Pressure Drop Coefficient Identification of Steam Network Model Based on Genetic Algorithm

2012 ◽  
Vol 271-272 ◽  
pp. 1388-1392
Author(s):  
Yong Ma ◽  
Yan Guang Sun ◽  
Li Ye Yu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Network Model ◽  
Model Parameters ◽  
Model Accuracy ◽  
Coefficient Identification ◽  
Pipe Networks ◽  
Flow Features ◽  
Test Result

This article establish a coupled thermo-hydraulic mathematical model for steam network by adopting a set of equations The model is simplified according to steam flow features in pipe networks. It is concluded that coupled iteration can be employed in steam network. It is well-known fact that, Steam Network mathematical model must be identified first. Here, identification is defined as process in which a number of Steam Network model parameters are adjusted until the model mimics behavior of the real Steam Network as closely as possible. The case study demonstrates that the integrated identification method gives modelers the maximum flexibility to improve the model accuracy and robustness. Test result indicates the advantage of genetic algorithm.

Coefficient Identification of Steam Network Model Based on Genetic Algorithm

2013 ◽  
Vol 658 ◽  
pp. 555-559
Author(s):  
Xiao Feng Zhu ◽  
Yong Zhang ◽  
Zhao Feng Lu ◽  
Yong Ma
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Network Model ◽  
Model Parameters ◽  
The Real ◽  
Coefficient Identification ◽  
Model Based ◽  
Test Result

This article establish a coupled thermo-hydraulic mathematical model for steam network by adopting a set of equations. Here, identification is defined as process in which a number of Steam Network model parameters are adjusted until the model mimics behavior of the real Steam Network as closely as possible. Test result indicates the advantage of genetic algorithm.

scholarly journals Optimization of the Nonlinear Muskingum Model Parameters for the River Routing, Tigris River a Case Study

2021 ◽  
Vol 16 (6) ◽  
pp. 649-656
Author(s):  
Maher Abd Ameer Kadim ◽  
Isam Issa Omran ◽  
Alaa Ali Salman Al-Taai
Keyword(s):  
Genetic Algorithm ◽  
Least Squares Method ◽  
Harmony Search ◽  
Flood Routing ◽  
Model Parameters ◽  
Muskingum Model ◽  
Tigris River ◽  
Nonlinear Muskingum Model ◽  
Genetic Algorithm Method

Flood forecasting and management are one of the most important strategies necessary for water resource and decision planners in combating flood problems. The Muskingum model is one of the most popular and widely used applications for the purpose of predicting flood routing. The particle swarm optimization (PSO) methodology was used to estimate the coefficients of the nonlinear Muskingum model in this study, comparing the results with the methods of genetic algorithm (GA), harmony search (HS), least-squares method (LSM), and Hook-Jeeves (HJ). The average monthly inflow for the Tigris River upstream at the Al-Mosul dam was selected as a case study for estimating the Muskingum model's parameters. The analytical and statistical results showed that the PSO method is the best application and corresponds to the results of the Muskingum model, followed by the genetic algorithm method, according to the following general descending sequence: PSO, GA, LSM, HJ, HS. The PSO method is characterized by its accurate results and does not require many assumptions and conditions for its application, which facilitates its use a lot in the subject of hydrology. Therefore, it is better to recommend further research in the use of this method in the implementation of future studies and applications.

A Game Theory Approach for Multiple Design Tasks Schedule

2010 ◽  
Vol 26-28 ◽  
pp. 163-166
Author(s):  
Guo Hai Zhang ◽  
Guang Hui Zhou ◽  
Xue Qun Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Game Theory ◽  
Solution Algorithm ◽  
Optimal Scheduling ◽  
Theory Approach ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
Numerical Case Study

This paper presents a new kind of scheduling solution for multiple design tasks in networked developing environments. The main contributions of this study can be focused on three points: The first is to distinguish the concepts and contents of the task scheduling in the networked developing environments. The second is to construct a game-theory mathematical model to deal with this new multiple design tasks scheduling problem. In the presented mathematical model, the players, strategies and payoff are given separately. Therefore, obtaining the optimal scheduling results is determined by the Nash equilibrium (NE) point of this game. In order to find the NE point, a genetic algorithm (GA)-based solution algorithm to solve this mathematical model is proposed. Finally, a numerical case study is presented to demonstrate the feasibility of the methods.

scholarly journals A mathematical model for human-to-human transmission of COVID-19: a case study for Turkey's data

2021 ◽  
Vol 18 (6) ◽  
pp. 9787-9805
Author(s):  
Süleyman Cengizci ◽  
◽  
Aslıhan Dursun Cengizci ◽  
Ömür Uğur ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Total Population ◽  
Reproduction Number ◽  
Model Parameters ◽  
The Novel ◽  
Galerkin Finite Element Method ◽  
Galerkin Finite Element ◽  
Proposed Model ◽  
Novel Coronavirus

<abstract><p>In this study, a mathematical model for simulating the human-to-human transmission of the novel coronavirus disease (COVID-19) is presented for Turkey's data. For this purpose, the total population is classified into eight epidemiological compartments, including the super-spreaders. The local stability and sensitivity analysis in terms of the model parameters are discussed, and the basic reproduction number, $ R_{0} $, is derived. The system of nonlinear ordinary differential equations is solved by using the Galerkin finite element method in the FEniCS environment. Furthermore, to guide the interested reader in reproducing the results and/or performing their own simulations, a sample solver is provided. Numerical simulations show that the proposed model is quite convenient for Turkey's data when used with appropriate parameters.</p></abstract>

scholarly journals A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

Modeling Heart Rate Dynamics in Response to Changes in Exercise Intensity

2015 ◽  
Author(s):  
Michael J. Mazzoleni ◽  
Claudio L. Battaglini ◽  
Brian P. Mann
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Heart Rate ◽  
Heart Rate Response ◽  
Synthetic Data ◽  
Model Parameters ◽  
Nonlinear Mathematical Model ◽  
Heart Rate Dynamics ◽  
Parameter Values ◽  
Good Agreement

This paper develops a nonlinear mathematical model to describe the heart rate response of an individual during cycling. The model is able to account for the fluctuations of an individual’s heart rate while they participate in exercise that varies in intensity. A method for estimating the model parameters using a genetic algorithm is presented and implemented, and the results show good agreement between the actual parameter values and the estimated values when tested using synthetic data.

scholarly journals Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190338 ◽  
Author(s):  
Ariel Wang ◽  
Shulin Cao ◽  
Yasser Aboelkassem ◽  
Daniela Valdez-Jasso
Keyword(s):  
Uncertainty Quantification ◽  
Network Model ◽  
Epistemic Uncertainty ◽  
Hill Coefficient ◽  
Model Parameters ◽  
State Variables ◽  
Model Accuracy ◽  
Network Parameter ◽  
Pulmonary Arterial ◽  
Adventitial Fibroblast

Here, we present a novel network model of the pulmonary arterial adventitial fibroblast (PAAF) that represents seven signalling pathways, confirmed to be important in pulmonary arterial fibrosis, as 92 reactions and 64 state variables. Without optimizing parameters, the model correctly predicted 80% of 39 results of input–output and inhibition experiments reported in 20 independent papers not used to formulate the original network. Parameter uncertainty quantification (UQ) showed that this measure of model accuracy is robust to changes in input weights and half-maximal activation levels (EC 50 ), but is more affected by uncertainty in the Hill coefficient ( n ), which governs the biochemical cooperativity or steepness of the sigmoidal activation function of each state variable. Epistemic uncertainty in model structure, due to the reliance of some network components and interactions on experiments using non-PAAF cell types, suggested that this source of uncertainty had a smaller impact on model accuracy than the alternative of reducing the network to only those interactions reported in PAAFs. UQ highlighted model parameters that can be optimized to improve prediction accuracy and network modules where there is the greatest need for new experiments. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

Identification of LPV Models with Two Scheduling Variables Using Transition Test

2014 ◽  
Vol 543-547 ◽  
pp. 1313-1317
Author(s):  
Jiang Yin Huang ◽  
Jing Zhao
Keyword(s):  
Distillation Column ◽  
Linear Models ◽  
Batch Process ◽  
Model Parameters ◽  
Model Structure ◽  
Model Accuracy ◽  
Weighting Functions ◽  
Identification Method ◽  
Research Findings

This paper presents the research findings of identification method for LPV models with two scheduling variables using transition test. The LPV model is parameterized as blended linear models, which is also called as multi-model structure. Linear weighting functions are used as the local model weights and the Gauss-Newton method is used to optimize the nonlinear LPV model parameters. Usefulness of the method is verified by modeling a high purity distillation column, the case study shows that the multi-model LPV models can yield a better model accuracy with respect to simulation outputs. The identification method proposed in this paper can be used in batch process identification.

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