Biogas is produced from anaerobic digestion of organic material using many different types of microorganisms. Methane is the most important gas. The most straightforward method is monitoring gases such as(H2, CH4 and CO2). Fluctutation in these gases is a good measure of a digester’s productivity (Spanjers & Lier, 2006).
Other measures include pH. If the pH is outside the range of 6 to 8 for example, there is fermentation (process) deterioration. Deterioration means loss of methane production and eventually collapse of the fermentation system (Liu et al., 2012).
Concentrations of volatile fatty acids (VFAs; mainly acetic, butyric and propionic acid) have been suggested as useful control parameters. These acids are indirectly linked to fermentation performance as measures of methanogen microorganism growth.
VFA accumulation can be interpreted as organic overload or inhibition of the methanogenic microbial communities (Madsen et al., 2011). Acidogenic microorganisms transform hydrolysis products into VFAs, while acetogenic microorganisms then convert VFAs into acetate, H2 and CO2. Methane is then produced by the methanogens (Gerardi, 2003).
Ideally, a monitoring system must have a set of sensors coupled to a treatment phase within a software program, where the measurements are carried out automatically with limited human intervention or expertise. The data can then be combined with numerical models to update an algorithm diagnosing the state of the digester and detecting erroneous working modes (Bernard et al., 2001).
Optical-based chemical sensors (colourimetric sensors) appear to have the potential of providing such additional crucial information. They are low cost, require relatively simple instrumentation and straightforward sample preparation, and can be integrated within existing control systems. In general, there can frequently be a trade-off between sensitivity and robustness, and their use in bioreactors may be severely challenged by limited selectivity, repeatability, robustness and stability (Peris & Escuder-Gilabert, 2013).
One way is to apply artificial noses and tongues. Each sensor in the array is only partially selective but has distinctive responses to the various chemical entities of interest. Adding a multivariate chemometric tool results in quantitative responses for each entity. A few attempts have been made to apply artificial tongues, based on electrical or electrochemical sensors, for detection during AD (Buczkowska et al., 2010). However, the limited success of these technologies is due to the complex and poorly reproducible composition of process media, resulting in sensor contamination and biofouling.
Bernard, O., Polit, M., Hadj-Sadok, Z., Pengov, M., Dochain, D., Estaben, M., & Labat, P. (2001). Advanced monitoring and control of anaerobic wastewater treatment plants: software sensors and controllers for an anaerobic digester. Water Science and Technology, 43(7), pp. 175-182 (Article).
Gerardi, M. H. (2003). The microbiology of anaerobic digesters. John Wiley & Sons.
Liu, Y., Zhang, Y., Quan, X., Li, Y., Zhao, Z., Meng, X., & Chen, S. (2012). Optimization of anaerobic acidogenesis by adding Fe0 powder to enhance anaerobic wastewater treatment. Chemical Engineering Journal, 192, pp. 179-185 (Article).
Madsen, M., Holm-Nielsen, J. B., & Esbensen, K. H. (2011). Monitoring of anaerobic digestion processes: A review perspective. Renewable and Sustainable Energy Reviews, 15(6), pp. 3141-3155 (Article).
Peris, M., & Escuder-Gilabert, L. (2013). On-line monitoring of food fermentation processes using electronic noses and electronic tongues: a review. Analytica Chimica Acta, 804, pp. 29-36 (Article). .
Spanjers, H., & van Lier, J. B. (2006). Instrumentation in anaerobic treatment–research and practice. Water Science and Technology, 53(4-5), pp. 63-76 (Article) .