Selection of Water Transmission Method and Analysis of Pipe Network Zoning in Municipal Water Supply and Drainage Design

With the acceleration of urbanization,the demand for water supply and drainage pipe networks has increased significantly.In the planning of urban construction,it is necessary to optimize the design of the water supply and drainage system pipe network to effectively save energy while providing residents with more accessible water resources.Therefore,the municipal water supply and drainage system and the water transmission methods should be designed according to the geographical conditions of the city.In this paper,we mainly analyze the design of municipal water supply and drainage systems and the selection of water transmission methods.Besides,the optimization of the water supply and drainage network zoning process and pipe network maintenance is also discussed,so as to provide a reference for municipal water supply and drainage work.

Research progress on the water vapor channel within the Yarlung Zsangbo Grand Canyon, China

The Second Tibetan Plateau Scientific Expedition and Research Program tasked a research team with the“Investigation of the water vapor channel of the Yarlung Zsangbo Grand Canyon(INVC)”in the southeastern Tibetan Plateau(TP).This paper summarizes the scientific achievements obtained from the data collected by the INVC observation network and highlights the progress in investigating the development of heavy rainfall events associated with water vapor changes.The rain gauge network of the INVC can represent the impacts of the Yarlung Zsangbo Grand Canyon(YGC)topography on precipitation at the hourly scale.The microphysical characteristics of the precipitation in the YGC are different than those in the lowland area.The GPM-IMERG(Integrated MultisatellitE Retrievals for Global Precipitation Measurement)satellite precipitation data for the YGC region should be calibrated before they are used.The meridional water vapor flux through the YGC is more important than the zonal flux for the precipitation over the southeastern TP.The decreased precipitation around the YGC region is partly due to the decreased meridional water vapor flux passing through the YGC.High-resolution numerical models can benefit precipitation forecasting in this region by using a combination of specific schemes that capture the valley wind and water vapor flux along the valley floor.

Impact of the Condition of Drinking Water Supply Networks on the Quality of Water Intended for Consumption: The Case of the Network in the Commune of Daloa (Central West of Côte d’Ivoire)

The potability of drinking water depends not only on the source and the treatment system, but also on the quality of the waterworks. In fact, the quality of drinking water is considerably degraded by the dilapidated state and lack of maintenance of drinking water networks. In Côte d’Ivoire, the majority of drinking water networks in the various towns are ageing. In Daloa, despite the efforts made by the company in charge of water treatment and distribution to make the water drinkable, the water at consumers’ taps is often colored, has an unpleasant aftertaste and settles after collection. As a result, people are concerned about the potability of tap water, and some are turning to alternative sources of drinking water of unknown quality. In order to determine the factors responsible for the deterioration in water color and taste, as well as the sectors of the network most affected, a diagnosis of the network’s equipment was carried out. Water samples taken from the network were analyzed for color and turbidity. The diagnosis revealed that most of the equipment (suction pads, valves, drains and fire hydrants) is outdated and irregularly maintained. Analyses show that the water is more colored in cast-iron and PVC pipes than in asbestos cement pipes. Coloration values in the network range from 0 to 27 UVC for asbestos cement pipes, from 15 to 56 UCV for ductile iron pipes, and from 11 to 102 UCV for PVC pipes. On the over hand, turbidity values vary from 8.02 to 3.32 NTU for ductile cast iron pipes, 8.51 to 16.98 NTU for asbestos cement pipes and 0.9 to 6.98 NTU for PVC pipes. Old cast-iron pipes release ferric ions on contact with water, degrading their color. Old cast-iron pipes release ferric ions into the water, degrading its color. The high color values observed in the vicinity of drains are thought to be due to irregular maintenance of the network. In fact, after network maintenance, a reduction rate ranging from 2% to 73% is observed for turbidity, while for color, the rate varies from 5% to 72%. In short, the network’s obsolescence and irregular maintenance contribute significantly to the deterioration of water quality.

High Water Resistance and Enhanced Mechanical Properties of Bio-Based Waterborne Polyurethane Enabled by in-situ Construction of Interpenetrating Polymer Network

In this study,acrylic acid was used as a neutralizer to prepare bio-based WPU with an interpenetrating polymer network structure by thermally induced free radical emulsion polymerization.The effects of the content of acrylic acid on the properties of the resulting waterborne polyurethane-poly(acrylic acid)(WPU-PAA)dispersion and the films were systematically investigated.The results showed that the cross-linking density of the interpenetrating network polymers was increased and the interlocking structure of the soft and hard phase dislocations in the molecular segments of the double networks was tailored with increasing the content of acrylic acid,leading to enhancement of the mechanical properties and water resistance of WPU-PAA films.Notably,with the increase in content of acrylic acid,the tensile strength,Young’s modulus,and toughness of the WPU-PAA-110 film increased by 3 times,and 8 times,and 2.4 times compared with WPU-PAA-80,respectively.The WPU-PAA-100 film showed the best water resistance,and the water absorption rate at 96 h was only 3.27%.This work provided a new design scheme for constructing bio-based WPU materials with excellent properties.

Horizontal Well Interference Performance and Water Injection Huff and Puff Effect on Well Groups with Complex Fracture Networks:A Numerical Study

Well interference has become a common phenomenon with the increasing scale of horizontal well fracturing.Recent studies on well interference in horizontal wells do not properly reflect the physical model of the postfracturing well groups and the realistic fracturing process of infill wells.Establishing the correspondence between well interference causative factors and manifestations is of great significance for infill well deployment and secondary oil recovery.In this work,we develop a numerical model that considers low velocity non-Darcy seepage inshale reservoirs to study the inter-well interferencephenomenon that occurs in theSantanghufield,andconstruct an explicit hydraulic fracture and complex natural fracture network model with an embedded discrete fracture model,focusing on the effect of fracture network morphology on well interactions.The model also considers a multi-segment wellbore model to accommodate the effect of inter-well crossflow on wellbore tubular flow.The changes in formation pressure and water saturation during fracturing are performed by controlling the injection pressure and water injection rate.The result shows that the shape of the fracture network generated by the infill well with the old well determines the subsequent fluid and oil-increasing performance of the disturbed well.The synergistic production or competitive relationship formed by fractures with different connectivity between the two wells determines the positive and negative effects of the interference.The paper also investigates the adaptation study of water injection huff and puff schemes for well groups with different connectivity,and demonstrated a potential yield increase of up to 10.85%under adaptation injection.This method of identifying well interference based on the production dynamics of affected wells and the subsequent corresponding water injection method provides valuable references for the selection of secondary oil recovery measures.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

Integrated assessment of sea water quality based on BP artificial neural network

In order to carry out an integrated assessment of sea water quality objectively, this paper based on the concept and principle of artificial neural network, generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold, established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model was used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations shown that pollution index in river＇s wet season was higher than that in dry season from 2004 to 2007, and the pollution was particularly serious in 2005 and 2006, but a little better in 2007. The assessed results of cases shown that the model was reasonable in design and higher in generalization, meanwhile, it was common, objective and practical to sea water quality assessment.

Application of Dynamic Programming Method in Optimization of Water Distribution Pipe Network

With a water-supply network by dynamic programming. The minimal as an example, the network was optimized annual discounted costs were taken as an objective function and node pressure etc. as constraint conditions. The alternative pipe diameters were optimized as per enumeration method and the group allowing objective function with the least values would be the optimized one. It is proved the optimized pipe network reduced by 11.49% in terms of cost and the optimized ben- efits proved much significant.

DEPTH PREDICTION OF RAIN WATER ON ROAD SURFACE BASED ON ARTIFICIAL NEURAL NETWORK

A model based on the non-linear artificial neural network （ANN） is established to predict the thickness of the water film on road surfaces. The weight and the threshold can be determined by training test data, and the water film thickness on the road surface can be accurately predicted by the empirical verification based on sample data. Results show that the proposed ANN model is feasible to predict the water film thickness of the road surface.

STUDY ON OPTIMAL CONTROL OF MUNICIPAL WATER DISTRIBUTION NETWORK

A systematic investigation is made on the problems which are related to the optimal control of the municipal water distribution network.A mathematical model of forecasting the water short term demand is proposed using the time series trigonometric function analysis method;the service discharge based macroscopic model of network performance is established using the network structuring method;a relatively satisfactory mathematical model for the optimal control of water distribution network is put forward in view of security and economy,and solved by the constrained mixed discrete variable complex arithmetic.The model is applied in many examples and the results are satisfactory.

Retrieval of Water Vapor Profiles with Radio Occultation Measurements Using an Artificial Neural Network

A new method applying an artificial neural network （ANN） to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the backpropagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application （CHAMP-ISDC）, it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

Water quality forecast through application of BP neural network at Yuqiao reservoir

This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value,the model adopts LM (Leven-berg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

Improving Energy Performance of Water Allocation Networks Through Appropriate Stream Merging

Water-using operations in the process industry have demands for water usually both on water quality and temperature, and the existing mathematical models of heat exchange networks cannot guarantee the energy performance of a water network optimal. In this paper, the effects of non-isothermal merging on energy performance of water allocation networks are analyzed, which include utility consumption, total heat exchange load, and number of heat exchange matches. Three principles are proposed to express the effects of non-isothermal merging on energy performance of water allocation networks. A rule of non-isothermal merging without increasing utility consumption is deduced. And an approach to improve energy performance of water allocation network is presented. A case study is given to demonstrate the method.

Application of an expert system using neural network to control the coagulant dosing in water treatment plant

The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.

Application of Artificial Neural Networks for the Prediction of Water Quality Variables in the Nile Delta

The quality of a water body is usually characterized by sets of physical, chemical, and biological parameters, which are mutually interrelated. Since August 1997, monthly records of 33 parameters, monitored at 102 locations on the Nile Delta drainage system, are stored in a National Database operated by the Drainage Research Institute (DRI). Correlation patterns may be found between water quantity and water quality parameters at the same location, or among water quality parameters within a monitoring location or among locations. Serial correlation is also detected in water quality variables. Through the investigation of the level of information redundancy, assessment and redesign of water quality monitoring network aim to improve the overall network efficiency and cost effectiveness. In this study, the potential of the Artificial Neural Network (ANN) on simulating interrelation between water quality parameters is examined. Several ANN inputs, structures and training possibilities are assessed and the best ANN model and modeling procedure is selected. The prediction capabilities of the ANN are compared with the linear regression models with autocorrelated residuals, usually used for this purpose. It is concluded that the ANN models are more accurate than the linear regression models having the same inputs and output.

Relationship Between Changes of River-lake Networks and Water Levels in Typical Regions of Taihu Lake Basin,China

The typical regions of the Taihu Lake Basin,China,were selected to analyze the variation characteristics of river-lake networks under intensive human activities.The characteristics of the fractal dimension of river networks and lakes for different periods were investigated and the influences of river system evolution on water level changes were further explored through the comparison of their fractal characters.The results are as follows:1) River network development of the study area is becoming more monotonous and more simple;the number of lakes is reducing significantly,and the water surface ratio has dropped significantly since the 1980s.2) The box dimension of the river networks in all the cities of the study area decreased slowly from the 1960s to the 1980s,while the decrease was significant from the 1980s to the 2000s.The variations of lake correlation dimension are similar to those of the river network box dimensions.This is unfavorable for the storage capacity of the river networks and lakes.3) The Hurst exponents of water levels were all between 0.5 and 1.0 from the 1960s to the 1980s,while decreased in the 2000s,indicating the decline in persistence and increase in the complexity of water level series.The paper draws a conclusion that the relationship between the fractal dimension of river-lake networks and the Hurst exponents of the water level series can reveal the impacts of river system changes on flood disasters to some extent:the disappearance of river networks and lakes will increase the possibility of flood occurrence.

Meteorological applications of precipitable water vapor measurements retrieved by the national GNSS network of China

In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.

Artificial neural network modeling of water quality of the Yangtze River system:a case study in reaches crossing the city of Chongqing

An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.