Jiabao Guan

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.

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.

A Multi-objective Optimization Algorithm for Sensor Placement in Water Distribution Systems

In this study a multi-objective optimization model is developed for water sensor network design in water distribution systems. In this model the three criteria used for evaluating the performance of the water sensor placement designed are directly used as the objectives of the optimization problem. These include minimizing the expected water volume contaminated, minimizing the expected time of detection and maximizing the detection likelihood. Due to the difficulty of determining sensor placement locations within thousands of junction combinations in the system, the sub-domain concept is introduced, which identifies a subset of junctions for candidate sensor locations. The sub-domains are determined using the roulette wheel method based on junction water demand values. The junctions with larger water demand have higher probabilities to be selected to the candidate sensor subset. For solution of the model an improved approach that is based on the non-dominated sorting genetic algorithm (NSGA-II) is used. The approach works over the sub-domain and the final Pareto optimal front is obtained through the sub-domain iteration process. The two water distribution systems provided in BWSN 2006 are chosen as examples to demonstrate the performance of the model and algorithm proposed. The impact of the non-detected scenarios in calculating objectives on the Pareto optimal front is also addressed in this study. The results show that the proposed model and the algorithm are effective in solving this problem.

Real Time Optimal Monitoring Network Design in River Networks

The components of a river monitoring network design study would include the selection of the water quality variables, identification of the location of sampling stations and determination of the sampling frequencies. These are primary design considerations which may require a variety of objectives, constraints and solution methods. In this study we focus on the optimal river water quality monitoring network design, which determines the optimal locations of data collection points, based on continuous measurements at these locations for a generic single or multiple contaminant sources. In the proposed model, the locations of sampling sites are determined such that “the contaminant detection time is minimized” for the overall river network while achieving “maximum reliability” for the system performance. In the proposed methodology, a water quality simulation model is used to generate the time series information for the concentrations of the water quality variables at the potential monitoring locations along the river network. Since contamination events may occur at any time within the simulation period in multiple occurrences, dynamic hydrodynamic and fate and contaminant transport analysis of the overall system would be necessary to arrive at the optimal design. For comparison purposes, the proposed model is tested on a simple network that has been studied in the literature. The comparative analysis of the design generated in this study and the outcome presented in the literature is discussed for various contamination scenarios. The results indicate that steady state solutions or solutions that are based on the geometry of the river network may not provide a reliable solution for the network design problem considered here and a dynamic analysis may be necessary to solve this important problem. The results show that the proposed model can be effectively used for optimal design of monitoring networks.

Scientific Discourse: Climate Change and Sea-Level Rise

AbstractSea-level rise (SLR) is one of the most damaging impacts of climate change. Rising sea levels lead to loss of coastal wetlands, coastal flooding, degradation of coastal ecosystems, and a general loss of quality of life. Due to its potential impacts on coastal management and on population health and safety, the impact of climate change on SLR has drawn significant attention in recent literature. SLR is associated with processes including glacial activity, ice-sheet melting, thermal expansion of sea water, hydrologic events such as increased or decreased land-based discharges, and local effects such as El Nino and La Nina, all of which are complexly linked to changes in global temperature. Unfortunately, many of these physical processes are not well understood in their relation to climate change, and the scientific knowledge required to represent them fully in predictive analysis is so complex that many current studies are shifting away from physical climate models to the application of empirical, s...