Anaerobic Digesters (AD’s) aid the process by which organic matter is degraded to produce a
gas mixture of methane and carbon dioxide by microorganisms. The performance of these
digesters can be controlled by monitoring the variation in the parameters (e.g. pH,
temperature, organic loading rate (OLR), and volatile fatty acids), which drive the process’
metabolic pathways, kinetics and microbial diversity. Any drastic changes in these can make
it difficult to control the feedrate of the digester and adversely affect the biogas production.
However there is a lack of robust statistical control strategies; the majority of existing
automation systems, which provide a base layer of simple regulatory control, are based on
standards from the 1970’s. Perceptive Engineering (PE) wants to address this by presenting
real time information of the parameters in a simple intuitive format, using a process model
that can provide optimal control of a small-scale AD.