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• 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]

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]

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]

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]