Removal of Naphthalene, Fluorene and Phenanthrene by Recyclable Oil Palm Leaves’ Waste Activated Carbon Supported Nano Zerovalent Iron (N-OPLAC) Composite in Wastewater

Despite keen interest in the development of efficient materials for the removal of polycyclic aromatic hydrocarbons (PAHs) in wastewater, the application of advanced composite materials is still unexplored and needs attention. Therefore, this study focused on the synthesis of the composite of oil palm leaves’ waste activated-carbon (OPLAC) and nano zerovalent iron (NZVI) at Fe:OPLAC = 1:1 (N-OPLAC-1) and 1:2 (N-OPLAC-2). The composite with enhanced surface properties was applied for removal of three PAHs including naphthalene (NAP), fluorene (FLU) and phenanthrene (PHE) in wastewater at various pH, dosages, contact time and initial concentration in batch testing. The PAHs’ removal parameters were optimized using design expert software. The PAHs’ removal efficiency was evaluated in produced water at optimized parameters. The results showed that the N-OPLAC-2 had superior surface properties compared to N-OPLAC-1. The removal of NAP, FLU and PHE was heterogenous, favorable and involved chemisorption proved by Freundlich isotherm and pseudo-second-order kinetic models using N-OPLAC-2. The optimum parameters were as follows: pH of 3, dosage and contact time of 122 mg/L and 49 min, respectively. The application of N-OPLAC-2 in produced water was favorable for removal of NAP, FLU and PHE and showed up to 90% removal efficiency, and higher stability up to 3 cycles. It can be concluded that the NZVI-OPLAC composite was successfully synthesized in this study and the materials showed good removal efficiency for three PAHs (NAP, FLU and PHE) in wastewater

Modified Activated Carbon Synthesized from Oil Palm Leaves Waste as a Novel Green Adsorbent for Chemical Oxygen Demand in Produced Water

Palm tree waste is one of the most widespread forms of agricultural waste, particularly in areas where oil palms are cultivated, and its management is one of the industry’s key concerns. To deal with this palm waste, researchers are working hard to work out the ways to convert this plentiful waste into useful material for future beneficial applications. The objective of this study was to employ chemical activation techniques to prepare a new activated carbon (AC) using discarded oil palm leaves (OPL) in Malaysia. Three chemical agents (H3PO4, NaOH and ZnCl2), as well as three pyrolysis temperatures (400 °C, 600 °C and 800 °C) and various impregnation ratios (1:0.5–1:3) were used to optimize the preparation process. As a result, the oil palm leaves activated carbon (OPLAC), with prominent surface properties, was obtained by ZnCl2 activations with a 1:1 impregnation ratio and carbonized at a pyrolysis temperature of 800 °C. The OPLAC-ZC had a surface area of 331.153 m2/g, pore size of 2.494 nm and carbon content of 81.2%. Results showed that the OPLAC-ZC was able to quickly (90 min) remove the chemical oxygen demand (COD) from produced water (PW), through chemical adsorption and an intraparticle diffusion mechanism. The material followed pseudo-second order kinetic and Freundlich isotherm models. The maximum adsorption capacity of organic pollutants forming COD in PW was found to be 4.62 mg/g (59.6 ± 5%). When compared to previous studies, the OPLAC-ZC showed equivalent or better COD removal capability. It is the first detailed study reporting the preparation of AC from OPL and applying it for organic pollutants adsorption forming COD in PW

A Comprehensive Insight on Adsorption of Polyaromatic Hydrocarbons, Chemical Oxygen Demand, Pharmaceuticals, and Chemical Dyes in Wastewaters Using Biowaste Carbonaceous Adsorbents

Recent trends in adsorption of hazardous organic pollutants including Polyaromatic Hydrocarbons (PAHs), Chemical Oxygen Demand (COD), Pharmaceuticals, and Chemical Dyes in wastewater using carbonaceous materials such as activated carbon (AC) and biochar (BC) have been discussed in this paper. Utilization of biomass waste in the preparation of AC and BC has gained a lot of attention recently. This review outlines the techniques used for preparation, modification, characterization, and application of the above-mentioned materials in batch studies. The approaches towards understanding the adsorption mechanisms have also been discussed. It is observed that in the majority of the studies, high removal efficiencies were reported using biowaste adsorbents. Regarding the full potential of adsorption, varying values were obtained that are strongly influenced by the adsorbent preparation technique and adsorption method. In addition, most of the studies were concentrated on the kinetic, isotherm equilibrium, and thermodynamic aspects of adsorption, suggesting the dominant isotherm and kinetic models as Langmuir or Freundlich and pseudo-second-order models. Due to development in biosorbents, adsorption has been found to be increasingly economical. However, application of these adsorbents at commercial scale has not been adequately investigated and needs to be studied. Most of the studies have been conducted on synthetic solutions that do not completely represent the discharged effluents. This also needs attention in future studies

Modified Activated Carbon Synthesized from Oil Palm Leaves Waste as a Novel Green Adsorbent for Chemical Oxygen Demand in Produced Water

Palm tree waste is one of the most widespread forms of agricultural waste, particularly in areas where oil palms are cultivated, and its management is one of the industry’s key concerns. To deal with this palm waste, researchers are working hard to work out the ways to convert this plentiful waste into useful material for future beneficial applications. The objective of this study was to employ chemical activation techniques to prepare a new activated carbon (AC) using discarded oil palm leaves (OPL) in Malaysia. Three chemical agents (H3PO4, NaOH and ZnCl2), as well as three pyrolysis temperatures (400 °C, 600 °C and 800 °C) and various impregnation ratios (1:0.5–1:3) were used to optimize the preparation process. As a result, the oil palm leaves activated carbon (OPLAC), with prominent surface properties, was obtained by ZnCl2 activations with a 1:1 impregnation ratio and carbonized at a pyrolysis temperature of 800 °C. The OPLAC-ZC had a surface area of 331.153 m2/g, pore size of 2.494 nm and carbon content of 81.2%. Results showed that the OPLAC-ZC was able to quickly (90 min) remove the chemical oxygen demand (COD) from produced water (PW), through chemical adsorption and an intraparticle diffusion mechanism. The material followed pseudo-second order kinetic and Freundlich isotherm models. The maximum adsorption capacity of organic pollutants forming COD in PW was found to be 4.62 mg/g (59.6 ± 5%). When compared to previous studies, the OPLAC-ZC showed equivalent or better COD removal capability. It is the first detailed study reporting the preparation of AC from OPL and applying it for organic pollutants adsorption forming COD in PW

A Study of Third Hankel Determinant for Certain Subclasses of Analytic Function

Recently the Hankel determinant problems got attractions of many well-known authors. Third Hankel determinant problems were determined for different subclasses of analytic functions. Here in our present investigation, we define certain new subclasses of analytic functions and then we obtain the upper bonds for the third Hankel determinant

Application of the Response Surface Methodology (RSM) in the Optimization of Acenaphthene (ACN) Removal from Wastewater by Activated Carbon

The presence of polycyclic aromatic hydrocarbons (PAHs) in wastewater has raised concerns about human health due to their potential carcinogenic and mutagenic properties. The widespread use of products containing acenaphthene (ACN, one of the 16 priority PAHs) in many industries and large-scale ACN release into the wastewater has resulted in dangerous concentrations of ACN in the environment. As a result, before discharge, it is required to eliminate or reduce its concentration to an acceptable level. Adsorption is an effective method of removing PAHs from wastewater. In this study, the ACN adsorption reaction in sample wastewater was evaluated using activated carbon produced by oil palm leaves. HPLC was used as an analytical method for quantifying ACN in wastewater samples. The initial concentration of ACN in water samples was 9.58 ± 0.5 mg/L. The experiments were conducted using the CCD combined with the RSM and using three independent variables, i.e., pH, activated carbon dosage (g/L), and contact time (min), and one dependent variable, i.e., ACN removal efficiency (%). The ANOVA was used to identify the significance of the developed model in the RSM. Lastly, the RSM was used to optimize the adsorption results. The experimental results determined that the removal of 98.73 ± 1% of ACN (the highest amount) was achieved at pH 7, while the removal of 88.44 ± 1% of ACN (the lowest amount) was achieved at pH 4.5. The adsorption efficiency of ACN was slightly increased by an increase in activated carbon dosage from 0.1 to 3 g/L (<4%). The contact time was the most significant factor in controlling the adsorption efficiency of ACN in wastewater, and not pH value or dosage. The adsorption reaction was quick, and 88–90% of ACN was removed within 5 min of the adsorption reaction, followed by slower adsorption for up to 90 min. The RSM model was developed on the basis of experimental results. An ANOVA determined that the developed model was significant enough to represent the adsorption data as the p-value was <0.05 for the model. The factors pH, adsorbent dosage, and contact time were also significant factors (p-value < 0.05). The optimization results showed that pH of 6.96, adsorbent dosage of 2.62 g/L, and contact time of 71.67 min were the optimal conditions for eliminating 98.88% of the ACN. The optimization results were verified in the lab, and a close agreement was found between the predicted results of the RSM and experimental results. The study found that the RSM is an effective tool for optimizing operating variables, as well as for significantly reducing time and experimentation costs

Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater

This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. Maximum COD (88.9%) and TOC (98.8%) removal were predicted at pH of 7, a dosage of 300 mg/L, and contact time of 60 mins using ANFIS-surface plots. The optimization approaches showed the performance in the following order: ANFIS-surface plots>ANN-GA>RSM-GA>RSM

Adsorptive removal of COD from produced water using tea waste biochar

This study was conducted to explore the effectiveness of tea waste (TW) biochar (BC) as an adsorbent for the oxidizable organic contaminants measured as chemical oxygen demand (COD) in produced water (PW). BCs were prepared by modifying the TW with single (pre-pyrolysis) and combined (pre and post pyrolysis) treatments using phosphoric acid and hydrogen peroxide solutions. Based on FTIR, XPS, XRD and BET characterizations, the combined modified BC had higher oxygen-containing functional groups (-OH and -COOH), surface area (82 ± 0.50 m2/g) and pore volume (0.08 ± 0.001 cm 3/g) compared to single modified BC (60 ± 0.50 m2/g, 0.02 ± 0.002 cm 3/g). The Langmuir monolayer adsorption model best fitted both BCs with separation factor < 1, showing favorable adsorption process. The controlling mechanism of the adsorption process was best described by the pseudo-second-order kinetic model with a coefficient of determination value of 0.995. The particle diffusion mechanism was demonstrated by the Weber–Morris plot. Taguchi method was used in Minitab 19 for optimization of operating factors i.e., pH, contact time and BC dosage. Maximum COD removal efficiencies were found to be 89.35 ± 0.5% and 95.5 ± 0.5% for single and combined modified BCs, respectively. The study provides a successful approach towards high level of COD removal from PW while reducing the waste generation and protecting the environment

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR&rsquo;s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population&rsquo;s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box&ndash;Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA&ndash;ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA&ndash;ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA&ndash;ANN hybrid model could be a viable option for the accurate prediction of reservoir water level