Design of water distribution networks using a pseudo-genetic algorithm and sensitivity of genetic operators

[EN] Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations. Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for each case. The results demonstrate the validity of the method. Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a good solution is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained[This work was supported by the project DPI2009-13674 (OPERAGUA) of the Direccion General de Investigacion y Gestion del Plan Nacional de I + D + I del Ministerio de Ciencia e Innovacion, Spain.Mora Meliá, D.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Fuertes Miquel, VS. (2013). Design of water distribution networks using a pseudo-genetic algorithm and sensitivity of genetic operators. 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Numerical modelling of pipelines with air pockets and air valves

[EN] This work considers the behaviour of air inside pipes when the air is expelled through air valves. Generally, the air shows isothermal behaviour. Nevertheless, when the transient is very fast, it shows adiabatic behaviour. In a real installation, an intermediate evolution between these two extreme conditions occurs. Thus, it is verified that the results vary significantly depending on the hypothesis adopted. To determine the pressure of the air pocket, the most unfavourable hypothesis (isothermal behaviour) is typically adopted. Nevertheless, from the perspective of the water hammer that takes place when the water column arrives at the air valve and abruptly closes, the most unfavourable hypothesis is the opposite (adiabatic behaviour). In this case, the residual velocity with which the water arrives at the air valve is higher, and, consequently, the water hammer generated is greater.Fuertes Miquel, VS.; López Jiménez, PA.; Martínez-Solano, FJ.; López-Patiño, G. (2016). Numerical modelling of pipelines with air pockets and air valves. Canadian Journal of Civil Engineering. 43(12):1052-1061. doi:10.1139/cjce-2016-0209S10521061431

Discussion of "Numerical modeling of mixed flows in sotrm water systems: critical review of literature" by Samba Bouso, Mathurin Daynou, and Musandji Fuamba

[EN] The discussed paper states that the rigid column model is applied for a single air pocket and the nature of the used equations makes it difficult to be employed in case of several air pockets. The discussers do not agree with this assertion.Fuertes Miquel, VS.; Iglesias Rey, PL. (2015). Discussion of "Numerical modeling of mixed flows in sotrm water systems: critical review of literature" by Samba Bouso, Mathurin Daynou, and Musandji Fuamba. Journal of Hydraulic Engineering. 141(2):1-2. doi:10.1061/(ASCE)HY.1943-7900.0000863S12141

A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots

Lack of homogeneity in the product (LHP) affects several sectors like horticulture, reverse logistics, furniture, ceramics and leathers, among others. Productive processes with LHP are characterized by manufacturing units of the same finished good (FG) with certain attributes that differ and are relevant to customers. This aspect leads to the existence of different subtypes of the same FG in each production lot, which provides homogeneous sublots. Due to inherent LHP uncertainty, the size of each homogeneous sublot is not known until produced. LHP becomes a problem when customers order several units of the same FG and require homogeneity among them; i.e., being served with the same subtype. Like inherent LHP uncertainty, discrepancies between planned homogeneous quantities and the real ones is quite usual. This means it is impossible to serve committed orders with the previously defined requirements of quantity, homogeneity and due date, which brings about a shortage situation. In this paper, a fuzzy mixed integer linear programming model is proposed to support shortage planning in environments with LHP (LHP-FSP model). The LHP-FSP model aims to maximize the profits of served orders by reallocating the quantities of subtypes in stock and the uncertainty future ones in the master plan among the already committed orders. One of the main contributions of the paper is to model the fuzzy interdependent coefficients that represent the fraction of each homogeneous sublot. Finally, experiments based on realistic data from a ceramic company have been designed to validate the model and to analyze its behavior in different scenarios.This research has been carried out within the project framework funded by the Spanish Ministry of Economy and Competitiveness (Ref. DPI2011-23597) and the Universitat Politecnica de Valencia (Ref. PAID-06-11/1840) entitled "Methods and models for operations planning and order management in supply chains characterized by uncertainty in production due to the lack of product uniformity" (PLANGES-FHP).Alemany Díaz, MDM.; Grillo Espinoza, H.; Ortiz Bas, Á.; Fuertes Miquel, VS. (2015). A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Applied Mathematical Modelling. 39(15):4463-4481. https://doi.org/10.1016/j.apm.2014.12.057S44634481391

Computational models calibration. Experiences in environmental studies

Mathematical models are a fundamental tool in the learning process of environmental engineering. These models need to be calibrated in order to be used by future engineers as a simulation tool for the represented problems. This paper deals with the concept of computational models calibration applied to higher environmental engineering studies. In this paper, we depict a methodology to calibrate water quality models, as an educational example that represents the environmental problem of dissolved oxygen in a stream. This methodology is based on defining two types of parameters involved in calibration. First, internal parameters appear in the equations from semi-empirical estimations and can be found within some intervals. Genetic algorithms are proposed to estimate them. Second, experimental measurements enter into equations as external parameters. They affect the accuracy of the final solutions. Therefore, an uncertainty analysis has to be performed. Finally, a termination criterion for calibration has been proposed, based on the overlap between the confidence intervals of predicted and measured values. By developing this methodology, we provide awareness to our students of the importance of calibration of mathematical models so that they can apply them in their future simulation of environmental problems. Students identify the possible sources of uncertainty at each stage of the environmental model performance and apply them in this particular problem, Genetic Algorithm Techniques, as a computational tool to improve the accuracy of their model predictionsLópez Jiménez, PA.; Martínez-Solano, FJ.; Fuertes Miquel, VS.; Iglesias Rey, PL. (2011). Computational models calibration. Experiences in environmental studies. Computer Applications in Engineering Education. 19(4):795-805. doi:10.1002/cae20366S79580519

New insights into the genetic etiology of Alzheimer’s disease and related dementias

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele