Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Removal of organic pollutants from water using carbon nanotubes functionalized with deep eutectic solvents / Rusul Khaleel Ibrahim

Many industries discharge large amount of wastewater that constitute the attention of many environmental concerns because it contains toxic and persistent organic pollutants that pollute the nature and threaten the human health. Although, carbon nanotubes (CNTs) have a high adsorption capacity for the removal of various kinds of organic pollutants from water, many flaws are hindering their adsorption performance. Functionalization of CNTs is a decisive process to overcome all the restrictions of CNTs application and to increase their removal efficiency. Therefore, this research has been carried out to investigate the potential of deep eutectic solvents (DESs) as novel functionalization agents for carbon nanotubes (CNTs) which can open a new window of opportunity in the area of wastewater treatment. In this regard, ten DESs were synthesized using five different salts and two hydrogen bond donors (HBDs) (i.e. ethylene glycol and di-ethylene glycol). Various molar ratios of HBD to salts were prepared to determine the optimum molar ratio by which the DES is homogeneous and stable. The DESs freezing points and functional groups were investigated, in addition to their physical properties of viscosity, density, conductivity and surface tension were determined as function of temperature in the particular temperature range of 293.15- 353.15 K. It is worth mentioning that all examined DESs were stable and in liquid phase at room temperature which emphasize their promising potential to be utilized as inexpensive environment-friendlier solvents. Owing to their low recorded freezing points and viscosities, DESs can be effortlessly processed without any further heating required. Subsequently, the prepared DESs were used to functionalize CNTs and produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) and methylene orange (MO) from water. A primary screening of adsorption process was conducted, and the chemical, physical and morphological properties of the adsorbents with the highest removal efficiencies were investigated using RAMAN, FTIR, FESEM, zeta potential, TGA and BET surface area. The effect of DES was obvious by increasing the purity and the surface area of CNTs resulting in increasing the maximum adsorption capacity of CNTs for 2,4-DCP and MO removal to reach 290 mg/g and 224 mg/g, respectively. Adsorption studies were carried out to evaluate the optimum conditions, kinetics and isotherms for 2,4-DCP adsorption process. RSM-CCD experimental design was used to conduct the optimization studies and to determine the optimum conditions for 2,4-DCP and MO removal by each selected adsorbent individually. Furthermore, all experimental data fitted well the pseudo-second order kinetic model and the equilibrium data for all DES-functionalized adsorbents was well fitted by both Langmuir and Freundlich isotherm models

Adsorption of 2,4-dichlorophenol from water using deep eutectic solvents-functionalized carbon nanotubes

In this work, novel adsorbents for 2,4-dichlorphenol (DCP) were introduced using deep eutectic solvent (DES) as functionalization agent for multi-wall carbon nanotubes (MWCNTs). Choline chloride salt (ChCl) was mixed with ethylene glycol (EG) as hydrogen bond donor (HBD) at molar ratio of (1:2) to prepare DES. Three DES-based MWCNTs adsorbents were produced and their chemical, physical and morphological properties were investigated using, RAMAN, FTIR, FESEM, zeta potential, TGA, TEM and BET surface area. The capability of DES as non-destructive functionalization agent for MWCNTs was proved by the increase of the purity and the surface area of MWCNTs. Response surface methodology was used to define the optimum conditions for 2,4-DCP adsorption onto each adsorbent. The adsorption experimental data were well described by pseudo-second order kinetic mode land by Langmuir isotherm model. DES-acid treated MWCNTs showed the highest maximum adsorption capacity of 390.53 mg g −1

Physical properties of ethylene glycol-based deep eutectic solvents

Deep eutectic solvents (DESs) have been widely recognized as ionic liquids (ILs) analogues due to their low production cost and superior favorable properties over conventional ILs. Studying the physical properties of these solvents will contribute to design processes involving DESs. In this study, five DESs have been successfully prepared using ethylene glycol (EG) as hydrogen bond donor (HBD) with N,N‑diethylethanolammonium chloride (DAC), benzyltriphenylphosphonium chloride (BTPC), choline chloride (ChCl), methyltriphenylphosphonium bromide (MTPB) and tetra‑n‑butylammonium bromide (TBAB) as salts. The freezing point of each of the five studied DESs was measured using Differential Scanning Calorimetry (DSC) and the DES functional groups were identified using the Fourier transform infrared (FTIR) spectroscopy. Moreover, the viscosity, density, electrical conductivity and surface tension were measured at a temperature range of 293.15–353.15 K and they were highly affected by the variation of the temperature. It is worth mentioning that the studied DESs showed many physical characteristics similar to that of ILs, which boost their possibilities to be employed in interdisciplinary domains

The formation of hybrid carbon nanomaterial by chemical vapor deposition: an efficient adsorbent for enhanced removal of methylene blue from aqueous solution

In this study, carbon species were grown on the surface of Ni-impregnated powder activated carbon to form a novel hybrid carbon nanomaterial by chemical vapor deposition. The carbon nanomaterial was obtained by the precipitation of the methane elemental carbon atoms on the surface of the Ni catalyst. The physiochemical properties of the hybrid material were characterized to illustrate the successful growth of carbon species on the carbon substrate. The response surface methodology was used for the evaluation of adsorption parameters effect such as pH, adsorbent dose and contact time on the percentage removal of MB dye from aqueous solution. The optimum conditions were found to be pH = 11, adsorbent dose = 15 mg and contact time of 120 min. The material we prepared showed excellent removal efficiency of 96% for initial MB concentration of 50 mg/L. The adsorption of MB was described accurately by the pseudo-second-order model with R2 of 0.998 and qe of 163.93 (mg/g). The adsorption system showed the best agreement with Langmuir model with R2 of 0.989 and maximum adsorption capacity (Qm) of 250 mg/g

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs

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

Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome. © 2019 by the authors

Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model

The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively