Treatment of wastewater from the dairy industry using electroflocculation and solid whey recovery

A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH3-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.

Chemically enhanced primary treatment of dairy wastewater using chitosan obtained from shrimp wastes: optimization using a Doehlert matrix design

Dairy operations generate large volumes of polluted wastewater that require treatment prior to discharge. Chemically enhanced primary treatment (CEPT) is a widely utilized wastewater treatment strategy; but it requires the use of non-biodegradable coagulants that can lead to toxic-byproducts. In this study, chitin from shrimp shell waste is extracted and converted into chitosan. Chitosan was demonstrated to be a natural, low-cost alternative coagulant compatible with the CEPT. Following treatment, dissolved air flotation allowed for the removal of turbidity, COD, and UV₂₅₄ from the synthetic dairy effluent (SDE). Doehlert matrix was used to optimize the chitosan dosage and pH of the CEPT; as well as to model the process. The mechanisms behind the coagulation-flocculation were revealed using zeta potential analysis. FTIR spectroscopy was utilized to confirm the functional groups present on the chitosan. Chitosan with a degree of deacetylation equal to 81% was obtained. A chitosan dose of 73.34 mg/L at pH 5.00 was found to be optimal for the removal of pollutants. Removals of COD, turbidity and UV₂₅₄ were 77.5%, 97.6%, and 88.8%, respectively. The amount of dry sludge generated to treat 1 m³ of SDE was 0.041 kg. Coagulation-flocculation mechanisms involved in chitosan-mediated treatment of SDE involve the neutralization of electrostatic charges carried on the amine groups present in cationic chitosan at pH 5.00. Doehlert matrix proved to be a useful tool in optimizing parameters throughout the coagulation-flocculation process. Chitosan from shrimp waste is a low-cost, eco-friendly coagulant alternative for the removal pollutants from dairy effluent using the CEPT.

Pollutant removal from dairy wastewater using live Azolla filiculoides in batch and continuous bioreactors

Fast and proper treatment of dairy wastewater is necessary before discharging it to the environment. In this study, healthy Azolla filiculoides was used to remove pollutants, including phosphorus (P), sodium (Na), chemical oxygen demand (COD), biological oxygen demand (BOD), and total dissolved solids (TDS) of dairy effluent in batch, continuous system, as well as continuous with the slow stirring system. These systems were handmade. The maximum removal efficiency was related to the P, which obtained 66.25% after 12 h in the batch bioreactor system. The highest removal of 13.69% after 21 h was obtained for Na using continuous with a slow stirring method. The highest removal related to the COD and BOD was 33.53% and 29.93% after 18 h, respectively, in continuous with the slow stirring system. TDS removal was achieved 31.44% after 24 h using the batch system. The results of these three systems were compared with each other using a one-way analysis of variance (ANOVA). There was no significant difference between them. Azolla filiculoides is an abundant plant in northern nature that a biosystem was used for optimum usage. It can be used as an efficient, inexpensive, and affordable bioadsorbent for dairy wastewater treatment. PRACTITIONER POINTS: Live Azolla filiculoides was used to remove pollutants. P, Na, BOD, COD, and TDS were removed from dairy wastewater. Batch, continuous, and continuous with the slow stirring systems were used. Live Azolla was an efficient, inexpensive, and affordable bio-adsorbent for dairy wastewater treatment.

Spectral deconvolution associated to the Gaussian fit as a tool for the optimization of photovoltaic electrocoagulation applied in the treatment of textile dyes

The objective of this study was to investigate the color removal in a binary mixture of azo dyes from the photovoltaic electrocoagulation (EC) technique, using spectral deconvolution and the Gaussian fit for qualitative and quantitative determination of the physical color parameter. Initially, a conventional energy source was used to feed the EC reactor and the experimental design was conducted according to the Rotational Central Compound Design (RCCD). The spectral deconvolution method associated to the Gaussian fit aided in the description of the composition of the sample matrix, In the first step, through the Analysis of Variance, the RCCD and the three-dimensional surface response graphs, the optimized operating conditions were identified, which corresponded to 1320 A m^-2 with an reaction time of 16.6 min, and an expected removal of 98.40% for Scarlet Red (SR) dye and 1160 A m^-2 with a run time of 15.7 min and 97.9% removal for Turquoise Blue (TB) dye. Using the photovoltaic module as the power source of the EC reactor, a maximum removal of 97 ± 0.43% for TB dye and 98% ± 0.81 for SR was obtained. The results encourage the applicability of photovoltaic module-fed EC technology as a promising alternative for the treatment of effluents containing textile dyes, as well as the use of the spectral deconvolution method associated with the Gaussian fit, for the reliability and precision of the results.