81 research outputs found
Reusable rainwater quality at Ikorodu area of Lagos, Nigeria: Impact of first-flush and household treatment techniques
YesWater scarcity is a huge problem in Africa, and hence rainwater becomes a crucial water source for fulfilling basic human needs. However, less attention has been given by African countries to the effectiveness of common rainwater treatments to ensure the population's health. This study investigates the impact of different household treatment techniques (HHTTs), i.e. treatments by chlorine, boiling, alum, and a combination of alum and chlorine, on its storage system using a case study at the Ikorodu area of Lagos state, which is a rural area in Nigeria. The first-flush quality has been particularly studied here, where the microbial reduction through its practice has been examined from five different roofs. One of the investigated roofs was from a residential building, and four were constructed for the purpose of this study. In this study, the physical parameters (i.e. total suspended solids and turbidity) and the microbial parameters (i.e. total coliform and Escherichia coli) of the collected rainwater have been investigated. From the results, it has been observed that: (1) the water quality at the free phase zone is better than that at the tank's bottom; (2) the combination of chlorine and alum gives the best rainwater quality after comparing the application of different HHTTs; and (3) a reduction of about 40% from the original contaminant load occurs in every 1 mm diversion.Hidden Histories of Environmental Science Grant Project (at Seed-grant Stage), funded by the Natural Environment Research Council (NERC) and Arts and Humanities Research Council (AHRC), part of UK Research and Innovation (UKRI
Effects of wastewater type on stability and operating conditions control strategy in relation to the formation of aerobic granular sludge – a review
Currently, research trends on aerobic granular sludge (AGS) have integrated the operating conditions of extracellular polymeric substances (EPS) towards the stability of AGS systems in various types of wastewater with different physical and biochemical characteristics. More attention is given to the stability of the AGS system for real site applications. Although recent studies have reported comprehensively the mechanism of AGS formation and stability in relation to other intermolecular interactions such as microbial distribution, shock loading and toxicity, standard operating condition control strategies for different types of wastewater have not yet been discussed. Thus, the dimensional multi-layer structural model of AGS is discussed comprehensively in the first part of this review paper, focusing on diameter size, thickness variability of each layer and diffusion factor. This can assist in facilitating the interrelation between disposition and stability of AGS structure to correspond to the changes in wastewater types, which is the main objective and novelty of this review
Comparison between genetic algorithm and linear programming approach for real time operation
Prediction of scour caused by 2D horizontal jets using soft computing techniques
This paper presents application of five soft-computing techniques, artificial neural networks, support vector regression, gene expression programming, grouping method of data handling (GMDH) neural network and adaptive-network-based fuzzy inference system, to predict maximum scour hole depth downstream of a sluice gate. The input parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, jet Froude number and the tail water depth. Six non-dimensional parameters were achieved to define a functional relationship between the input and output variables. Published data were used from the experimental researches. The results of soft-computing techniques were compared with empirical and regression based equations. The results obtained from the soft-computing techniques are superior to those of empirical and regression based equations. Comparison of soft-computing techniques showed that accuracy of the ANN model is higher than other models (RMSE = 0.869). A new GEP based equation was proposed
Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia
Abstract Climate change is not a myth. There is enough evidence to showcase the impact of climate change. Town planners and authorities are looking for potential models to predict the climatic factors in advance. Being an agricultural area in Saudi Arabia, Tabuk region gets greater interest in developing such a model to predict the atmospheric temperature.Therefore, this paper presents two different studies based on artificial neural networks (ANNs) and gene expression programming (GEP) to predict the atmospheric temperature in Tabuk. Atmospheric pressure, rainfall, relative humidity and wind speed are used as the input variables in the developed models. Multilayer perceptron neural network model (ANN model), which is high in precession in producing results, is selected for this study. The GEP model that is based on evolutionary algorithms also produces highly accurate results in nonlinear models. However, the results show that the GEP model outperforms the ANN model in predicting atmospheric temperature in Tabuk region. The developed GEP-based model can be used by the town and country planers and agricultural personals. Graphical abstrac
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Experimental observation of turbulent structure at region surrounding the mid-channel braid bar
NoRiver morphological processes are among the most complex and least understood phenomenon in nature. Recent research indicates that the braiding of marine waterways of the estuary zone occurs at an aspect ratio similar to the alluvial braided river. The instability of complex sporadic fluvial processes at river-sea interface is responsible for bar formation in alluvial as well as in marine waterbodies Due to the lack of knowledge of flow characteristics around bar, the flow structure around the sand bar is analyzed. The bursting events play the crucial role in understanding the fluvial characteristics in the vicinity of submerged structure. The study of bursting events around the mid-channel bar is only done by the present author. The effect of submergence ratio on the turbulence behavior in the proximity of bar is analyzed in this study. The flow turbulence generated by the mid-channel bar is also analyzed in detail. The extreme turbulent burst is segregated from low intensity turbulent events by using the hole size concept. The effect of hole size on the parameter Dominance Function is analysed which is not yet studied by any researcher for
mid-channel bar. The Momentum Dominance Function (MDF) parameter increases with increase in the Hole Size. This indicates that the magnitude of upward flux increases with increase in the hole size. The effect of bar height on the turbulent burst which is not yet studied by any researchers is analyzed in the present research. The joint probability distribution of bursting events is modeled using the Gram-Charlier bivariate joint probability function. The joint probability distribution gives the details of probabilistic structure of flow in the vicinity of bar. The effect of bar is predominant only in the lower flow layer. The joint probability distribution graph becomes more eccentric toward the dominant quadrants with increase in the submergence ratio. This indicates
that the probability of dominant events further increases with increase in the submergence ratio
Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models
Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1]
Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers
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