In this study, calcined Lapindo volcanic mud (LVM) was used as an adsorbent to remove an anionic dye, methyl orange (MO), from an aqueous solution by the batch adsorption technique. Various conditions were evaluated, including initial dye concentration, adsorbent dosage, contact time, solution pH, and temperature. The adsorption kinetics and equilibrium isotherms of the LVM were studied using pseudo-first-order and -second-order kinetic equations, as well as the Freundlich and Langmuir models. The experimental data obtained with LVM fits best to the Langmuir isotherm model and exhibited a maximum adsorption capacity (q(max)) of 333.3 mg g(-1); the data followed the second-order equation. The intraparticle diffusion studies revealed that the adsorption rates were not controlled only by the diffusion step. The thermodynamic parameters, such as the changes in enthalpy, entropy, and Gibbs free energy, showed that the adsorption is endothermic, random and spontaneous at high temperature. The results indicate that LVM adsorbs MO efficiently and could be utilized as a low-cost alternative adsorbent for the removal of anionic dyes in wastewater treatment.
Oxidation of sulfide in aqueous solution by hydrogen peroxide was investigated in the presence of hydrated ferric oxide catalyst. The ferric oxide catalyst was synthesized by sol gel technique from ferric chloride and ammonia. The synthesized catalyst was characterized by Fourier transform infrared spectroscopy, X-Ray diffraction analysis, scanning electrom microscope and energy dispersive X-ray analysis. The catalyst was quite effective in oxidizing the sulfide by hydrogen peroxide. The effects of sulfide concentration, catalyst loading, H2O2 dosing and temperature on the kinetics of sulfide oxidation were investigated. Kinetic equations and activation energies for the catalytic oxidation reaction were calculated based on the experimental results.
In this study, solidification/stabilization (S/S) of nickel hydroxide sludge using ordinary Portland cement (OPC) and oil palm ash (OPA) was carried out. The effects of increased substitution of OPA wt% in the S/S mix designs on the treated samples' physical and chemical characteristics were investigated. The physical characteristics studied were unconfined compressive strength (UCS) and changes in crystalline phases while chemical characteristics studied were leachability of nickel and leachate pH. Results indicated the optimum mix design for S/S of nickel hydroxide sludge using both OPC and OPA at B/S(d)=1 in terms of cost-effectiveness and treatment efficiency was 15 wt% OPA, 35 wt% OPC and 50 wt% sludge. The sufficient UCS and low leached nickel concentrations shown for this mix design indicate the viability of using OPA as substitute of OPC as it can significantly reduce cost normally incurred by usage of high amounts of OPC.
The preparation of chars and activated carbon as low-cost elemental mercury adsorbents was carried out through the carbonisation of coconut husk (pith and fibre) and the activation of chars with potassium hydroxide (KOH), respectively. The synthesised adsorbents were characterised by using scanning electron microscopy, Fourier transform infrared spectroscopy and nitrogen adsorption/desorption analysis. The elemental mercury removal performance was measured using a conventional flow type packed-bed adsorber. The physical and chemical properties of the adsorbents changed as a result of the carbonisation and activation process, hence affecting on the extent of elemental mercury adsorption. The highest elemental mercury (Hg°) adsorption capacity was obtained for the CP-CHAR (3142.57 µg g(-1)), which significantly outperformed the pristine and activated carbon adsorbents, as well as higher than some adsorbents reported in the literature.
Food wastes with high moisture and organic matter content are likely to emit odours as a result of the decomposition process. The management of odour from decomposing wastes is needed to sustain the interest of residents and local councils in the source separation of kitchen wastes. This study investigated the potential of baking soda (at 50 g, 75 g and 100g per kg food waste) to control odour from seven days stored food waste. It was found that 50 g of baking soda, spread at the bottom of 8l food wastes bin, can reduce the odour by about 70%. A higher amount (above 100g) is not advised as a pH higher than 9.0 may be induced leading to the volatilization of odorous ammonia. This research finding is expected to benefit the waste management sector, food processing industries as well as the local authorities where malodour from waste storage is a pressing issue.
In Malaysia, large amounts of organic materials, which lead to disposal problems, are generated from agricultural residues especially from palm oil industries. Increasing landfill costs and regulations, which limit many types of waste accepted at landfills, have increased the interest in composting as a component of waste management. The objectives of this study were to characterize compost feedstock properties of common organic waste materials available in Malaysia. Thus, a ratio modelling of matching ingredients for empty fruit bunches (EFBs) co-composting using different organic materials in Malaysia was done. Organic waste materials with a C/N ratio of < 30 can be applied as a nitrogen source in EFB co-composting. The outcome of this study suggested that the percentage of EFB ranged between 50% and 60%, which is considered as the ideal mixing ratio in EFB co-composting. Conclusively, EFB can be utilized in composting if appropriate feedstock in term of physical and chemical characteristics is coordinated in the co-composting process.
The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
Dairy cattle treated wastewaters are potential resources for production of microalgae biofuels. A study was conducted to evaluate the capability of Arthrospira platensis cultivated in dairy farm wastewater for biodiesel production. The biomass of Arthrospira platensis was found to be 4.98 g L-1 and produced 30.23 wt% lipids to dry biomass cultivated in wastewater which was found nitrogen stressed in photo bioreactor. The extracted lipid displayed a suitable fatty acid profile for biodiesel, although the content of linolenic acid was found a little higher than the standard EN14214. It was found that nitrogen stressed medium increase the total lipid content but temperature and intensities of light were the most important factors to control the quantity of linolenic acid and hence the quality of biodiesel, while the optimum CO2 helped to achieve maximum biomass and triacylglycerols. The Arthrospira platensis offer a good option for the treatment of wastewater before final discharge.
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
Presence of fat, oil, and grease (FOG) in wastewater is an ever-growing concern to municipalities and solid-waste facility operators. FOG enters the sewer system from restaurants, residences, and industrial food facilities. Its release into the sewer system results in a continuous build-up that causes eventual blockage of sewer pipes. Several researchers have investigated FOG deposition based on the local conditions of sewers and lifestyle. This paper attempts to review the physical and chemical characteristics of FOG, sources of FOG, and potential chemical and biological reactions of FOG. The effect of the aforementioned factors on the FOG-deposition mechanism is also discussed. Moreover, insight into the current control and treatment methods and potential reuse of FOG is highlighted. It is expected that this review would provide scientists and the concerned authorities a holistic view of the recent researches on FOG control, treatment, and reuse.
This study was carried out to investigate the physicochemical properties of compost from oil palm empty fruit bunches (EFB) inoculated with effective microorganisms (EM∙1™). The duration of microbial-assisted composting was shorter (∼7 days) than control samples (2 months) in a laboratory scale (2 kg) experiment. The temperature profile of EFB compost fluctuated between 26 and 52 °C without the presence of consistent thermophilic phase. The pH of compost changed from weak acidic (pH ∼5) to mild alkaline (pH ∼8) because of the formation of nitrogenous ions such as ammonium (NH4 (+)), nitrite (NO2 (-)), and nitrate (NO3 (-)) from organic substances during mineralization. The pH of the microbial-treated compost was less than 8.5 which is important to prevent the loss of nitrogen as ammonia gas in a strong alkaline condition. Similarly, carbon mineralization could be determined by measuring CO2 emission. The microbial-treated compost could maintain longer period (∼13 days) of high CO2 emission resulted from high microbial activity and reached the threshold value (120 mg CO2-C kg(-1) day(-1)) for compost maturity earlier (7 days). Microbial-treated compost slightly improved the content of minerals such as Mg, K, Ca, and B, as well as key metabolite, 5-aminolevulinic acid for plant growth at the maturity stage of compost. Graphical Abstract Microbial-assisted composting on empty fruit bunches.