Research on contaminant sources identification of uncertainty water demand using genetic algorithm

Photovoltaic reverse osmosis (PVRO) systems can provide a viable clean water source for many remote communities. To be cost-effective, PVRO systems need to be custom-tailored for the local water demand, solar insolation, and water characteristics. Designing a custom system composed of modular components is not simple due to the large number of design choices and the variations in the sunlight and demand. This paper presents a modular design architecture, which when implemented on a low-cost PC, would enable users to configure systems from inventories of modular components. The method uses a hierarchy of filters or design rules, which can be provided in the form of an expert system, to limit the design space. The architecture then configures a system from the reduced design space using a genetic algorithm to minimize the system lifetime cost subject to system constraints. The genetic algorithm uses a detailed cost model and physics-based PVRO system model which determines the ability of the system to meet demand. Determining the ability to meet demand is challenging due to variations in water demand and solar radiation. Here, the community’s historical water demand, solar radiation history, and PVRO system physics are used in a Markov model to quantify the ability of a system to meet demand or the loss-of-water probability (LOWP). Case studies demonstrate the approach and the cost-reliability trade-off for community-scale PVRO systems. In addition, long-duration simulations are used to demonstrate the Markov model appropriately captures the uncertainty.

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A genetic algorithm-based procedure for 3D source identification at the Borden emplacement site

Journal of Hydroinformatics ◽

10.2166/hydro.2009.002 ◽

2009 ◽

Vol 11 (1) ◽

pp. 51-64 ◽

Cited By ~ 10

Author(s):

Xin Jin ◽

G. (Kumar) Mahinthakumar ◽

Emily M. Zechman ◽

Ranji S. Ranjithan

Keyword(s):

Genetic Algorithm ◽

Inverse Problems ◽

Objective Function ◽

Selection Criterion ◽

Model Errors ◽

True Solution ◽

Optimization Framework ◽

Contaminant Sources ◽

Objective Function Values ◽

Future Work

Finding the location and concentration of groundwater contaminant sources typically requires the solution of an inverse problem. A parallel hybrid optimization framework that uses genetic algorithms (GA) coupled with local search approaches (GA-LS) has been developed previously to solve groundwater inverse problems. In this study, the identification of an emplaced source at the Borden site is carried out as a test problem using this optimization framework by using a Real Genetic Algorithm (RGA) as the GA approach and a Nelder–Mead simplex as the LS approach. The RGA results showed that the minimum objective function did not always correspond to the minimum solution error, indicating a possible non-uniqueness issue. To address this problem, a procedure to identify maximally different starting points for LS is introduced. When measurement or model errors are non-existent or minimal it is shown that one of these starting points leads to the true solution. When these errors are significant, this procedure leads to multiple possible solutions that could be used as a basis for further investigation. Metrics of mean and standard deviation of objective function values was adopted to evaluate the possible solutions. A new selection criterion based on these metrics is suggested to find the best alternative. This suggests that this alternative generation procedure could be used to address the non-uniqueness of similar inverse problems. A potential limitation of this approach is the application to a wide class of problems, as verification has not been performed with a large number of test cases or other inverse problems. This remains a topic for future work.

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Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

Water Science & Technology Water Supply ◽

10.2166/ws.2021.049 ◽

2021 ◽

Author(s):

Majid Gholami Shirkoohi ◽

Mouna Doghri ◽

Sophie Duchesne

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Water Demand ◽

Urban Water ◽

Ann Model ◽

Short Term ◽

Urban Water Demand ◽

Artificial Neural ◽

Mean Square Errors

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.

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