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This study demonstrated the degradation of unionized acetate in anaerobic digestion at 45°C through the identification of kinetics and inhibition parameters. The kinetic parameters, Ks and rmax, were determined using Monod-based model for three different conditions, namely uninhibited, inhibited, and systems with high substrate condition. The Ks values for uninhibited condition were in the range of 0.124 to 0.191 mg/L as unionized HAc. Ks value of inhibited condition were at 0.027 mg/L as unionized HAc. Ks values for systems with high substrate condition were found to be in the range of 0.237 to 0.279 mg/L as unionized HAc. As for rmax, a 35°C anaerobic digestion system showed the highest value at 0.166 mg/L/day - greater than the values of all 45°C systems under all experimental conditions. Additionally, the inhibition parameter KI was also determined using the Michaelis-Menten model. The parameter was determined for inhibitory conditions resulting from high free NH3 content. The inhibition type was uncompetitive with KI value of 0.072 mg/L as unionized HAc. The outcomes suggested that the methanogens responsible for the digestion process at 45°C were thermo-tolerant acetateutilizing methanogens of Methanosarcinaceae species, and the system will be totally inhibited with the presence of high free NH3 content. [ABSTRACT FROM AUTHOR]

A novel material, Tris coated magnetic nano structured adsorbent (T-MNSA) was synthesized using incipient wetness impregnation method for removal of bisphenol A (BPA). T-MNSA maximum adsorption capacity was 160 mg/g and sorption density was 0.078 mole BPA/mole of Tris. In addition, degradation using ultrasound assisted bismuth ferrite (BF) sono-catalyst was studied for removal and cost efficiency differences. The degradation results show a complete removal of BPA at condition of 0.10 g/l of BF, 100% power, and 72 kHz frequency. The mechanism study reveals that T-MNSA forms biomolecules and covalently attaches to BPA, but BF generates large amount of H2O2 molecules and breaks down BPA to non-toxic CO2 and water. Cost analysis study reveals that T-MNSA is more economical than BF due to lower production and removal cost, almost ten times lower than degradation process using BF, and the material can be commercially produced in bulk quantities. [ABSTRACT FROM AUTHOR]

Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model. [ABSTRACT FROM AUTHOR]

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92. [ABSTRACT FROM AUTHOR]

• The highest MPR and yield were observed at thermophilic temperature. • Biological treatment of POME using integrated UASFF bioreactor is a clean method of producing biogas. • Treating POME using two-stage UASFF bioreactor is the first study in Malaysia. • More than 70% of total COD removal of POME could be achieved using integrated UASFF bioreactor. In a two-stage of up-flow anaerobic sludge fixed-film (UASFF) bioreactor, palm oil mill effluent (POME) wastewater was used as inoculum and substrate to study the biodegradation in association with hydrogen and methane production. During the first stage (H 2 -UASFF unit), different temperatures (37–70 °C) and hydraulic retention time (HRT) was designed and analyzed using response surface methodology (RSM) for biohydrogen production. In the second stage (CH 4 -UASFF unit), at 24 h HRT, a continuous experiment was carried out using the same temperature and effluent from H 2 -UASFF unit (also known as dark fermentation effluent) was used as substrate (12–20 g COD L−1). This study is focusing on the second stage of UASFF bioreactor (i.e. CH 4 -UASFF) for biomethane production. Studied parameters were designed using Historical Data in RSM. Its effects on methane production rate (MPR), methane yield, biogas percentage and COD removal were analyzed. The relation between initial effluent COD and temperature on biomethane production and overall reactor performance treating POME wastewater were also examined. Results showed that 76% of total COD removal efficiency (TCOD) was achieved using two-stage UASFF bioreactor. The MPR of 15.63 L CH 4 d−1, methane yield of 0.803 L CH 4 g−1 CODrem.d−1, COD removal efficiency of 66.28% and 93.31% of CH 4 at optimum temperature and effluent COD of 54 °C and 12 g COD L−1, respectively were obtained in CH 4 -UASFF unit. Based on this study, thermophilic temperature and low POME concentration showed a significant impact on biomethane production and more than 70% of TCOD of POME was reduced at 24 h HRT. [ABSTRACT FROM AUTHOR]

One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world. [ABSTRACT FROM AUTHOR]

The most important and indispensable substance for life on earth is water. Regrettably, the growths of civilization, population, and industrssialization have led to a steady deterioration in the quality of available water resources. Heavy metals are a particular problem because of their high toxicity. This work highlights up-to-date methods for the removal of heavy metals from water using the technique of adsorption. After a comprehensive review of the range of materials used as adsorbents to remove heavy metals, the paper focuses on one particular technique, involving carbon nanotubes (CNTs). Tests have confirmed that CNTs are highly efficient, but have also revealed some drawbacks. Further adjustments and refinements are therefore required to improve their ability to remove heavy metals. This study shows that using deep eutectic solvents (DESs) as a functionalization agent to replace ionic liquids (ILs) has a number of advantages, including lower costs, easier availability of materials and environmentally friendliness. In sum, it suggests that the most efficient adsorption process is to use functionalized CNTs with DESs as an adsorbent. The critical analysis and conclusions emerging from this study should be of benefit to engineers, environmental scientists and chemists interested in the use of carbon nanotubes for use in environmental remediation. • Heavy metals removal using different techniques and materials. • Deep eutectic solvents (DESs) an environmentally friendly functionalization agent. • The applications in the nanotechnology for heavy metals removal. [ABSTRACT FROM AUTHOR]

A start-up study was conducted to produce biohydrogen and biomethane from Palm Oil Mill Effluent (POME) using a two-stage up-flow anaerobic sludge fixed-film (UASFF) bioreactor. 100% molasses was used to start the system, and POME was added at 10% increments until it reached 100% after 59 days. During this period of continuous operation, the HRT and temperature were adjusted in order to optimize the condition for biogas production. Hydrogen and methane gas production fluctuated between 53–70% and 90–95%, respectively, in the last four days of operation (days 56–59), with POME percentage being increased from 70% to 100% (30%–0% molasses). Using 100% raw POME led to a total COD removal of 83.70%, average gas production rates of 5.29 L H 2 d−1 (57.11% H 2) and 9.60 L CH 4 d−1 (94.08% CH 4), in their respective units. This output is comparable to, if not better than using 100% molasses as substrate. This work concludes that based on the relative consistency of biogas production on days 56–59, the two-stage UASFF bioreactor operating at a final HRT of 4 h and temperature of 43 °C has taken a period of two months for start-up. Image 1 • A total COD removal efficiency of 83.70% was achieved using raw POME in two-stage UASFF reactor. • Short-term and stable H 2 and CH 4 production were established in thermophilic UASFF bioreactor. • Highest H 2 percentage and HPR was 57.11% and 5.29 L d−1 using 100% raw POME. • Highest CH 4 percentage and MPR was 94.08% and 9.60 L d−1 using 100% raw POME. [ABSTRACT FROM AUTHOR]

• Decision tree model for water quality index prediction proposed in this study. • Authors identified the inter-relationship of WQI with different sets of parameters. • The proposed methodology is offering as cost-effective approach for WQI estimation. The development of water quality prediction models is an important step towards better water quality management of rivers. The traditional method for computing WQI is always associated with errors due to the protracted analysis of the water quality parameters in addition to the great effort and time involved in gathering and analyzing water samples. In addition, the cost of identifying the magnitude of some of the parameters through experimental testing is very high. The water quality of rivers in Malaysia is ranked into five classes based on water quality index (WQI). WQI is function of six water quality parameters: ammoniac nitrogen (NH 3 -N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). In this research, the decision tree machine learning technique is used to predict the WQI for the Klang River and its classification within a specific water quality class. Klang River is one of the most polluted rivers in Malaysia. Modeling experiments are designed to test the prediction and classification accuracy of the model based on various scenarios composed of different water quality parameters. Results show that the proposed prediction model has a promising potential to predict the class of the WQI. Moreover, the proposed model offers a more efficient process and cost-effective approach for the computation and prediction of WQI. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. [ABSTRACT FROM AUTHOR]

Abstract In this study two deep eutectic solvents (DESs) were prepared using ethylene glycol (EG) and two different ammonium-based salts. The potential of these DESs as novel agents for CNTs functionalization was examined by performing a comprehensive characterization study to identify the changes developing after the functionalization process. The impact of DESs was obvious by increasing the surface area of CNTs to reach 197.8 (m2/g), and by adding new functional groups to CNTs surface without causing any damage to the unique structure of CNTs. Moreover, CNTs functionalized with DESs were applied as new adsorbents for the removal of methyl orange (MO) from water. The adsorption conditions were optimized using RSM-CCD experimental design. The kinetics and the equilibrium adsorption data were analyzed using different kinetic and isotherm models. According to the regression results, adsorption kinetics data were well described by pseudo-second order model, whereas adsorption isotherm data were best represented by Langmuir isotherm model. The highest recorded maximum adsorption capacity (q max) value was found to be 310.2 mg/g. Graphical abstract Image 1 Highlights • DESs were used as functionalization agents for CNTs. • DES-CNTs combination was introduced as new adsorbent for Methyl orange (MO). • The dispersion of CNTs was enhanced after functionalization with DESs. • The highest maximum adsorption capacity of DES-CNTs adsorbent was 310.2 mg/g. [ABSTRACT FROM AUTHOR]

Abstract Obtaining successful operation rules for dam and reservoir systems is crucial for improving water management to meet the increase in agricultural, domestic and industrial activities. Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. The main purpose of an operation rule is to minimize the gap between water supply and water demand patterns. To examine the optimized model performance, the simulation of a dam and reservoir system is usually carried out for a particular period utilizing the generated operation rule. During the simulation procedure, although reservoir inflow and evaporation are stochastic variables that are required to be forecasted during simulation, they are considered deterministic variables. This study attempts to integrate a forecasting model for reservoir inflow and evaporation with the operation rules generated from optimization models during the simulation procedure. The present study employs several optimization models to generate an optimal operation rule and two different forecasting models for reservoir inflow and reservoir evaporation. The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). Two different forecasting models have been developed for reservoir inflow and evaporation using the radial basis function neural network (RBF-NN) and support vector regression (SVR). It is necessary to analyze the proposed simulation procedure for examining the operation rule to comprehend the analysis under different optimal operation rules and levels of accuracy for both hydrological variables. The suggested models have been applied to generate optimal operation policies and reservoir inflow and evaporation forecasts for the Timah Tasoh dam (TTD) located in Malaysia. The results show that the major findings regarding the model performance during the simulation period indicate the necessity to pay attention to evaluating the optimized model performance by considering the results of the forecasting model for both the hydrological variables of reservoir inflow and reservoir evaporation rather than the deterministic values. [ABSTRACT FROM AUTHOR]

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model’s efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month−1), root mean square error (1.14 BCM month−1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region. [ABSTRACT FROM AUTHOR]

A batch study for biohydrogen production was conducted using raw palm oil mill effluent (POME) and POME sludge as a feed and inoculum respectively. Response Surface Methodology (RSM) was used to design the experiments. Experiments were conducted at different reaction temperatures (30–50 °C), inoculum size to substrate ratios (I:S) and reaction times (8–24 h). An optimum condition of biohydrogen production was achieved with COD removal efficiency of 21.95% with hydrogen yield of 28.47 ml H 2 g −1 COD removed. The I:S ratio was 40:60, with reaction temperature of 50 °C at 8 h of reaction time. The study showed that a lower substrate concentration (less than 20 g L −1 ) for biohydrogen production using pre-settled POME was achievable, with optimum HRT of 8 h under thermophilic condition (50 °C). This study also found that pre-settled POME is feasible to be used as a substrate for biohydrogen production under thermophilic condition. [ABSTRACT FROM AUTHOR]

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10−5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99. [ABSTRACT FROM AUTHOR]