Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

Abstract

The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.

Optimisation and Modelling of Anaerobic Digestion of Whiskey Distillery/Brewery Wastes after Combined Chemical and Mechanical Pre-Treatment

“…Although total VFA concentration of all samples after AD was so much higher than the initial corresponding values, samples which were seeded with 50% sludge (sample nos. 7,11 in Figure 5) had the lowest final total VFAs values indicating that these particular samples were not affected by the VFA inhibition as much as the others [59]. Therefore, their biogas yields as well as methane percentage of the produced biogas is significantly higher than the samples seeded with lower sludge ( Table 8).…”

Review of Soft Sensors in Anaerobic Digestion Process

“…The convex quadratic programming is solved by the structural risk minimization criterion, which also addresses the high-dimensional and small-sample problems that cannot be solved by artificial neural networks [88]. Given the small-sample problem caused by the difficulty of obtaining target variables in the anaerobic digestion process, Kazemi proposed the soft sensor based on SVR to predict the VFA concentration [89]. The loss function of the soft sensor is expressed as…”

Modeling and Optimization of Anaerobic Digestion: A Review

“…The concentration of VFAs has been widely suggested for the control and monitoring of anaerobic digesters as it is the main methanogenic intermediate also its accumulation in reactors is reliable in indicating process imbalance [8]. It is mostly used to determine the stress level of the [16] system because it can provide specific information for process diagnosis [6,8]. The measurement of biogas flow and composition is very important because they indicate the overall performance of the digester [8].…”

“…In another work, Kazemi and co-authors [16] implemented different data-driven methods including random forest, support vector machine, and genetic programming in order to predict the effluent VFAs. The used data consist of 5 input variables (COD, alkalinity, total soluble solids, biological oxygen demand, and gas flow).…”