Mustafa M. Aral

Optimal remediation with well locations and pumping rates selected as continuous decision variables

Abstract The design of a pump-and-treat groundwater remediation system can be solved as an optimization problem. A common approach in this formulation is to minimize the total cost of the pump-and-treat system, while defining the locations and extraction or injection rates of the candidate pumping wells as continuous decision variables. With this choice, the degree of freedom added to the optimization problem yields significant improvements on the solution. In this approach coupled solution of groundwater simulation models and optimization algorithms are required. The repeated use of the groundwater simulation models throughout the optimization cycle tends to be numerically complex and computationally costly when the governing equations are nonlinear. To overcome this drawback, we propose a new computational procedure, identified as progressive genetic algorithm (PGA), to solve the optimal design problem. PGA is a subdomain method, which combines standard genetic algorithm with ground water simulation models in an iterative solution process and provides a powerful tool for the solution of highly nonlinear optimization problems. Numerical examples are included to demonstrate the feasibility and efficiency of the proposed algorithm. Applications indicate that the proposed approach provides a feasible alternative for the solution of nonlinear optimization problems in groundwater management.

Optimal water quality monitoring network design for river systems.

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.

Optimal Design of Sensor Placement in Water Distribution Networks

In this study we provide a methodology for the optimal design of water sensor placement in water distribution networks. The optimization algorithm used is based on a simulation-optimization and a single-objective function approach which incorporates multiple factors used in the design of the system. In this sense the proposed model mimics a multiobjective approach and yields the final design without explicitly specifying a preference among the multiple objectives of the problem. A reliability constraint concept is also introduced into the optimization model such that the minimum number of sensors and their optimal placement can be identified in order to satisfy a prespecified reliability criterion for the network. Progressive genetic algorithm approach is used for the solution of the model. The algorithm works on a subset of the complete set of junctions present in the system and the final solution is obtained through the evolution of subdomain sets. The proposed algorithm is applied to the two test netwo...

Genetic algorithms in search of groundwater pollution sources

Genetic algorithms (GAs) are relatively new combinatorial search methods which have been used in the solution optimization problems, machine learning and general search problems in numerous fields [Goldberg, 1989; Holland, 1975, Davis, 1991]. In GAs the problem analyzed is conceptualized as a living environment and the computational process is formulated as an iterative-evolutionary process with similarities to evolution of biological systems. GAs may also be identified as iterative stochastic search processes based on the methods employed in the computational steps. In this algorithm, first a random initial population is generated and coded. Based on certain characteristics of this population, a new population is generated by means of three primary operations identified as “selection,” “crossover (mating)” and “mutation.” These three operations, in essence, simulate the mechanisms of natural selection and evolution. In these computations each member of the population, at every stage of the evolution, is a solution to the problem being analyzed. The goal in this evolutionary process is for the new population to have a higher “quality” than the previous one. In optimization problems the “quality” of a member of a population may be measured in terms of the value of the objective function. That is, every population will have a different objective function value and there are better populations which yield a maximum (minimum) value for the objective function. The iterative process of generation of new populations continues until the population converges on a suitable maximum or minimum value of the objective function evaluated. Once this is achieved the optimal solution of the problem is considered solved. Computational steps of this process will be briefly presented below.

Optimization Model and Algorithms for Design of Water Sensor Placement in Water Distribution Systems

In this entry for the “Battle of the Water Sensor Networks (BWSN),” the authors develop a closed-loop algorithmic process for the optimal design of water sensor placement in waterdistribution systems. The proposed solution, the simulation-optimization methodology, focuses on the relation between the input and output of the water-distribution system and not on the topological structure of the system. The proposed model is based on a single objective function approach as opposed to a multi-objective case. However, unlike conventional single objective models, the proposed objective function incorporates multiple factors such as time of detection, contaminated water volume, population affected, and reliability of the optimal system—in this sense it mimics a multi-objective approach. An improved genetic algorithm is proposed for the solution of the model. The algorithm works on a subset of the complete set of junctions present in the system (junction subdomain) and the final solution is obtained through the evolution of subdomains. The proposed algorithm is applied to two test networks submitted by the BWSN committee. The results indicate that the proposed model is effective in solving this problem.

Progressive genetic algorithm for solution of optimization problems with nonlinear equality and inequality constraints

Abstract A new approach, identified as progressive genetic algorithm (PGA), is proposed for the solutions of optimization problems with nonlinear equality and inequality constraints. Based on genetic algorithms (GAs) and iteration method, PGA divides the optimization process into two steps; iteration and search steps. In the iteration step, the constraints of the original problem are linearized using truncated Taylor series expansion, yielding an approximate problem with linearized constraints. In the search step, GA is applied to the problem with linearized constraints for the local optimal solution. The final solution is obtained from a progressive iterative process. Application of the proposed method to two simple examples is given to demonstrate the algorithm.

Estimating Exposure to Volatile Organic Compounds from Municipal Water-Supply Systems: Use of a Better Computational Model

The Southington, Connecticut, water-supply system is characterized by a distribution network that contains more than 1 700 pipeline segments of varying diameters and construction materials, more than 186 mi (299 km) of pipe, 9 groundwater extraction wells capable of pumping more than 4 700 gal/min (0.2965 m3/s), and 3 municipal reservoirs. Volatile organic compounds, which contaminated the underlying groundwater reservoir during the 1970s, contaminated the water-supply system and exposed the towns residents to volatile organic chemicals. We applied a computational model to the water-supply system to characterize and quantify the distribution of volatile organic compounds in the pipelines, from which we estimated the demographic distribution of potential exposure to the towns residents. Based on results from modeling analyses, we concluded the following: (a) exposure to volatile organic compound contamination may vary significantly from one census block to another, even when these census blocks are adjacent to each other within a specified radius; (b) maximum spatial spread of contamination in a water-distribution system may not occur under peak demand conditions, and, therefore, maximum spatial distribution of the exposed population also may not correspond to peak demand conditions, and (c) use of the proposed computational model allows for a more refined and rigorous methodology with which to estimate census-block-level contamination for exposure assessment and epidemiologic investigations.

The Use of Water Quality Index Models for the Evaluation of Surface Water Quality: A Case Study for Kirmir Basin, Ankara, Turkey

Water quality is an important factor for health and safety issues associated with public health and also for aquatic life. More and more water quality issues are becoming a significant concern due to the growth of population, urban expansion and development. Thus, assessment of surface water quality has become an important issue. Water Quality Index (WQI) is a single number which can be calculated easily and used for overall description of the quality of water bodies used for different purposes. In this study, the water quality data obtained from 10 sampling stations during one year monitoring period at Kirmir basin was evaluated. Kirmir basin is one of the important drinking water sources of the capital city of Turkey, Ankara. The analyses of 44 water quality parameters were done for all water samples at the General Directorate of State Hydraulic Works (DSI), Department of Chemistry, Water Analysis Laboratory. It was found that DO, BOD, phosphate, color, turbidity, T. coli, E. coli, Enterococci, iron, manganese, arsenic, aluminum, boron, and barium values which exceed the limit values given in the water quality standards are the major pollutants that affect the water quality in this basin. For easy interpretation of the data, five different WQI models were applied for the selected parameters. The suitability of these WQI models is discussed with respect to their applicability in similar studies. For this study it is concluded that the Canadian WQI (CWQI) and Oregon WQI (OWQI) would provide the best results.

Contaminant transport in layered porous media: 1. General solution

Analysis of transport of contaminants in a layered aquifer system still poses significant challenges, although considerable progress has been made in the literature toward the understanding of this problem. In this study, an analytical approach is chosen as the mode of analysis, and a closed form solution is developed for a quasi-three-dimensional advective-diffusion equation defined for a layered aquifer. The layered aquifer is idealized as an aquifer-aquitard system in which the solution domain is considered to be finite and two dimensional in the aquifer region and infinite and one dimensional in the adjacent aquitards. The analytical solution outlined emphasizes the advection of contaminant in the longitudinal direction in the aquifer and also in the transverse vertical direction in the aquiferaquitard system. The analytical solution also includes the two-dimensional diffusion of the contaminant in the aquifer, one-dimensional diffusion in the aquitards, and first-order reaction and retardation terms in both aquifer and aquitards.

Dynamic System Model to Predict Global Sea-Level Rise and Temperature Change

Climate-change-based global sea-level rise is of concern because it contributes to significant loss of coastal wetlands and mangroves and to increasing damage from coastal flooding in many regions of the world. Physical mechanisms that describe the dynamic global climate systems and the effect of this system behavior on sea-level rise are inherently complex. In this study, conducted using systematic analysis of historic data on temperature change and sea-level rise, a linear dynamic system model is proposed to predict global sea-level rise and mean surface temperatures. Unlike the semiempirical approaches proposed in the recent literature, this model incorporates the inherent interaction between temperature and sea-level rise into the model. The resulting model, recognized from the historic data, shows that the rate of sea-level rise is proportional to temperature, and this rise is also a function of the temporal state of the sea level. Similarly, the rate of temperature change is a function of the temporal state of the temperature and is also affected by the sea-level rise. The proposed model is also used to predict the sea-level rise during the 21st century. DOI: 10.1061/(ASCE)HE.1943-5584.0000447.