Biohydrogen generation from distillery effluent using baffled up-flow microbial electrolysis cell.

Microbial electrolysis cell (MEC) is gaining importance not only for effectively treating wastewater but also for producing hydrogen. The up-flow microbial electrolysis cell (UPMEC) is an innovative approach to enhance the efficiency, and substrate degradation. In this study, a baffled UPMEC with an anode divided into three regions by inserting the baffle (sieve) plates at varying distances from the cathode was designed. The effect of process parameters, such as flow rate (10, 15, and 20 mL/min), electrode area (50, 100, and 150 cm2), and catholyte buffer concentration (50, 100, and 150 mM) were investigated using distillery wastewater as substrate. The experimental results showed a maximum of 0.6837 ± 0.02 mmol/L biohydrogen at 150 mM buffer, with 49 ± 1.0% COD reduction using an electrode of area 150 cm2. The maximum current density was 1335.94 mA/m2 for the flow rate of 15 mL/min and surface area of 150 cm2. The results showed that at optimized flow rate and buffer concentration, maximum hydrogen production and effective treatment of wastewater were achieved in the baffled UPMEC. PRACTITIONER POINTS: Biohydrogen production from distillery wastewater was investigated in a baffled UPMEC. Flowrate, concentration and electrode areas significantly influenced the hydrogen production. Maximum hydrogen (0.6837±0.02mmol/L.day) production and COD reduction (49±1.0%) was achieved at 15 mL/min. Highest CHR of 95.37±1.9 % and OHR of 4.6±0.09 % was observed at 150 mM buffer concentration.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks.

Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m2h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R2 - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.