Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.
The effect of temperature on the efficiency of organics and nutrients removal during the cultivation of aerobic granular sludge (AGS) in biological treatment of synthetic wastewater was studied. With this aim, three 3 L sequencing batch reactors (SBRs) with influent loading rate of 1.6 COD g (L d)(-1) were operated at different high temperatures (30, 40 and 50 °C) for simultaneous COD, phosphate and ammonia removal at a complete cycle time of 3 h. The systems were successfully started up and progressed to steady state at different cultivation periods. The statistical comparison of COD, phosphate and ammonia for effluent from the three SBRs revealed that there was a significant difference between groups of all the working temperatures of the bioreactors. The AGS cultivated at different high temperatures also positively correlated with the accumulation of elements including carbon, oxygen, phosphorus, silicon, iron, aluminium, calcium and magnesium that played important roles in the granulation process.
Nowadays, the pretreatment of wastewater prior to discharge is very important in various industries as the wastewater without any treatment contains high organic pollution loads that would pollute the receiving waterbody and potentially cause eutrophication and oxygen depletion to aquatic life. The reuse of seafood wastewater discharge in microalgae cultivation offers beneficial purposes such as reduced processing cost for wastewater treatment, replenishing ground water basin as well as financial savings for microalgae cultivation. In this paper, the cultivation of Chlorella vulgaris with an initial concentration of 0.01 ± 0.001 g⋅L-1 using seafood sewage discharge under sunlight and fluorescent illumination was investigated in laboratory-scale without adjusting mineral nutrients and pH. The ability of nutrient removal under different lighting conditions, the metabolism of C. vulgaris and new medium as well as the occurrence of auto-flocculation of microalgae biomass were evaluated for 14 days. The results showed that different illumination sources did not influence the microalgae growth, chemical oxygen demand (COD) and biochemical oxygen demand (BOD) significantly. However, the total nitrogen (total-N) and total phosphorus (total-P) contents of microalgae were sensitive to the illumination mode. The amount of COD, BOD, total-N and total-P were decreased by 88%, 81%, 95%, and 83% under sunlight mode and 81%, 74%, 79%, and 72% under fluorescent illumination, respectively. Furthermore, microalgae were auto-flocculated at the final days of cultivation with maximum biomass concentration of 0.49 ± 0.01 g⋅L-1, and the pH value had increased to pH 9.8 ± 0.1 under sunlight illumination.
Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
Palm oil mill effluent (POME) is highly polluting wastewater generated from the palm oil milling process. Palm oil mill effluent was used as an electrolyte without any additive or pretreatment to perform electrocoagulation (EC) using electricity (direct current) ranging from 2 to 4 volts in the presence of aluminum electrodes with a reactor volume of 20 L. The production of hydrogen gas, removal of chemical oxygen demand (COD), and turbidity as a result of electrocoagulation of POME were determined. The results show that EC can reduce the COD and turbidity of POME by 57 and 62%, respectively, in addition to the 42% hydrogen production. Hydrogen production was also helpful to remove the lighter suspended solids toward the surface. The production of Al(OH)XHO at the aluminum electrode (anode) was responsible for the flocculation-coagulation process of suspended solids followed by sedimentation under gravity. The production of hydrogen gas from POME during EC was also compared with hydrogen gas production by electrolysis of tap water at pH 4 and tap water without pH adjustment under the same conditions. The main advantage of this study is to produce hydrogen gas while treating POME with EC to reduce COD and turbidity effectively.
This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
Understanding the relationship between microbial community and mechanism of aerobic granulation could enable wider applications of granules for high-strength wastewater treatment. The majority of granulation studies principally determine the engineering aspects of granules formation with little emphasis on the microbial diversity. In this study, three identical reactors namely R1, R2 and R3 were operated using POME at volumetric loadings of 1.5, 2.5 and 3.5 kg COD m(-3) d(-1), respectively. Aeration was provided at a volumetric flow rate of 2.5 cms(-1). Aerobic granules were successfully developed in R2 and R3 while bioflocs dominated R1 until the end of experiments. Fractal dimension (D(f)) averaged at 1.90 suggesting good compactness of granules. The PCR-DGGE results indicated microbial evolutionary shift throughout granulation despite different operating OLRs based on decreased Raup and Crick similarity indices upon mature granule formation. The characteristics of aerobic granules treating high strength agro-based wastewater are determined at different volumetric loadings.
The main objective of this work was to determine the effectiveness of various biofouling reducers (BFRs) to operational condition in hybrid membrane bioreactor (MBR) of palm oil mill effluent (POME). A series of tests involving three bench scale (100 L) hybrid MBR were operated at sludge retention times (SRTs) of 30 days with biofouling reducer (BFR). Three different biofouling reducers (BFRs) were powdered actived carbon (PAC), zeolite (Ze), and Moringa oleifera (Mo) with doses of 4, 8 and 12 g L(-1) respectively were used. Short-term filtration trials and critical flux tests were conducted. Results showed that, all BFRs successfully removed soluble microbial products (SMP), for PAC, Ze, and Mo at 58%, 42%, and 48%, respectively. At their optimum dosages, PAC provided above 70% reductions and 85% in fouling rates during the short-term filtration and critical flux tests.
The effects of Mecoprop (RS)-MCPP were investigated in an anaerobic membrane bioreactor (AnMBr) fed with synthetic wastewater containing stepwise increases in Mecoprop concentration, 5-200 mg L(-1) over 240 days. Effects were observed in terms of soluble chemical oxygen demand (COD) removal efficiency, volatile fatty acid (VFA) production, and methane yield. Soluble COD removal efficiency was stable at Mecoprop concentrations below 200 (±3) mg L(-1), with an average of 98 (±0.7)% removal. However, at 200 (±3) mg L(-1) Mecoprop, the COD removal efficiency decreased gradually to 94 (±1.5)%. At 5 mg L(-1) Mecoprop, acetic and propionic acid concentrations increased by 60% and 160%, respectively. In contrast, when Mecoprop was increased to 200 (±3) mg L(-1), the formation and degradation of acetate was unaffected by the higher Mecoprop concentration, acetate remaining below 35 mg L(-1). Increases in the Mecoprop specific utilization rate were observed as Mecoprop was increased stepwise between 5 and 200 mg L(-1).
Bio-hydrogen production from wastewater using sludge as inoculum is a sustainable approach for energy production. This study investigated the influence of initial pH and temperature on bio-hydrogen production from dairy wastewater using pretreated landfill leachate sludge (LLS) as an inoculum. The maximum yield of 113.2 ± 2.9 mmol H2/g chemical oxygen demand (COD) (12.8 ± 0.3 mmol H2/g carbohydrates) was obtained at initial pH 6 and 37 °C. The main products of volatile fatty acids were acetate and butyrate with the ratio of acetate:butyrate was 0.4. At optimum condition, Gibb's free energy was estimated at -40 kJ/mol, whereas the activation enthalpy and entropy were 65 kJ/mol and 0.128 kJ/mol/l, respectively. These thermodynamic quantities suggest that bio-hydrogen production from dairy wastewater using pretreated LLS as inoculum was effective and efficient. In addition, genomic and bioinformatics analyses were performed in this study.