Coupled optimization-simulation water quality model for regional water quality management

The objective pursued in water quality management is to secure water qualities such that marginal cost of the cleaning water equals the accuring marginal benefits. It is the purpose of this paper to show how the methods of system analysis may be used to plan more economical new pollution control facilities or how to upgrade existing systems. The particular problem investigated is the following: Given a region with a number of industries and/or municipalities, where should one build treatment plants and will be necessary levels that total regional cost of waste water treatment is minimum. To approach these problems, a stream water quality simulation is linked with a treatment cost minimization model in order to capitalize on the unique adventage of each in managing river basin water quality.     

Water allocation improvement in river basin using Adaptive Neural Fuzzy Reinforcement Learning approach

An accurate simulation model is a necessary tool for optimizing allocation of scarce water resources in large-scale river basins. Adaptive Neural Fuzzy Inference System (ANFIS) method is used to simulate seven interconnected sub-basins in a regional river system located in Iran. Simulated predictions of the method are compared with historical data measurements. ANFIS is a powerful tool for simulating water resources systems of all sub-basins. In this study, a new methodology, Adaptive Neural Fuzzy Reinforcement Learning (ANFRL) is presented for obtaining optimal values of the decision variables. By combining ANFIS with Fuzzy Reinforcement Learning within the content of historical data over a consecutive monthly management period, ANFRL method was derived. Based upon the results of this research, this methodology can be used to develop fuzzy rule systems that accurately simulate the behavior of complex river basin systems within the context of uncertainty. As previous researches have shown that, when simulation model accurately reproduces observed river basin behavior, the optimization model yields better results. Application of this approach in the present case study shows that the effects of uncertainty, imprecise and random factors are 21, 11 and 15% over water resources system, water demand estimated and hydrological regime, respectively. Finally, the results of this method showed that about 16% improvement in water allocation was attained when compared to the primary water resources management in this case study.     

区域水环境经济系统的多目标递阶规划

本文针对文献[1]提出的以水系水质规划为基础的多子区域多目标水环境经济规划问题, 提出了它的相互作用式逐步折衷递阶优化解法,并以两个子区域和五个污水集中处理厂的系 统为例进行计算机仿真,说明其有效性.     

Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system

The widespread and relentless discharge of untreated wastewater into the Upper Litani Basin (ULB) river system in Lebanon has reached staggering levels rendering its water unfit for most uses especially during the drier times of the year. Despite the call by governmental and non-governmental agencies to develop several wastewater treatment plants and sewage networks in an effort to control this problem, these efforts do not seem to be coordinated or based on comprehensive and integrated assessments of current and projected conditions in the basin.This paper provides an overview of the development and implementation of an integrated decision support system (DSS) designed to help policy makers and other stakeholders have a clearer understanding of the key factors and processes involved in the sewage induced degradation of surface water quality in the ULB, and formulate, assess and evaluate alternative management plans. The DSS is developed based on the WEAP model, which provides a GIS based and visual simulation environment and scenario management and analysis capabilities. The DSS was used to assess two main water quality management plans taking into consideration hydrological, spatial and seasonal variabilities. An incremental cost-effectiveness analysis was conducted to identify best buy plans. The results have confirmed the gravity of this problem and demonstrated the importance of taking immediate action on curbing this onslaught on this valuable and scarce fresh water resource.     

Application of artificial neural networks for water quality prediction

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

     

Feedback mechanisms between water availability and water use in a semi-arid river basin: A spatially explicit multi-agent simulation approach

Understanding the processes responsible for the distribution of water availability over space and time is of great importance to spatial planning in a semi-arid river basin. In this study the usefulness of a multi-agent simulation (MAS) approach for representing these processes is discussed. A MAS model has been developed to represent local water use of farmers that both respond to and modify the spatial and temporal distribution of water resources in a river basin. The MAS approach is tested for the Jaguaribe basin in semi-arid Northeast Brazil. Model validity and required data for representing system dynamics are discussed. For the Jaguaribe basin both positive and negative correlations between water availability and water use have been encountered. It was found that increasing wet season water use in times of drought amplify water stress in the following dry season. It is concluded that with our approach it is possible to validly represent spatial-temporal variability of water availability that is influenced by water use and vice versa.     

An automated multi-step calibration procedure for a river system model

Predicted climate change impact on future water availability in the Murray–Darling Basin (MDB) has highlighted the need for a whole of basin model that incorporates various physical and management characteristics for planning and operational purposes. Modelling platforms such as eWater Source Integrated Modelling System (Source) offer a useful framework in this regard, but at present lack automated calibration techniques to parameterise river system models.This paper presents an automated river system calibration procedure which is robust, repeatable, transparent and systematic. The procedure allows for river network calibration (as opposed to isolated reach by reach calibration), since this has more utility for basin planning and prediction. The calibration procedure routs upstream flow, estimates ungauged inputs via rainfall–runoff (RR) models, and estimates flow based split (distributary) functions and loss functions in complex river systems.This procedure was tested in the Northern Murray–Darling Basin (MDB) and results from the Border Rivers catchment are presented. The results from the Border Rivers case study demonstrate the applicability of the procedure with median calibration and evaluation NSE values of 0.88 and 0.79, respectively. The use of this procedure in the Border Rivers region has highlighted the likelihood of changing stream channel connections at higher flows in the lower reaches of the river network.     

Sufficient optimality conditions in hierarchical models of nonuniform systems

We give a brief survey of the studies developed on the basis of general Krotov’s sufficient optimality conditions in an important research direction related to nonuniform systems. We construct a two-layered model of the network structure. The upper level of this model is an abstract network of operators; the lower level contains continuous dynamical models. For such a network, we pose an optimization problem and find general sufficient optimality conditions as generalizations of sufficient conditions for discrete-continuous dynamical systems. We survey possible applications of this direction and consider an example of optimizing nature preservation activities in a river basin.     

Optimal planning for two-stage stochastic industrial systems

We consider the optimal planning problem for a certain class of industrial systems operating under uncertainty. We construct mathematical models, define optimization problems for the planning, propose efficient algorithms for solving them. We show examples of applied problems that can be formalized in this mathematical model.     

Optimal Management of a Bioreactor for Eutrophicated Water Treatment: A Numerical Approach

This paper presents a numerical algorithm for computing the optimal design variables in the management of a bioreactor for the treatment of eutrophicated water: initial and distributed quantities of phytoplankton added along the process, and total duration of the process. This real-world problem is formulated as a state-control constrained optimal control problem, whose numerical resolution is the main aim of this study. After discretizing the control problem, we present a structured algorithm for solving the discrete state systems, computing the corresponding derivatives, and minimizing the objective function. Finally, the good performance of the algorithm is shown by applying it to a realistic example with two pre-reservoirs.     

Generalized multipath planning model for ride-sharing systems

Ride-sharing systems should combine environmental protection (through a reduction of fossil fuel usage), socialization, and security. Encouraging people to use ride-sharing systems by satisfying their demands for safety, privacy and convenience is challenging. Most previous works on this topic have focused on finding a fixed path between the driver and the riders either based solely on their locations or using social information. The drivers’ and riders’ lack of options to change or compute the path according to their own preferences and requirements is problematic. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired characteristics of ride-sharing systems. In this paper, we formalized the ride-sharing problem as a multi source-destination path planning problem. An objective function that models different objectives in a unified framework was developed. Moreover, we provide a similarity model, which can reflect the personal preferences of the rides and utilize social media to obtain the current interests of the riders and drivers. The model also allows each driver to generate sub-optimal paths according to his own requirements by suitably adjusting the weights. Two case studies have shown that our system has the potential to find the best possible match and computes the multiple optimal paths against different user-defined objective functions.     

A quadratic-cost dual control-based approach for optimal trajectory planning under uncertainty

This paper presents a dual control-based approach for optimal trajectory planning under uncertainty. The method approximately converts a nonlinear stochastic optimal control problem whose objective function is a combination of quadratic stage and/or terminal costs, with additive Gaussian process and measurement noises, into a deterministic optimal control problem by augmenting the uncertainty state defined by the square-root of the estimation error covariance matrix. The open-loop solution to the resulting deterministic optimal control reformulation is obtained using an existing pseudo-spectral method. The effectiveness of the proposed dual control-based approach is verified with two numerical examples of trajectory planning for two-dimensional robot motion with lack of observability for localization, which highlights the impact of the dual effect on the shape of designed paths.

     

Integration of MONERIS and GREAT-ER in the decision support system for the German Elbe river basin

The Elbe-DSS is a computer based system for integrated river basin management of the German part of River Elbe basin. Simulation models are used to assess the efficiency of measures such as reforestation, changes of agricultural practices or the efficiency of wastewater treatment plants for achieving management targets. MONERIS and GREAT-ER are integrated into the Elbe-DSS to assess nutrient and pollutant loads. MONERIS calculates nutrient inputs from diffuse and point sources on a sub-catchment scale of about 1000 km². GREAT-ER is a tool for exposure assessment of point source emissions and considers fate in sewage treatment plants as well as degradation and transport in rivers. Both models make long-term predictions, but their spatial scales of operations differ. GREAT-ER divides the whole river network into small segments that are linked through a routing algorithm. The segments are coupled to MONERIS using accumulated flow length distribution. Linking the two models allows to distribute diffuse nutrient emissions calculated from MONERIS and point source emissions from GREAT-ER to the river network, where further elimination and transport processes are calculated. We exemplify the DSS in a study assessing the effects of different reforestation and erosion control measures on phosphate loads and concentrations in the river network.     

Multiple-criteria optimization of pollution control model

This paper deals with a multiple-criteria optimization problem for the pollution control model which was introduced by Kulikowski. The model consists of n polluters which discharge the waste into the environment through waste treatment plants. The pollution of the environment, observed at m points, is expressed by a system of linear operators. The problem of multiple-criteria optimization for this pollution control model is formulated. The main difficulty of multiple-criteria optimization is that it is no longer clear what one means by an optimal solution. A possible remedy to this situation is to introduce an objective function which is expressed as a function of various criterions. Given the specific objective function, the optimum waste treatment strategy for the pollution control model is first determined with respect to another performance measure which is a linear combination of the various criterions. The optimum strategy for this performance is obtained in terms of the weighting factors in the linear combination functional. A search procedure is then used to determine the optimum values of those weighting factors for the specified objective function.     

A software library for quantifying regional-scale nitrogen transport within river basin systems

A software library for quantifying nitrogen fate and transport at the regional-scale is presented as a tool for managing river basin systems. The tool is based on the United States Geological Survey (USGS) SPAtially Referenced Regression on Watershed attributes (SPARROW) model and implements the prediction portion of SPARROW to quantify overland and in-stream losses of nitrogen within a river basin system. The intention of the software library is to serve as an Application Programmers Interface (API) that can be used to construct analysis and visualization tools that address a variety of management scenarios. By leveraging the API along with other existing open source and freely available software libraries for visualization, numerical methods, and network algorithms, it is possible to produce decision support tools that relate pollution sources to downstream loadings. These tools can be used by water resource managers to understand how hypothetical regional-scale changes to the landscape (e.g. land use change) might impact water quality conditions. The use of the model API is demonstrated with a case study that addresses three potential management questions regarding nitrogen fate and transport in the Upper Neuse River Basin of North Carolina.     

A combined neurodynamic approach to optimize the real-time price-based demand response management problem using mixed zero-one programming

This paper presents a microgrid system model considering three types of load and the user’s satisfaction function. The objective function with mixed zero-one programming is used to maximize every user’s profit and satisfaction in the way of the demand response management under real-time price. An energy function is used to transform the constrained problem into an unconstrained problem, and two neural networks are used to find the local optimal solutions of the objective function with different rates of convergence. A neurodynamic approach is used to combine the neural networks with the particle swarm optimization to find the global optimal solution of the objective function. The simulation results show that the combined approach is effective in solving the optimal problem.

     

HS算法在生物生态组合污水处理中的应用

针对生物生态组合污水处理的实际需求,建立以改进的混沌和声搜索算法优化径向基神经网络的ICHS-RBF智能模型,解决生物段生态段的污水处理负荷分配问题并预测出水水质。以重庆某污水处理厂为例,进行一系列实验,结果表明该模型能够进行污水处理负荷的合理分配,出水水质预测的相对误差最高为6.3%,最低为0.4%,具有较强的学习泛化能力和较高的预测精度。利用LabVIEW虚拟仪器平台开发了基于该模型的污水处理过程模拟系统,为过程控制提供很好的研究平台。     

Toward optimal waste management policies for a river basin

A mathematical analysis of marginal cost functions, where the total basin-wide treatment cost for a series of users of water in a river basin is to be minimized subject to constraints, is presented. The situation considered is where a series of water-using firms are located along a river; the river being the sole source of water supply and the only place to discharge wastewater. In such a situation, upstream users, if they discharge waste, impose damage costs on those downstream. On the other hand, if the upstream user is restricted in discharging wastes into the river, the costs for withholding or treating of these wastes are imposed on him. This trade-off is the basis for the minimization of the total basin-wide treatment cost.The results of this study show that under most conditions an optimal solution can be found which balances the upstream marginal waste withholding cost with the downstream damage costs. The analysis is concluded with a presentation of conditions which make these marginal cost relationships sufficient as well as necessary for optimally. Although stream-flow, inflow and outflow are treated as deterministic, this model should be valid except during extreme, transient conditions.     

Economic sizing of warehouses: A linear programming approach

Determining the best size for a private warehouse is a planning problem which can affect the overall operations of the firm for many years into the future. A linear programming formulation is presented which determines the optimal size warehouse to construct when demand is highly seasonal and public warehouse space is available on a monthly basis. The model is then extended for the dynamic sizing problem in which the warehouse size is allowed to change over time.