• pH: Monitoring pH is very important since it affects the microorganisms' activity and can be correlated with changes in acids and bases as well as activity. However, it cannot be used to evaluate the state of the system since it is affected by the buffer capacity of the liquid (determined mostly by the bicarbonate, ammonia, and volatile fatty acids).

• Alkalinity: It is distinguished in total and bicarbonate alkalinity. Total alkalinity is measured through titration to pH 3.7 and expresses the capacity of an anaerobic system to maintain the pH under acidification (Powell and Archer, 1989). However, total alkalinity increases as the VFA concentration increases. Therefore, the bicarbonate alkalinity, measured through titration to 5.75, can reflect the effective buffer capacity of the system. Various methods have been developed for the online measurement of the bicarbonate alkalinity (Table 10.3).

• Organic matter: Common parameters such as the total and volatile solids, chemical oxygen demand, total organic matter, and biochemical methane potential (preferable to biological oxygen demand in the case of anaerobic systems) express the aggregate organic matter present in a digester and, correlated with the organic matter of the influent, give an accurate estimate of the organic matter removal. However, these are time-consuming, offline measurements, except for the total organic carbon method which can be applied online in the case of anaerobic systems with low solid content (Table 10.3).

• Biogas flow: The biogas production rate, and especially the performance in methane, is the most commonly used measurement to detect the process stability. A reduction in the biogas production rate usually suggests that the volatile fatty acids have been accumulated as a result of overloading or the presence of a toxicant. However, any change in this parameter is caused by process instability and cannot be an early warning, that is, it is not sensitive enough.

• Biogas composition: The principal gases in the headspace of an anaerobic digester are CO₂ and CH₄. When CO₂ increases relatively in proportion to CH₄, process imbalance has already evolved and, consequently, this index cannot be used as an early indicator. On the other hand, CO₂ in the gas phase is influenced by changes in alkalinity and pH in the bioreactor, and as a result when pH control is applied in low buffered systems, changes in its value do not reflect process instability (Ryhiner et al., 1992). Hydrogen is another significant intermediate compound, which regulates the performance of the acetogens, used for the detection of an upcoming imbalance (Molina et al., 2009). Measuring anions and cations produced or consumed as a result of the metabolic hydrogen in the headspace does not correspond to the actual concentration sensed by the microorganisms which are in the aqueous phase. Thus, the measurement of dissolved hydrogen is suggested as a more reliable index (Frigon and Guiot, 1995). Accumulation of hydrogen leads to VFA accumulation due to thermodynamic limitations of acetogenesis. Its concentration should be kept lower than 40 nM (which corresponds to a partial pressure less than 6 Pa at 35°C). Hydrogen sulfide and carbon monoxide can also be detected but they are not frequently used for process control.

    Although the above-mentioned parameters need to be monitored and adjusted, they cannot be used as early indicators of process imbalance because they will not provide information about the complex biochemical reactions that occur in the AD process. It is crucial to monitor and control the early process indicators such as VFA concentration and biogas composition to have a comprehensive insight of the microbiological dynamics of the AD before a process imbalance happens.

• Volatile fatty acids (VFAs): These are the most important intermediate compounds in anaerobic digestion since their accumulation leads to pH decrease, stressing the methanogens further. The increase in acetate concentration under overload conditions does not indicate necessarily process imbalance if the biogas production rate has also increased. In this case, the system may operate at a higher acetate concentration at a new steady state, without rejecting the possibility of process failure. However, propionate and butyrate accumulation denote signs of imbalance since it usually happens when the hydrogen concentration increases. Propionate is accumulated first, since its conversion requires a six times lower concentration of hydrogen than butyrate (Ozturk, 1991). Therefore propionate has been suggested as a suitable indicator for process imbalance along with butyrate, the ratio of propionate to butyrate, and the iso forms of butyrate and valerate (Boe et al., 2008). Depending on the metabolic pathways prevailing in an anaerobic bioreactor, VFAs may be formed at various concentrations and there cannot be a rule of thumb for a “safe” level of VFAs securing stable operation. For example, Pullammanappallil et al. (2001) found that operation of a controlled, glucose-fed bioreactor in the presence of phenol remained stable at a high propionate concentration (2750 mg/L). Moreover, the inhibition of VFAs is pH-dependent and their inhibitory effect increases at pH values ranging from 6 to 7.5.

    VFA concentrations and biogas composition are generally analyzed offline by chromatographic methods (gas chromatography (GC)) and Headspace GC in large-scale biogas plants and research laboratories, which require technically more complex analytical systems and well-trained employees (Vanrolleghem and Lee, 2003). They can also be monitored spectroscopically, electrochemically and by some other (mass spectrometry and titration) methods (Madsen et al., 2011). However, most of these analyses require sample preparation (Holm-Nielsen et al., 2006). Taking representative samples from the digester is a difficult task and leads to sampling errors due to the highly heterogeneous and viscous nature of the AD medium. Therefore, process parameters should be monitored online to prevent experimental/sampling errors and human interference. Moreover, it would be easier to detect the sudden changes and predict any possible problems on time by online monitoring.

    The main problems in online monitoring are sample preparation and fouling of the sensors, which make most of the analytical methods inapplicable for online detection (Falk, 2012). However, recent advances in process analytical technologies using, eg, spectroscopic and electrochemical measurement principles, provide online monitoring and deciphering of the complex bioconversion processes. Most of the online process monitoring systems measure the overall process signals which are related to the mixture of different parameters. With the help of the chemometric multivariate data analysis techniques, these promising online process monitoring systems bring the AD process monitoring and control to a more reliable and effective direction.

    At present, online methods have not been frequently utilized in biogas plants. Still, infrared and near-infrared spectroscopies were shown to be able to monitor VFA, COD, and TOC concentrations simultaneously in industrial and lab-scale digesters (Holm-Nielsen et al., 2008; Spanjers et al., 2006; Steyer et al., 2002). To achieve this, an ultrafiltration unit is generally included to the system to provide clear liquid free of particles. Besides the ultrafiltration, gas bubbles also interfere with the reading in spectroscopy, therefore it is recommended to use debubblers or macerators to obtain clear, particle- and bubble-free samples (Madsen et al., 2011). The online monitoring systems should be calibrated using the samples from the system in long-term operations. It is important to calibrate the spectrometer using the samples itself; the calibration with individual standards would not provide a feasible curve because the medium in the digester has complex chemical components. Besides sampling and calibration, multivariate data analysis (ie, chemometrics) is a critical factor to obtain proper readings by considering the chemical interferences in the reactor. This is done by delivering the full spectra obtained from one or more process analyzers to the data interpreter in order to be analyzed, correlated, and interpreted. Then, the prediction model is estimated by the use of external validation. Although such controlling systems seem complex at present, these technologies will be improved on and simplified in the near future.

• Metabolic activity: The physicochemical parameters available for measurement respond to changes in the metabolic activity of the anaerobic microorganisms, but the correlation is not always direct. Since the success of a control scheme applied on anaerobic systems is based on directing the microbial activity to the desired performance, its assessment is very important. The microbial activity can be evaluated through measurement of the specific methanogenic activity, application of molecular techniques (for the qualitative and quantitative detection of specific microorganisms based on the DNA and RNA probing) and detection of changes in cellular components such as enzymes (NADH and coenzyme F420), ATP and phospholipid fatty acids (Fountoulakis et al., 2004; Montero et al., 2009; Nordberg et al., 2000). Moreover measurements of the activity of certain enzymes and application of microcalorimetry (heat released in an anaerobic ecosystem which can be correlated to the size of the microbial population, the metabolic state, and activity) have also been used for monitoring (Switzenbaum et al., 1990). Since most of the analytical procedures required for assessing the metabolic activity are elaborate and time-consuming or require samples of low solid content, the utilization of these measurements is limited for online control, but can be used offline to give a better insight to the system status.