtain useful information and knowledge from these data by the application of the knowledge discovery in databases (KDD) (Han and Kamber, 2001). It consists of various stages including data warehouse (DWH) and data mining (DMN) techniques and refers to the overall process of discovering knowledge from data (Shapiro-Piatetsky et al., 1996; Velickov and Solomatine, 2000; Abonyi and Feil, 2007) (Figure 13.1).
