Assessment of electrooxidation as pre- and post-treatments for improving anaerobic digestion and stabilisation of waste activated sludge.

This study evaluates the effects of electro-oxidation as a means for enhancing sludge stabilisation. Boron-doped diamond electrodes were used to treat waste activated sludge and digestate under different operating parameters (current density, conductivity, pH, and time). Electro-oxidation runs affected the solubilisation of organic matter, which seemed to improve anaerobic digestion and dewaterability characteristics. Among the tested parameters, pre-treating sludge via electro-oxidation under alkaline conditions (Treatment T5) resulted in the highest increase in soluble organic material compared to that in the control, with total organic carbon (TOC) and soluble chemical oxygen demand (COD) values of 2753 and 7819 mg L-1, respectively (control TOC and COD values were 385 and 1073 mg L-1). This pretreatment also achieved a high hydrolysis rate (higher concentration in volatile fatty acids) with a concomitant increase in methane yield (approximately 18%). On the other hand, the application of electro-oxidation as a post-treatment for improving digestate dewaterability resulted in noticeable changes in the release of water during drying due to protein and aliphatic matrix modification of the sample.

A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature (Te) and burnout index (Df) were selected as the responses to be optimised, while the CO2/O2 molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box-Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on Df were successfully described by the Box-Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO2/O2 molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.