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This work deals with the degradation of phenol based on the classical Fenton process, which is enhanced by the presence of chelating agents. Several iron-chelating agents such as ethylenediaminetetraacetic acid (EDTA), nitrilotriacetic acid (NTA), diethylenetriamine pentaacetic acid (DTPA), and ethylenediamine-N,N’-diacetic acid (EDDA) were explored, although particular attention was given to EDTA. The effect of the molar ligand to iron ratio, EDTA:Fe, initial pH, and temperature on the oxidation process was studied. The results demonstrate that the proposed alternative approach allows the capacity for degrading phenol to be extended from the usual acidic pH (around 3.0) to circumneutral pH range (6.5–7.5). The overall feasibility of the process depends on the concentration of the chelating agent and the initial pH of the solution. The maximum phenol conversion, over 95%, is achieved using a 0.3 to 1 molar ratio of EDTA:Fe, stoichiometric ratio of H2O2 at an initial pH of 7.0, and a temperature of 30 °C after 2 hours of reaction, whereas only 10% of phenol conversion is obtained without EDTA. However, in excess of ligand (EDTA:Fe > 1), the generation of radicals seems to be strongly suppressed. Improvement of the phenol removal efficiency at neutral pH also occurs for the other chelating agents tested.

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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.

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