In this approach, a batch reactor was employed to study the degradation of pollutants under natural sunlight using TiO2 as a photocatalyst. The effects of photocatalyst dosage, reaction time and pH were investigated by evaluating the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD) and biodegradability (BOD/COD). Design Expert-Response Surface Methodology Box Behnken Design (BBD) and MATLAB Artificial Neural Network - Adaptive Neuro Fuzzy Inference system (ANN-ANFIS) methods were employed to perform the statistical modelling. The experimental values of maximum percentage removal efficiencies were found to be TOC = 82.4, COD = 85.9, BOD = 30.9% and biodegradability was 0.070. According to RSM-BBD and ANFIS analysis, the maximum percentage removal efficiencies were found to be TOC = 90.3, 82.4; COD = 85.4, 85.9; BOD = 28.9, 30.9% and the biodegradability = 0.074, 0.080 respectively at the pH 7.5, reaction time 300 min and photocatalyst dosage of 4 g L-1. The study reveals both models found to be well predicted as compared with experimental values. The values of R2 for RSM-BBD (0.920) and for ANFIS (0.990) models were almost close to 1. The ANFIS model was found to be marginally better than that of RSM-BBD.
Although a significant amount of brewery wastewater (BW) is generated during beer production, the nutrients in the BW could be reused as a potential bio-resource for biohydrogen production. Therefore, improvements in photofermentative biohydrogen production due to a combination of BW and pulp and paper mill effluent (PPME) as a mixed production medium were investigated comprehensively in this study. The experimental results showed that both the biohydrogen yield and the chemical oxygen demand removal were improved through the combination of BW and PPME. The best biohydrogen yield of 0.69 mol H2/L medium was obtained using the combination of 10 % BW + 90 % PPME (10B90P), while the reuse of the wastewater alone (100 % BW and 100 % PPME) resulted in 42.3 and 44.0 % less biohydrogen yields than the highest yield, respectively. The greatest light efficiency was 1.97 % and was also achieved using the combination of both wastewaters at 10B90P. This study revealed the potential of reusing and combining two different effluents together, in which the combination of BW and PPME improved the nutrients and light penetration into the mixed production medium.
The main limitation of a conventional palm oil mill effluent (POME) ponding system lies in its inability to completely decolourise effluent. Decolourisation of effluent is aesthetically and environmentally crucial. However, determination of the optimum process parameters is becoming more complex with the increase of the number of coagulants and responses. The primary objective of this study is to determine the optimum polymeric coagulant in the coagulation-flocculation process of palm oil mill effluent by considering all output responses, namely lignin-tannin, low molecular mass coloured compounds (LMMCC), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH and conductivity. Here, multiple-objective optimisation on the basis of ratio analysis (MOORA) is employed to discretely measure multiple response characteristics of five different types of coagulants as a function of assessment value. The optimum coagulant is determined based on the highest assessment value and was identified as QF25610 (cationic polyacrylamide). On the other hand, the lowest assessment value was represented by AN1800 (anionic polyacrylamide). This study highlights the simplicity of MOORA approach in handling various input and output parameters, and it may be useful in other wastewater treatment processes as well.
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.
This study was to investigate the mineralization of wastewater containing methyl orange (MO) in integrated anaerobic-aerobic biofilm reactor with coconut fiber as bio-material. Different aeration periods (3h in phase 1 and 2; 3, 6 and 15 h in phase 3; 24 h in phase 4 and 5) in aerobic chamber were studied with different MO concentration 50, 100, 200, 200 and 300 mg/L as influent from phase 1-5. The color removals estimated from the standard curve of dye versus optical density at its maximum absorption wavelength were 97%, 96%, 97%, 97%, and 96% and COD removals were 75%, 72%, 63%, 81%, and 73% in phase 1-5, respectively. The MO decolorization and COD degradation followed first-order kinetic model and second-order kinetic model, respectively. GC-MS analysis indicated the symmetrical cleavage of azo bond and the reduction in aromatic peak ensured the partial mineralization of MO.
sugar industry is one of the industries that produce a high amount of
pollutant since its wastewater contains high amount of organic material, biochemical
oxygen demand (bod) and chemical oxygen demand (cod). if this waste is
discharged without a proper treatment into the watercourse, it can cause problem to aquatic
life and environment. for the primary treatment process, sugar wastewater can be treated
by using chemical precipitation method which involves coagulation process. currently,
ferric chloride has been used as the coagulant but it consumes more alkalinity and
corrosive. in this study, the suitable coagulant to be used to treat the wastewater from sugar
industry and the optimum conditions to achieve high percentage removal of cod was
determined. the characteristic of the wastewater was firstly determined. then, the most
suitable coagulant to be used for the treatment was studied by determining their efficiency
to reduce cod and tss in the wastewater at different dosages. aluminium sulphate
(alum), ferric chloride and polyaluminium chloride (pac) were chosen to be studied for
suitable coagulant. The optimum condition of the coagulant (ph, coagulant dosage, fast
mixing speed) was determined by using design expert software. results showed that alum
can be used to effectively remove 42.9% of cod and 100% of tss at high dosage (50
mg/l). the optimum condition of alum was at ph 5.2, 10 mg/l of alum and 250 rpm of
mixing speed. this shows that at optimum condition, alum can be used to treat wastewater
from sugar industry.
Assessment of the treatment performance in the field-scale hybrid constructed wetland (CW) for ammonia manufacturing plant remains limited. After being in operations running on and off since 2014, the hybrid CW which treats effluent from the ammonia manufacturing plant in Peninsular, Malaysia has recently demonstrated the full clogging to the CW. It takes only 8 months to demonstrate a big deterioration of performance in 2019. Though the mechanism of clogging is not clear, which can be partially from inherent design problems or operational issues, nonetheless, it is important to evaluate how this clogging has impacted the effluent treatment performance and the continuous utilization of the CW. The purpose of this study is to evaluate the impact of the treatment performance on the ammoniacal nitrogen and COD removal when the CW is clogged. The result revealed that there is no impact on COD removal, but it has a substantial impact on the ammoniacal nitrogen removal. The ammoniacal nitrogen removal dropped to negative (outlet concentration is higher than inlet concentration) during the clogged period. Another observation is, the low removal rate also coincides with a high COD/N ratio, when the COD/N ratio increased to >2, the ammoniacal nitrogen removal rate dropped substantially, with the coefficient of determination, R2 of 40.5%. The root cause for the clogging to develop in a short period of time is unidentified. However, it is still worth noting that COD and ammoniacal nitrogen efficiency did not behave the same at the clogged CW.
Nanofiltration membranes technology commonly used for wastewater treatment especially
wastewater containing charged and/or uncharged species. Commonly, textile wastewater
possesses high chemical oxygen demand (COD) and non-biodegradable compounds such as
pigments and dyes which lead to environmental hazard and serious health problem. Therefore, the
objective of this study was to investigate the effects of hydrophilic surfactant on the preparation and
performance of Active Nanofiltration (ANF) membrane. The polymeric ANF membranes were
prepared via dry/wet phase inversion technique by immersion precipitation process. The
Cetyletrimethylammonium bromide (CTAB) as cationic surfactant was added in casting solution at
concentrations from 0 to 2.5 wt%. The synthesized membrane performance was evaluated in terms
of pure water permeation (PWP) and dye rejection. The experimental data showed that the
membrane demonstrated good increment of PWP ranging from 0.27 to 10.28 L/m2
h at applied
pressure from 100 to 500kPa, respectively. Meanwhile, the ANF membranes achieved high
removal of Methyl Blue and Reactive Black 5 dye up to 99.5% and 91.6%, respectively.
Wastewater from industrial plants such as textile, electroplating and petroleum refineries contains various substances that tend to increase the chemical oxygen demand (COD) of the wastewater. Therefore, it is desired to develop a process suitable for treating the wastewater to meet the regulatory limits. This work was conducted to investigate the potential of adapted single culture of A. baumannii, A.calcoaceticus and C.cellulans in reducing COD in real textile wastewater. The study was carried out by adapting each single culture (10% inoculums) to increasing concentration (1%, 2.5 %, 5%, 7.5 % and 10%) of textile wastewater. Then it was introduced to the textile effluent without pH adjustment for five days and the COD values were measured. The textile wastewater was supplemented with pineapple waste for bacterial growth and metabolism. Results obtained showed that pineapple waste was a good nutrient supply for the growth of the bacteria and the best concentration of textile wastewater for adaptation was at 2.5%. The results also showed that A.calcoaceticus shows highest COD reduction with 67% removal whereas A. baumannii and C.cellulans with 60% and 58% removal respectively. The outcome supported that the single culture used in this study showed considerably high reduction of COD from real textile wastewater.
This study presents a mathematical model examining wastewater pollutant removal through
an oxidation pond treatment system. This model was developed to describe the reaction
between microbe-based product mPHO (comprising Phototrophic bacteria (PSB)), dissolved
oxygen (DO) and pollutant namely chemical oxygen demand (COD). It consists
of coupled advection-diffusion-reaction equations for the microorganism (PSB), DO and
pollutant (COD) concentrations, respectively. The coupling of these equations occurred
due to the reactions between PSB, DO and COD to produce harmless compounds. Since
the model is nonlinear partial differential equations (PDEs), coupled, and dynamic, computational
algorithm with a specific numerical method, which is implicit Crank-Nicolson
method, was employed to simulate the dynamical behaviour of the system. Furthermore,
numerical results revealed that the proposed model demonstrated high accuracy when
compared to the experimental data.
In this work, landfill leachate treatment by electrocoagulation process with a novel rotating anode reactor was studied. The influence of rotating anode speed on the removal efficiency of chemical oxygen demand (COD), total dissolved solids (TDS), and total suspended solids (TSS) of raw landfill leachate was investigated. The influence of operating parameters like leachate pH, leachate temperature, current, and inter-distance between the cathode rings and anode impellers on the electrocoagulation performance were also investigated. The results revealed the optimum rotating speed is 150 rpm and increasing the rotating speed above this value led to reducing process performance. The leachate electrocoagulation treatment process favors the neutral medium and the treatment performance increases with increasing current intensity. Furthermore, the electrocoagulation treatment performance improves with increasing leachate temperature. However, the performance reduces with increasing inter-electrode distance.
Increasing agricultural irrigation to counteract a soil moisture deficit has resulted in the production of hazardous agricultural wastewater with high turbidity and chemical oxygen demand (COD). An innovative, sustainable, and effective solution is needed to overcome the pollution and water scarcity issues caused by the agricultural anthropogenic processes. This research focused on a sustainable solution that utilized a waste (broken lentil) as natural coagulant for turbidity and COD removal in agricultural wastewater treatment. The efficiency of the lentil extract (LE), grafted lentil extract (LE-g-DMC) and aluminium sulphate (alum) coagulants was optimized through the response surface methodology. Three-level Box-Behnken design was used to statistically visualize the complex interactions of pH, concentration of coagulants and settling time. LE achieved a significant 99.55% and 79.87% removal of turbidity and COD at pH 4, 88.46 mg/L of LE and 6.9 minutes of settling time, whereas LE-g-DMC achieved 99.83% and 80.32% removal of turbidity and COD at pH 6.7, 63.08 mg/L of LE-g-DMC and 5 minutes of settling time. As compared to alum, LE-g-DMC required approximately 30% less concentration. Moreover, LE and LE-g-DMC also required 75% and 65% less settling time as compared to the alum. Both LE and LE-g-DMC produced flocs with excellent settling ability (5.77 mg/L and 4.48 mL/g) and produced a significant less volume of sludge (10.60 mL/L and 8.23 mL/L) as compared with the alum. The economic analysis and assessments have proven the feasibility of both lentil-based coagulants in agricultural wastewater treatment.
This paper reviews the related literature reported in 2019 about various types of wastewaters associated with chemical and allied products. The subjects comprise wastewaters produced from various activities in agricultural, chemical, dye, petrochemical, and pharmaceutical. PRACTITIONER POINTS: Bioflocculant chitosan was used for sludge dewatering and the treatment of water and wastewater, and polishing of sanitary landfill leachate. Alkaline lignin-based flocculants were used to achieve excellent color removal for paper mill sludge. Powdered activated coke was used to remove COD (chemical oxygen demand) from chemical industry wastewater effluents.
This study evaluated and compared the performance of two vertical flow constructed wetlands (VF) using expanded clay (VF1) and biochar (VF2), of which both are low-cost, eco-friendly, and exhibit potentially high adsorption as compared to conventional filter layers. Both VFs achieved relatively high removal for organic matters (i.e. Biological oxygen demand during 5 days, BOD5) and nitrogen, accounting for 9.5 - 10.5 g.BOD5.m-2.d-1 and 3.5 - 3.6 g.NH4-N.m-2.d-1, respectively. The different filter materials did not exert any significant discrepancy to effluent quality in terms of suspended solids, organic matters and NO3-N (P > 0.05), but they did influence NH4-N effluent as evidenced by the removal rate of that by VF1 and VF2 being of 82.4 ± 5.7 and 84.6 ± 6.4%, respectively (P
Salinity expressed as total dissolved solids (TDS), is the most challenging parameter in bioremediation of produced water which may inhibit the microbial activities and cause sedimentation problems. The present study explores the feasibility of using walnut shell as an inexpensive and accessible adsorbent-carrier for the immobilization of isolated halophilic microorganisms for treatment of synthetic oilfield produced water. The moving bed biofilm reactor (MBBR) was examined with influent chemical oxygen demand (COD) concentrations from 900 to 3600 mg L-1, TDS concentrations from 35,000-200,000 mg L-1, and cycle times from 24 to 72 h. Comparison of the MBBR with the conventional sequencing batch reactor (SBR) indicated that both systems operated at lower influent COD and TDS concentrations satisfactorily; but at higher TDSs (above 150,000 mg L-1) the MBBR was more resistant to the shocks of toxicity (salinity) and organic load relative to the SBR. Also, the effluent turbidity was lower and the free sludge settling property was more favorable in the MBBR with average sludge volume index (SVI) of 38.8 mL g-1 compared to the SBR with SVI of 98.09 mL g-1. Microbial identification confirmed the presence of eight dominant halophilic species which were hydrocarbon degraders and/or denitrifiers.
Safe disposal of effluent from palm oil production poses an environmental concern. The highly polluting effluent is customarily treated by unsustainable open ponds with low efficiency, direct emissions, and massive land use. This study looks into an application of integrated anaerobic/oxic/oxic scheme for treatment of high strength palm oil mill effluent. The anaerobic reactors functioned as a prime degrader that removed up to 97.5% of the chemical oxygen demand (COD), while the aerobic reactors played a role of an effluent polisher that further reduced the COD. Their complementing roles resulted in a remarkable removal of 99.7%. Assessment of emission mitigation and biogas energy revealed that yearly energy of 53.2 TJ, emissions reduction of 239,237 tCO2 and revenue of USD 1.40 millions can be generated out of electricity generation and heating. The integrated scheme provides a viable and sustainable treatment of the high strength palm oil mill effluent.
The hemipteran (Insecta) diversity in the upper part of the Kerian River Basin was low with only 8 families and 16 genera recorded at 4 study sites from 3 rivers. Water bug composition varied among sampling sites (Kruskal-Wallis χ (2) = 0.00, p<0.05) but was not affected by wet-dry seasons (Z = 0.00, p>0.05). All recorded water parameters were weakly associated with generic abundance but the biochemical oxygen demand (BOD), chemical oxygen demand (COD), Water Quality Index (WQI) and heavy metals (zinc and manganese) showed relatively strong positive or negative relations with hemipteran diversity and richness (H' and R2). Within the ranges of measured water parameters, the WQI was negatively associated with hemipteran diversity and richness, implying the tolerance of the water bugs to the level of pollution encountered in the river basin. Based on its highest abundance and occurrence (ISI), Rhagovelia was the most important genus and along with Rheumatogonus and Paraplea, these genera were common at all study sites. In conclusion, habitat availability and suitability together with some environmental parameters influenced the abundance and composition of hemipterans in this river basin.
The study of river water quality plays an important role in assessing the pollution status and health of the water bodies. Human-induced activities such as domestic activities, aquaculture, agriculture and industries have detrimentally affected the river water quality. Pinang River is one of the important rivers in Balik Pulau District that supplies freshwater for human consumption. A total of 442 physical and chemical parameters data of the Pinang River, Balik Pulau catchment were analysed to determine the sources of pollutants entering the river. Non-supervised artificial neural network (ANN) was employed to classify and cluster the river into upstream, middle-stream and downstream zones. The monitored data and non-supervised ANN analysis demonstrated that the source of nitrate was derived from the upper part of the Pinang River, Balik Pulau while the sources of nitrite, ammonia and ortho-phosphate are predominant at the middle-stream of the river system. Meanwhile, the sources of high total suspended solid and biological oxygen demand were concentrated at the downstream of the river.
The performance and operational characteristics of a laboratory scale modified anaerobic hybrid baffled (MAHB) reactor were studied using recycled paper mill effluent (RPME) wastewater. MAHB reactor was continuously operated at 35°C for 90 days with organic loading rate (OLR) increased from 0.14 to 0.57 g/L/dy. This present study demonstrated that the system was proficient in treating low strength RPME wastewater. Highest carbon oxygen demand (COD) removal were recorded up to 97% for an organic loading of 0.57 g /L/dy while effluent alkalinity assured that the system pH in the MAHB compartments were of great advantages to acidogens and methanogens respectively. Methane and biogas production rate shows increment as the load increases, which evidently indicated that the most significant approach to enhance gas production rates involves the increment of incoming substrate moderately. Variations of biogas and volatile fatty acid (VFA) in different compartments of MAHB reactor indicated the chronological degradation of substrate. The compartmental structure of MAHB reactor provided its strong ability to resist shock loads. From this present study, it shows the potential usage of MAHB reactor broadens the usage of multi-phase anaerobic technology for industrial wastewater treatment.
Water-immiscible substrate, diesel, was supplied as the main substrate in the fermentation of Pseudomonas aeruginosa USM-AR2 producing rhamnolipid biosurfactant, in a stirred tank bioreactor. In addition to the typical gas-aqueous system, this system includes gas-hydrocarbon-aqueous phases and the presence of surfactant (rhamnolipid) in the fermentation broth. The effect of diesel dispersion on volumetric oxygen transfer coefficient, k L a, and thus oxygen transfer, was evaluated at different agitations of 400, 500 and 600 rpm. The oxygen transfer in this oil-water-surfactant system was shown to be affected by different oil dispersion at those agitation rates. The highest diesel dispersion was obtained at 500 rpm or impeller tip speed of 1.31 m/s, compared to 400 and 600 rpm, which led to the highest k L a, growth and rhamnolipid production by P. aeruginosa USM-AR2. This showed the highest substrate mixing and homogenization at this agitation speed that led to the efficient substrate utilization by the cells. The oxygen uptake rate of P. aeruginosa USM-AR2 was 5.55 mmol/L/h, which showed that even the lowest k L a (48.21 h-1) and hence OTR (57.71 mmol/L/h) obtained at 400 rpm was sufficient to fulfill the oxygen demand of the cells. The effect of rhamnolipid concentration on k L a showed that k L a increased as rhamnolipid concentration increased to 0.6 g/L before reaching a plateau. This trend was similar for all agitation rates of 400, 500 and 600 rpm, which might be due to the increase in the resistance to oxygen transfer (k L decrease) and the increase in the specific interfacial area (a).