Examining the effect of water demand variation on optimization: the case for a genetic algorithm

2021 ◽  
Author(s):  
A. Bouach ◽  
S. Benmamar
Keyword(s):  
Genetic Algorithm ◽  
Water Demand ◽  
Effect Of Water ◽  
Demand Variation

Modular Design of Community-Scale Photovoltaic Reverse Osmosis Systems Under Uncertainty

2014 ◽  
Author(s):  
Amy Bilton ◽  
Steven Dubowsky
Keyword(s):  
Genetic Algorithm ◽  
Solar Radiation ◽  
Markov Model ◽  
Reverse Osmosis ◽  
Water Demand ◽  
Design Space ◽  
Modular Design ◽  
Cost Model ◽  
Low Cost ◽  
Lifetime Cost

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.

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

2019 ◽  
Vol 39 (4) ◽  
pp. 581-595 ◽  
Author(s):  
Faruk Serin ◽  
Süleyman Mete ◽  
Erkan Çelik
Keyword(s):  
Genetic Algorithm ◽  
Cycle Time ◽  
Efficient Algorithm ◽  
Assembly Line ◽  
Operating Cost ◽  
Practical Case ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Content Type ◽  
Demand Variation

Purpose Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

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

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.

scholarly journals MODELING OF WATER DEMAND VARIATION

1986 ◽  
Vol 4 ◽  
pp. 133-140
Author(s):  
Yutaka TAKAHASI ◽  
Tosio KOIKE ◽  
Seigo NASU ◽  
Tosiyuki UMETA
Keyword(s):  
Water Demand ◽  
Demand Variation

scholarly journals Sensitivity analysis of water consumption in an office building

2018 ◽  
Vol 30 ◽  
pp. 01002
Author(s):  
Tomas Suchacek ◽  
Ladislav Tuhovcak ◽  
Jan Rucka
Keyword(s):  
Sensitivity Analysis ◽  
Water Consumption ◽  
Water Demand ◽  
Working Hours ◽  
Working Time ◽  
Office Building ◽  
Time Step ◽  
Step Duration ◽  
Demand Variation

This article deals with sensitivity analysis of real water consumption in an office building. During a long-term real study, reducing of pressure in its water connection was simulated. A sensitivity analysis of uneven water demand was conducted during working time at various provided pressures and at various time step duration. Correlations between maximal coefficients of water demand variation during working time and provided pressure were suggested. The influence of provided pressure in the water connection on mean coefficients of water demand variation was pointed out, altogether for working hours of all days and separately for days with identical working hours.

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