Simultaneous synthesis of heat-integrated water networks by a nonlinear program:Considering the wastewater regeneration reuse

Heat-integrated water network synthesis(HIWNS)has received considerable attention for the advantages of reducing water and energy consumptions.HIWNS is effective in water and energy sustainability.Mixed integer non-linear programming(MINLP)is usually applied in HIWNS.In this work,a novel nonlinear programming(NLP)was proposed for HIWNS by considering wastewater reuse and wastewater regeneration reuse.Integer variables are changed to non-linear equation by the methods for identifying stream roles and denoting the existence of process matches.The model is tested by examples with single and multiple regeneration unit problems.The testing results showed that the NLP is an alternative method for HIWNS with wastewater reuse and regeneration reuse.

Effects of water tables and nitrogen application on soil bacterial community diversity, network structure, and function in an alpine wetland, China

Nitrogen deposition and water tables are important factors to control soil microbial community structure.However,the specific effects and mechanisms of nitrogen deposition and water tables coupling on bacterial diversity,abundance,and community structure in arid alpine wetlands remain unclear.The nitrogen deposition(0,10,and 20 kg N/(hm^(2)•a))experiments were conducted in the Bayinbulak alpine wetland with different water tables(perennial flooding,seasonal waterlogging,and perennial drying).The 16S rRNA(ribosomal ribonucleic acid)gene sequencing technology was employed to analyze the changes in bacterial community diversity,network structure,and function in the soil.Results indicated that bacterial diversity was the highest under seasonal waterlogging condition.However,nitrogen deposition only affected the bacterial Chao1 and beta diversity indices under seasonal waterlogging condition.The abundance of bacterial communities under different water tables showed significant differences at the phylum and genus levels.The dominant phylum,Proteobacteria,was sensitive to soil moisture and its abundance decreased with decreasing water tables.Although nitrogen deposition led to changes in bacterial abundance,such changes were small compared with the effects of water tables.Nitrogen deposition with 10 kg N/(hm^(2)•a)decreased bacterial edge number,average path length,and robustness.However,perennial flooding and drying conditions could simply resist environmental changes caused by 20 kg N/(hm^(2)•a)nitrogen deposition and their network structure remain unchanged.The sulfur cycle function was dominant under perennial flooding condition,and carbon and nitrogen cycle functions were dominant under seasonal waterlogging and perennial drying conditions.Nitrogen application increased the potential function of part of nitrogen cycle and decreased the potential function of sulfur cycle in bacterial community.In summary,composition of bacterial community in the arid alpine wetland was determined by water tables,and diversity of bacterial community was inhibited by a lower water table.Effect of nitrogen deposition on bacterial community structure and function depended on water tables.

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.

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.

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.

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.

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.

The Design of Water-reusing Network with a Hybrid Structure Through Mathematical Programming

A new design method for a water-reusing network, with a hybrid structure, to reduce the complexity of the network and to minimize freshwater consumption, is proposed. The unique feature of the methodology proposed .in this article is to control the complexity of the water network by regulation of the control number in a water-reusing system. It combines the advantages of a conventional water-reusing network and a water-reusing net work with internal water mains. To illustrate the proposed method, a single contaminant system and a multiple contaminant system serve as examples of the problems.

Synthesis and Scheduling of Optimal Batch Water-recycle Networks

This work develops an optimization-based methodology for the design and scheduling of batch water recycle networks. This task requires the identification of network configuration, fresh-water usage, recycle assignments from sources to sinks, wastewater discharge, and a scheduling scheme. A new source-tank-sink representation is developed to allow for storage and dispatch tanks. The problem is solved in stages by first eliminating scheduling constraints and determining minimum usage of fresh water and wastewater discharge. An iterative procedure is formulated to minimize the total annual cost of the system by trading off capital versus operating costs. The work overcomes limitations in previous literature work including restricted recycle within the same cycle, lumped balances that may not lead to feasible solutions, and unrealistic objective functions. A case study is solved to illustrate the usefulness of the devised procedure.

Comparative study on identification methods of pipe roughness coefficients in water networks

In this paper,we improve object functions and constraint conditions of genetic algorithms (GAs) applied in PRCs identification of water networks.This identification method can increase calculation efficiency,but can not solve an identification problem with infinitely many solutions well.Then we propose PRCs identification based on the minimal norm method,which satisfies observability conditions and has advantages of high computing efficiency and short time consumption.The two identification methods are applied in a water network,and their identification results are compared under the same conditions.From the results,we know that PRCs identification based on the minimal norm method has advantages of higher computing efficiency,shorter time consumption and higher precision.

Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II

Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in-troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated;this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.