Precipitation softening: a pretreatment process for seawater desalination

Reduction of membrane fouling in reverse osmosis systems and elimination of scaling of heat transfer surfaces in thermal plants are a major challenge in the desalination of seawater. Precipitation softening has the potential of eliminating the major fouling and scaling species in seawater desalination plants, thus allowing thermal plants to operate at higher top brine temperatures and membrane plants to operate at a reduced risk of fouling, leading to lower desalinated water costs. This work evaluated the use of precipitation softening as a pretreatment step for seawater desalination. The effectiveness of the process in removing several scale-inducing materials such as calcium, magnesium, silica, and boron was investigated under variable conditions of temperature and pH. The treatment process was also applied to seawater spiked with other known fouling species such as iron and bacteria to determine the efficiency of removal. The results of this work show that precipitation softening at a pH of 11 leads to complete elimination of calcium, silica, and bacteria; to very high removal efficiencies of magnesium and iron (99.6 and 99.2 %, respectively); and to a reasonably good removal efficiency of boron (61 %).     

Synthesis of Hybrid Gas Permeation Membrane/Condensation Systems for Pollution Prevention

ABSTRACT

Pollution prevention is a major economic and environmental issue in the chemical processing industries. This paper addresses the design of cost-effective recovery systems for vaporous emissions, systems that allow environmentally sound recycling of the recovered components for re-use within the process as a means of pollution prevention. A methodology is proposed to design optimal hybrid systems that involve gas permeation membranes and vapor condensation systems. The design methodology is presented as a mixed-integer, nonlinear program. Based on a fixed structure of the system, a short-cut formulation is derived. Additionally, the incorporation of the system into the emerging mass integration methodology is presented. It is demonstrated, through an industrial case study, that hybrid membrane/condensation systems possess advantages over either separation technique alone.     

Performance evaluation of sequencing batch reactor for beverage industrial wastewater treatment

Attempts were made in this study to examine the effectiveness of sequencing batch reactor (SBR) for the treatment of beverage industrial wastewater. The SBR was operated at three different organic loading rates (OLRs): 2, 1.7 and 1.1 kg COD/m3 d. Results of continuous long-term operation showed that by decreasing OLR from 2 to 1.7 kg COD/m3 day, the removal efficiency was increased from 95.5 to 99.3% for COD, from 95.3 to 98.1% for BOD and from 87 to 97.7% for TSS. While further decreasing of the OLR to 1.1 kg COD/m3 day, there is no significant adverse effect on organics removal. Also, residual total nitrogen (TN) concentration decreased by decreasing the OLR. However, increasing the OLRs exerted a slightly negative effect on the removal of total phosphorous. On the other hand, the experimental data indicated that the substrate utilization kinetic followed Monod's kinetics model approximately. The maximum specific substrate utilization rate (micro(max), half velocity coefficient (Ks), growth yield coefficient (Y) and decay coefficient (Kd) were 2.94 d(-1), 15.22 mg/L, 0.2384 g VSS/g COD and 0.2019 h(-1), respectively.     

Feedstock quality and growth of bioenergy crops fertilized with sewage sludge

Sewage sludge is rich in essential plant nutrients, but its use is restricted for crop production due to the pollutants it contains, such as metalloids and heavy metals. Sludge is also very sticky and compact. Therefore, the objectives of this work were to evaluate (1) the impact of various amounts of sludge on bioenergy crop productivity and quality and (2) the use of peat as an adjuvant to reduce the stickiness, density and nutrient richness of the sludge. Three different applications of sludge were examined, high, low (50% of high) and low mixed with an equal volume of peat. The sludge-peat mix increased significantly leaf area and biomass accumulation of maize and hemp. High sludge and sludge-peat mix applications increased significantly the leaf area and biomass accumulation as well as the net photosynthesis of oilseed rape. High sludge application resulted in the highest heavy metal and metalloid accumulation in maize and hemp. Sludge-peat mix resulted in the highest heavy metal and metalloid accumulation in oilseed rape. However, the sludge-peat mix application provided the best feedstock quality in all three crops, since chloride, silicon and sulfur concentrations and ash content in plant material were the lowest of the three sludge treatments.     

Effects of chitosan on oxidative stress and metallothioneins in aquatic worm Tubifex tubifex (Oligochaeta, Tubificidae)

Chitosan is a natural polymer which has the property to elicit the natural defenses mechanism in plant and which can be an interesting biopesticides. It is then necessary to investigate the potential toxicity of chitosan for aquatic animal health. Metallothioneins (MTs) are low molecular weight proteins, mainly implicated in metal ion detoxification. Increase in MTs contents had been considered as a specific biomarker of metal exposure. However recently it has been demonstrated that MTs participate in several cellular functions such as regulation of growth and anti-oxidative defenses. Therefore, the induction of MTs has been investigated in the aquatic worms Tubifex tubifex exposed to chitosan. MTs levels in exposed worm increased significantly (p > 0.05) after 2, 4, and 7 days of exposure to different concentrations of chitosan (maximum + 158.19 +/- 10.2% after 2 days of exposure to 125 mgl(-1) of chitosan). Several antioxidant parameters including glutathione (GSH), glutathione-S-transferase (GST), glutathione reductase (GR), and catalase (CAT) were quantified in T. tubifex after 2, 4, and 7 days of exposure to chitosan. Exposure to chitosan had a negative effect on T. tubifex growth (maximum effect -6.11 +/- 1.6% after 7 days with 125 mgl(-1)) demonstrating the toxic effect of the pesticide. This growth rate decrease was accompanied by a reduction in protein contents. The activity of catalase (CAT), glutathione-S-transferase (GST), and glutathione reductase (GR) increased in response to the chitosan demonstrating an oxidative stress in the worms.     

The effect of air pollution on drivers’ safety performance

Environmental Science and Pollution Research - Numerous studies have determined the adverse effects of air pollution on the health, welfare, and comfort of humans. More recently, the effects of air...     

Appraisal of methane production and anaerobic fermentation kinetics of livestock manures using artificial neural networks and sinusoidal growth functions

This study aimed to perform a comparative analysis of the performance of five models (Gompertz, logistic, Richards, the first-order, artificial neural networks) in predicting methane production rate from anaerobic digestion of livestock manures. The input variables were fermentation time, digestion temperature, biogas temperature, ambient temperature, pH, and specific biogas production rate. The physicochemical compositions of cow manure and sheep manure showed that volatile solid (VS) contents were close to each other in manure compositions (77.6% and 64.7%, respectively), while the potential of methane production from cow manure (673.44 mL CH₄/g VS) was greater than that from sheep manure (320.32 mL CH₄/g VS). The determination coefficients (R²) for logistic function, Gompertz, Richards, the first-order, and ANN models were obtained as 0.968, 0.967, 0.975, 0.825, and 0.995 for the cow manure, respectively. In case of the sheep manure, the R² values obtained from these models were 0.976, 0.979, 0.981, 0.968 and 0.991, respectively. Although the determination coefficients of all models were in satisfactory agreement with the experimental data, the ANN model showed competitive lower RMSE values of 0.111 and 0.164 for cow and sheep manure data sets, respectively, indicating its superior performance than other models.