Treatment of colored and real industrial effluents through electrocoagulation using solar energy

Optimizing COD removal from greywater by photoelectro-persulfate process using Box-Behnken design: assessment of effluent quality and electrical energy consumption

Greywater (GW) is a potential source for water reuse in various applications. However, GW treatment is still a vital issue in water reuse in cases of environmental standards and risk to public health. This study investigates optimization and modeling of a hybrid process for COD removal from GW. Persulfate (PS) was simultaneously activated by electrogenerated ferrous ion (EC) and UV to generate sulfate radical. Photoelectro-persulfate (PEPS) was optimized by Box-Behnken design and the effects of four variables (pH, PS dosage, current density, and electrolysis time) were evaluated on COD removal. The results and several coefficients showed that the obtained model was acceptable for predicting the COD removal. Moreover, under optimum conditions (pH = 6.9, PS = 8.8 mM, current density = 2.0 mA/cm(2), and 49.3 min electrolysis time), BOD5, turbidity, TSS, phosphate, and UV254 were effectively removed and COD and BOD5 values reached to discharge standards. Different configurations of the processes were assessed for COD removal. The order of COD removal efficiency followed: PS < Fe(II) < UV/PS ≤ Fe(II)/PS < Fe(II)/PS/UV < electrocoagulation ≤ electrocoagulation/UV < electro-PS < PEPS. The monitoring PS concentration during 60 min reaction time in the aforesaid processes indicated that PEPS could remarkably activate PS. The solution pH was also monitored and related results revealed that the presence of PS during the 10 min first time decreased pH value while production of hydroxide ion at cathode increased pH significantly. Finally, the contribution of electrochemical process in the electrical energy consumption was far less than that of photolysis process in hybrid PEPS process.

Treatment of wastewater by electrocoagulation: a review

The electrocoagulation (EC) process is an electrochemical means of introducing coagulants and removing suspended solids, colloidal material, and metals, as well as other dissolved solids from water and wastewaters. The EC process has been successfully employed in removing pollutants, pesticides, and radionuclides. This process also removes harmful microorganisms. More often during EC operation, direct current is applied and electrode plates are sacrificed (dissolved into solution). The dissolution causes an increased metal concentration in the solution that finally precipitates as oxide precipitates. Due to improved process design and material of construction, the EC process is being widely accepted over other physicochemical processes. Presently, this process has gained attention due to its ability to treat large volume and for its low cost. The aim of this study is to review the mechanism, affecting factors, process, and application of the electrocoagulation process.

Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models

In this research, Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were applied for optimization of Reactive Blue 19 removal using combined electrocoagulation/coagulation process through Multi-Objective Particle Swarm Optimization (MOPSO). By applying RSM, the effects of five independent parameters including applied current, reaction time, initial dye concentration, initial pH and dosage of Poly Aluminum Chloride were studied. According to the RSM results, all the independent parameters are equally important in dye removal efficiency. In addition, ANFIS was applied for dye removal efficiency and operating costs modeling. High R(2) values (≥85%) indicate that the predictions of RSM and ANFIS models are acceptable for both responses. ANFIS was also used in MOPSO for finding the best techno-economical Reactive Blue 19 elimination conditions according to RSM design. Through MOPSO and the selected ANFIS model, Minimum and maximum values of 58.27% and 99.67% dye removal efficiencies were obtained, respectively.