| 1. |
- Abba, S. I., et al.
(författare)
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Effluents quality prediction by using nonlinear dynamic block-oriented models : A system identification approach
- 2021
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Ingår i: Desalination and Water Treatment. - : Desalination Publications. - 1944-3994 .- 1944-3986. ; 218, s. 52-62
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Tidskriftsartikel (refereegranskat)abstract
- The dynamic and complex municipal wastewater treatment plant (MWWTP) process should be handled efficiently to safeguard the excellent quality of effluents characteristics. Most of the available mathematical models do not efficiently capture the MWWTP process, in such cases, the data-driven models are reliable and indispensable for effective modeling of effluents characteristics. In the present research, two nonlinear system identification (NSI) models namely; Hammerstein-Wiener model (HW) and nonlinear autoregressive with exogenous (NARX) neural network model, and a classical autoregressive (AR) model were proposed to predict the characteristics of the effluent of total suspended solids (TSSeff) and pHeff from Nicosia MWWTP in Cyprus. In order to attain the optimal models, two different combinations of input variables were cast through auto-correla-tion function and partial auto-correlation analysis. The prediction accuracy was evaluated using three statistical indicators the determination coefficient (DC), root mean square error (RMSE) and correlation coefficient (CC). The results of the appraisal indicated that the HW model outperformed NARX and AR models in predicting the pHeff, while the NARX model performed better than the HW and AR models for TSSeff prediction. It was evident that the accuracy of the HW increased averagely up to 18% with regards to the NARX model for pHeff . Likewise, the TSSeff performance increased averagely up to 25% with regards to the HW model. Also, in the validation phase, the HW model yielded DC, RMSE, and CC of 0.7355, 0.1071, and 0.8578 for pHeff, while the NARX model yielded 0.9804, 0.0049 and 0.9902 for TSSeff, respectively. For comparison with the traditional AR, the results showed that both HW and NARX models outperformed in (TSSeff) and pHeff prediction at the study location. Hence, the outcomes determined that the NSI model (i.e., HW and NARX) are reliable and resilient modeling tools that could be adopted for pHeff and TSSeff prediction.
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| 2. |
- Abba, S.I., et al.
(författare)
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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
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| 3. |
- Al-Janabi, Ahmed Mohammed Sami, et al.
(författare)
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Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.
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| 4. |
- Alsafadi, Karam, et al.
(författare)
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An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East
- 2022
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Ingår i: Environmental Research Letters. - UK : Institute of Physics Publishing (IOPP). - 1748-9326. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO2) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO2 emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.
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| 5. |
- Amari, Abdelfattah, et al.
(författare)
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Investigation of the viable role of oil sludge-derived activated carbon for oily wastewater remediation
- 2023
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- A wide range of studies has been carried out to describe the equilibrium data of adsorption for the surface adsorption process. However, no extensive investigation has been carried out to evaluate the oil sludge based activated carbon surface adsorption. Therefore, the possibility of carbon active production using different oil sludges and consequently the adsorption mechanism of these kind of adsorbents is still unknown. In this study, a novel low-cost approach was introduced to synthesize the activated carbon using oil sludge applying a two-step process including carbonization and chemical activation. In this way, four different types of oil sludges were characterized and then applied to synthesize different carbon actives and their performance were investigated as an adsorbent. The results showed that all synthesized activated carbons, with about 6% ash and pH = 7 and the specific surface area of 110 m2/gr, have the ability to treatment of oily wastewater; which can be referred to the high carbon content (>80%). The iodine number and the efficiency of prepared activated carbon were obtained as 406.8 mg/g and 94%, respectively. The adsorption process was also studied at different process conditions such as temperature (308–338 K), pH value (3–9) and adsorbent amount (50–200 mg/L) to find the optimum condition for wastewater treatment. The results show that the pH value has an optimum in the adsorption rate (the maximum adsorption was measured at pH = 5) and the adsorption capacity can be reduced by increasing the temperature or decreasing the adsorbent amount. Moreover, three different adsorption isotherm models were applied, i.e., Langmuir, Temkin, and Freundlich isotherms; which the Langmuir equation was more suitable than others investigated isotherm models with R2 ≈ 0.999.
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| 6. |
- Bhat, Shakeel Ahmad, et al.
(författare)
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Application of Biochar for Improving Physical, Chemical, and Hydrological Soil Properties: A Systematic Review
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:17
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Forskningsöversikt (refereegranskat)abstract
- Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility.
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| 7. |
- El Jery, Atef, et al.
(författare)
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Industrial oily wastewater treatment by microfiltration using silver nanoparticle-incorporated poly (acrylonitrile-styrene) membrane
- 2023
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Ingår i: Environmental Sciences Europe. - : Springer. - 2190-4707 .- 2190-4715. ; 35:1
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Tidskriftsartikel (refereegranskat)abstract
- Membrane filtration exhibit operational limitations such as biofouling, which leads to concentration polarization and reduces permeability and selectivity, despite advantages such as low operating cost, high selectivity, and permeability. In recent years, the antibacterial properties of silver nanoparticles (AgNPs) have been investigated for improving membrane processes; however, the fouling phenomena in presence of AgNPs in the membrane matrix have not been fully discussed. Herein, the antifouling properties of a poly (acrylonitrile-styrene) copolymer incorporated with AgNPs were studied in a microfiltration membrane process. The Creighton method was used to synthesize AgNPs, and the effects of AgNPs on the porosity, morphology, pore size, mechanical strength, permeability, and selectivity of the membranes were investigated. Moreover, to investigate the biofouling of the obtained membranes, microfiltration of industrial oily wastewater was performed at constant pressure over three cycles. Using AgNPs in the membrane matrix resulted in enhanced antifouling properties of the copolymer membrane, which is related to the structure of the AgNPs in the casting solution, as proven by SAXS analysis. The results show that the CFU% for Staphylococcus aureus and E.coli reach 2% and 6%, respectively. Finally, the Derjaguin–Landau–Verwey–Overbeek (DLVO) thermodynamic model was applied to study the antifouling mechanism, correctly predict the separation behavior in the membrane, and design, simulate, and optimize the separation processes in the membrane separation plantsa. The DLVO model could predict the separation behavior in the synthesized membranes, and the poly(acrylonitrile-styrene) copolymer membranes containing AgNPs were proven have promising industrial wastewater treatment applications.
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| 8. |
- El Jery, Atef, et al.
(författare)
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Isotherms, kinetics and thermodynamic mechanism of methylene blue dye adsorption on synthesized activated carbon
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject of extensive research. However, a comprehensive understanding of the thermodynamic aspects of dye solution adsorption is lacking. Previous studies have primarily focused on enhancing the adsorption capacity of methylene blue dye. This study aimed to develop an environmentally friendly and cost-effective method for treating methylene blue dye wastewater and to gain insights into the thermodynamics and kinetics of the adsorption process for optimization. An adsorbent with selective methylene blue dye adsorption capabilities was synthesized using rice straw as the precursor. Experimental studies were conducted to investigate the adsorption isotherms and models under various process conditions, aiming to bridge gaps in previous research and enhance the understanding of adsorption mechanisms. Several adsorption isotherm models, including Langmuir, Temkin, Freundlich, and Langmuir–Freundlich, were applied to theoretically describe the adsorption mechanism. Equilibrium thermodynamic results demonstrated that the calculated equilibrium adsorption capacity (qe) aligned well with the experimentally obtained data. These findings of the study provide valuable insights into the thermodynamics and kinetics of methylene blue dye adsorption, with potential applications beyond this specific dye type. The utilization of rice straw as an adsorbent material presents a novel and cost-effective approach for MB dye removal from wastewater.
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| 9. |
- El Jery, Atef, et al.
(författare)
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Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network
- 2023
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Ingår i: PeerJ. - : PeerJ Inc.. - 2167-8359. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques.
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| 10. |
- El Jery, Atef, et al.
(författare)
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Proposing empirical correlations and optimization of Nu and Sgen of nanofluids in channels and predicting them using artificial neural network
- 2023
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Ingår i: Case Studies in Thermal Engineering. - : Elsevier. - 2214-157X. ; 45
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Tidskriftsartikel (refereegranskat)abstract
- Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model's predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well.
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