Free-surface constructed wetlands are known as a low-energy green technique to highly decrease a wide range of pollutants in wastewater and stormwater before discharge into natural water. In this study, two spatial analyses, principal factor analysis and hierarchical cluster analysis (HACA), were employed to interpret the effect of wetland on the water quality variables (WQVs) and to classify the wetland into groups with similar characteristics. Eleven WQVs were collected at the 17 sampling stations twice a month for 13 months. All sampling stations were classified by HACA into three clusters, with high, moderate, and low pollution areas. To improve the water quality, the performance of Cluster-III (micropool) is more significant than Cluster-I and Cluster-II. Implications of this study include potential savings of time and cost for long-term data monitoring purposes in the free-constructed wetland.
This paper highlights a preliminary study on the potential of a tipping flush gate to be used in an open storm drain to remove sediment. The investigation was carried out by using a plasboard model of the tipping flush gate installed in a rectangular flume. A steady flow experiment was carried out to determine the discharge coefficients and also the outflow relationship of the tipping flush gate. The velocity produced by the gate at various distances downstream of the gate during flushing operation was measured using a flowmeter and the velocity at all the points was higher than the recommended self-cleansing design available in the literature. A preliminary experiment on the efficiency of flushing was conducted using uniform sediment with d50 sizes of 0.81, 1.53 and 4.78 mm. Results generally showed that the number of flushes required to totally remove the sediment from the initial position by a distance of 1 m increased by an average of 1.50 times as the sediment deposit bed thickness doubled. An equation relating the number of flushes required to totally remove the sediment bed for 1 m with the sediment bed deposit thickness was also developed for the current study.
Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
A pilot scale membrane plant was constructed and monitored in Shah Alam, Malaysia for municipal wastewater reclamation for industrial application purposes. The aim of this study was to verify its suitability under the local conditions and environmental constraints for secondary wastewater reclamation. Immersed-type crossflow microfiltration (IMF) was selected as the pretreatment step before reverse osmosis filtration. Secondary wastewater after chlorine contact tank was selected as feed water. The results indicated that the membrane system is capable of producing a filtrate meeting the requirements of both WHO drinking water standards and Malaysian Effluent Standard A. With the application of an automatic backwash process, IMF performed well in hydraulic performance with low fouling rate being achieved. The investigations showed also that chemical cleaning is still needed because of some irreversible fouling by microorganisms always remains. RO treatment with IMF pretreatment process was significantly applicable for wastewater reuse purposes and promised good hydraulic performance.
Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.
The potential of Pleurotus ostreatus spent mushroom compost (PSMC) as a green biosorbent for nickel (II) biosorption was investigated in this study. A novel approach of using the half-saturation concentration of biosorbent to rapidly determine the uptake, kinetics and mechanism of biosorption was employed together with cost per unit uptake analysis to determine the potential of this biosorbent. Fifty per cent nickel (II) biosorption was obtained at a half-saturation constant of 0.7 g biosorbent concentration, initial pH in the range of 4-8, 10 min contact time, 50 mL 50 mg/L nickel (II) initial concentration. The experimental data were well fitted with the Langmuir isotherm model and the maximum nickel (II) biosorption was 3.04 mg/g. The results corresponded well to a second pseudo order kinetic model with the coefficient of determination value of 0.9999. Based on FTIR analysis, the general alkyl, hydroxyl or amino, aliphatic alcohol and carbonyl functional groups of biosorbent were involved in the biosorption process. Therefore, biosorption of nickel (II) must involve several mechanisms simultaneously such as physical adsorption, chemisorption and ion exchange. Cost comparison for PSMC with Amberlite IRC-86 ion exchange resin indicates that the biosorbent has the potential to be developed into a cost effective and environmentally friendly treatment system.
Neat cellulose acetate (CA) and CA/polysulfone (PSf) blend ultrafiltration membranes in the presence of polyvinylpyrrolidone as a pore former were prepared via a phase inversion technique. The prepared membranes were characterized by Fourier transform infrared, scanning electron microscopy, mechanical strength, water content, porosity, permeate flux and heavy metals (Pb2+, Cd2+, Zn2+ and Ni2+) rejection to comprehend the impact of polymer blend composition and additive on the properties of the modified membranes. The water flux expanded by increasing of PSf content in the polymer composition. CA/PSf (60/40) had the highest flux among prepared membranes. Prepared blend membranes were able to remove heavy metals from water in the following order: Pb2+ > Cd2+ > Zn2+ > Ni2+. The CA/PSf (80/20) blend membrane had great performance among prepared membranes due to the high heavy metals removal and permeate flux.
Removal of low-concentration ammonia (1-10 ppm) from aquaculture wastewater was investigated via polysulfone (PSf)/zeolite mixed matrix membrane. PSf/zeolite mixed matrix membranes with different weight ratios (90/10, 80/20, 70/30 and 60/40 wt.%) were prepared and characterized. Results indicate that PSf/zeolite (80/20) was the most efficient membrane for removal of low-concentration ammonia. The ammonia elimination by PSf/zeolite (80/20) from aqueous solution for 10, 7, 5, 3 and 1 ppm of ammonia was 100%, 99%, 98.8%, 96% and 95% respectively. The recorded results revealed that pure water flux declined in higher loading of zeolite in the membrane matrix due to surface pore blockage caused by zeolite particles. On the other hand, ammonia elimination from water was decreased in higher contents of zeolite because of formation of cavities and macrovoids in the membrane substructure.
The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero.
As the ponding system used to treat palm oil mill effluent (POME) frequently fails to satisfy the discharge standard in Malaysia, the present study aimed to resolve this problem using an optimized electrocoagulation process. Thus, a central composite design (CCD) module in response surface methodology was employed to optimize the interactions of process variables, namely current density, contact time and initial pH targeted on maximum removal of chemical oxygen demand (COD), colour and turbidity with satisfactory pH of discharge POME. The batch study was initially designed by CCD and statistical models of responses were subsequently derived to indicate the significant terms of interactive process variables. All models were verified by analysis of variance showing model significances with Prob > F < 0.01. The optimum performance was obtained at the current density of 56 mA/cm(2), contact time of 65 min and initial pH of 4.5, rendering complete removal of colour and turbidity with COD removal of 75.4%. The pH of post-treated POME of 7.6 was achieved, which is suitable for direct discharge. These predicted outputs were subsequently confirmed by insignificant standard deviation readings between predicted and actual values. This optimum condition also permitted the simultaneous removal of NH3-N, and various metal ions, signifying the superiority of the electrocoagulation process optimized by CCD.
The aim of this study is to investigate the performance of combined solar photo-catalyst of titanium oxide/zinc oxide (TiO2/ZnO) with aeration processes to treat petroleum wastewater. Central composite design with response surface methodology was used to evaluate the relationships between operating variables for TiO2 dosage, ZnO dosage, air flow, pH, and reaction time to identify the optimum operating conditions. Quadratic models for chemical oxygen demand (COD) and total organic carbon (TOC) removals prove to be significant with low probabilities (<0.0001). The obtained optimum conditions included a reaction time of 170 min, TiO2 dosage (0.5 g/L), ZnO dosage (0.54 g/L), air flow (4.3 L/min), and pH 6.8 COD and TOC removal rates of 99% and 74%, respectively. The TOC and COD removal rates correspond well with the predicted models. The maximum removal rate for TOC and COD was 99.3% and 76%, respectively at optimum operational conditions of TiO2 dosage (0.5 g/L), ZnO dosage (0.54 g/L), air flow (4.3 L/min), reaction time (170 min) and pH (6.8). The new treatment process achieved higher degradation efficiencies for TOC and COD and reduced the treatment time comparing with other related processes.
In Malaysia, most colored wastewater from dyeing factories is discharged to the environment causing serious problems. In this paper the influence of several reacting conditions, i.e. H2O2, pH, Ultraviolet (UV) intensity and dye concentration, on the performance of the immobilized system is discussed. The pH of the solution was varied from 3 to 11 while H2O2 concentration tested was from 10(-4) M to 5 x 10(-2) M. UV was tested at 365 nm and 254 nm, while dye concentration ranged from 2.5 x 10(-4) M to 10(-3) M. The influence of the reacting conditions was assessed based on absorbance. Using an OG concentration of 10(-3) M, the degradation increases from 17.8% to 49.7%. Optimum concentration of H2O2 was found to be 5 x 10(-3) M for degradation. Increasing the intensity of the UV light via shorter light wavelength also improves the performance of the system. Increasing the concentration of the dye reduces the overall performance of the system. Using the dye concentration of 2.5 x 10(-4) M (H2O2 = 10(-2) M, lambda = 254 nm, pH = 11), gives a degradation of 93.2%. At dye concentration of 10(-3) M, the performance was reduced to 53.1%.
Sampling of urban runoff was carried out in a small catchment, which represents a residential area (3.34 ha) in Skudai, Johor. One hundred and seventeen runoff samples from ten storm events were analysed. Runoff quality showed large variations in concentrations during storms, especially for SS, BOD5 and COD. Concentrations of NO3-N, NO2-N, NH3-N, and P were also high. Lead (Pb) was also detected but the levels were low (<0.001 mg/L). In general, the river quality is badly polluted and falls in Class V based on the Malaysian Interim National Water Quality Standards. Event mean concentrations for all parameters were found to vary greatly between storms. The values (mg/L) were BOD5 (72), COD (325), SS (386), NO3-N (2.5), NO2-N (0.58), NH3-N (6.8), P (3.4), respectively. First flush phenomena were observed for BOD, COD, SS, NO3-N, NH3-N and P. The first 20-30% of the runoff volume evacuated between 20-59% BOD, 15-69% COD, 15-78% SS, 14-49% NO3-N, 14-19% NO2-N, 23-53% NH3-N and 23-43% P.
In this paper, the adsorptive performance of synthesized thiourea (TU) modified poly(acrylonitrile-co-acrylic acid) (TU-P(AN-co-AA)) polymeric adsorbent for capturing p-nitrophenol (PNP) from aqueous solution was investigated. TU-P(AN-co-AA) was synthesized via the redox polymerization method with acrylonitrile (AN) and acrylic acid (AA) as the monomers, then modified chemically with thiourea (TU). Characterization analysis with Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), elemental microanalysis for CHNS, zeta potential measurement, Brunauer-Emmett-Teller (BET) surface analysis and thermal analyses were carried out to determine the morphology and physico-chemical properties of the synthesized polymer. The characterization results indicated successful surface modification of polymer with TU. The performance of TU-P(AN-co-AA) for the removal of PNP was investigated under various experimental parameters (adsorbent dosage, initial adsorbate concentration, contact time and temperature). The results demonstrated that the Freundlich isotherm model and pseudo-second-order kinetic model best described the equilibrium and kinetic data, respectively. Thermodynamic studies showed that the uptake of PNP by TU-P(AN-co-AA) was spontaneous and exothermic in nature. The results of the regeneration studies suggested that the TU-P(AN-co-AA) polymer is a reusable adsorbent with great potential for removing PNP from wastewater.
In the present study, Electrochemical Oxidation was used to remove COD and color from semi-aerobic landfill leachate collected from Pulau Burung Landfill Site (PBLS), Penang, Malaysia. Experiments were conducted in a batch laboratory-scale system in the presence of NaCl as electrolyte and aluminum electrodes. Central composite design (CCD) under Response surface methodology (RSM) was applied to optimize the electrochemical oxidation process conditions using chemical oxygen demand (COD) and color removals as responses, and the electrolyte concentrations, current density and reaction time as control factors. Analysis of variance (ANOVA) showed good coefficient of determination (R(2)) values of >0.98, thus ensuring satisfactory fitting of the second-order regression model with the experimental data. In un-optimized condition, maximum removals for COD (48.77%) and color (58.21%) were achieved at current density 80 mA/cm(2), electrolyte concentration 3,000 mg/L and reaction time 240 min. While after optimization at current density 75 mA/cm(2), electrolyte concentration 2,000 mg/L and reaction time 218 min a maximum of 49.33 and 59.24% removals were observed for COD and color respectively.
Although ultrafiltration (UF) membranes are applicable in wastewater and water treatment, most UF membranes are hydrophobic and susceptible to severe fouling by natural organic matter. In this work, polysulfone (PSf) membrane was blended with silicaluminophosphate (SAPO) nanoparticles, SAPO-34, to study the effect of SAPO-34 incorporation in humic acid (HA) fouling mitigation. The casting solution was prepared by blending 5-20 wt% of SAPO-34 nanoparticles into the mixture of PSf, 1-methyl-2-pyrrolidinone and polyvinyl alcohol at 75 °C. All membrane samples were then prepared using the phase inversion method. Blending SAPO-34 zeolite into PSf membranes caused augmentation in surface hydrophilicity and pore size, leading to higher water permeation. In the HA filtration test, mixed matrix membranes (MMMs) with SAPO-34 zeolite showed reduced HA fouling initiated from pore blocking. The MMM with 20 wt% SAPO-34 loading exhibited the highest increment of water permeation (83%) and maintained about 75% of permeate flux after 2.5 h. However, the SAPO-34 fillers agglomerated in the PSf matrix and induced macrovoid formation on the membrane surface when excessive zeolite was added.
This study aimed to produce polyhydroxyalkanoates (PHAs) from organic wastes by mixed bacterial cultures using anaerobic-aerobic fermentation systems. Palm oil mill effluent (POME) was used as an organic source, which was cultivated in a two-step-process of acidogenesis and acid polymerization. POME was operated in a continuous flow anaerobic reactor to access volatile fatty acids (VFAs) for PHAs production. During fermentation, VFA concentration was produced in the range of 5 to 8 g/L and the COD concentration reduced up to 80% from 65 g/L. The VFA from anaerobic fermentation was then utilised for PHA production using a mixed culture in availability of aerobic bioreactor. Production of PHAs was recorded high when using a high volume of substrates because of the higher VFA concentration. Even though the maximum PHA content was observed at only 40% of the cell dried weight (CDW), their production and performance are significant in mixed microbial culture.
The objective of this study was to investigate the cycling of arsenic in the water column of a post-mining lake. This study is part of a research project to develop health risk assessment for the surrounding population. Inductively Coupled Plasma-Mass Spectrophotometer (ICP-MS) and Capillary Electrophoresis (CE) have been used to analyze the total amount and speciation, respectively. A computer program, called MINTEOA2, which was developed by the United States Environmental Protection Agency (USEPA) was used for predicting arsenic, iron, and manganese as functions of pH and solubility. Studying the pH values and cycle of arsenic shows that the percentage of bound arsenate, As(V) species in the form of HAsO4- increases with range pH from 5 to 7, as well as Fe(II) and Mn(III). As expected phases of arsenic oxides are FeAsO4 and Mn3(AsO4), as a function of solubility, however none of these phases are over saturated and not precipitated. It means that the phases of arsenic oxides have a high solubility.
Cr(VI) biosorption and bioreduction ability of locally isolated Cr-resistant bacteria was investigated using the shake-flask technique. A mixture of S. epidermidis and B. cereus showed the highest minimum inhibitory concentration (MIC) level at 750 mg/L Cr(VI) followed by S. aureus and Bacillus sp. of 250 mg/L, and A. haemolyticus of 70 mg/L. From the Langmuir adsorption isotherm, the treatment of cells with heat-acid resulted in the highest amount of Cr(VI) adsorped (78.25 mg/g dry wt. for S. epidermidis) compared to heat-acetone (67.93 mg/g dry wt. Bacillus sp.), heat only (36.05 mg/g dry wt. S. epidermidis) or untreated cells (45.40 mg/g dry wt. S. epidermidis and B. cereus). FTIR analysis showed the involvement of amine groups in Cr(VI) adsorption. In the bioreduction study, A. haemolyticus was able to completely reduce Cr(VI) up to 50 mg/L.
Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.