Study on optimal operation of natural gas pipeline network based on improved genetic algorithm

Zhang, Zhenwu; Liu, Xiantao

doi:10.1177/1687814017715981

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…The GA utilizes natural selection and recombination under defined fitness criteria. The GA was chosen based on the findings of the optimization research area, which ensures that it has excellent performance, is light on computation and is easy to use [53,54]. Several kinds of research in the field of optimization using GA include electricity consumption and anomaly detection in the study by Qu et al [55] their work combines GA with the AdaBoost ensemble model.…”

Section: Classification and Genetics-based Stacking Ensemble (Ga-stac...mentioning

confidence: 99%

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Ismail¹,

Alsalamah²,

Mohamed³

2023

Computers, Materials &Amp; Continua

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As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent predictive method using stacked-based ensemble learning (GA-Stacking) optimized by a Genetic Algorithm (GA) for outbreak prediction and health decision aided processes. GA-Stacking utilizes five well-known classifiers, including Decision Tree (DT), Random Forest (RF), RIGID regression, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost), at its first level. It also introduces GA to identify comparisons to forecast the number, combination, and trust of these base classifiers based on the Mean Squared Error (MSE) as a fitness function. At the second level of the stacked ensemble model, a Linear Regression (LR) classifier is used to produce the final prediction. The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering, Johns Hopkins University, which consisted of 10,722 data samples. The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99% for the three selected countries. Furthermore, the proposed model achieved good performance when compared with existing baggingbased approaches. The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC, 2023, vol.74, no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.

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Section: Classification and Genetics-based Stacking Ensemble (Ga-stac...mentioning

confidence: 99%

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Ismail¹,

Alsalamah²,

Mohamed³

2023

Computers, Materials &Amp; Continua

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“…The model has a wide variety of variables and a comprehensive consideration of constraints, but the compressor type is single. Zhang and Liu studied an optimized operation model of natural gas pipeline based on an improved genetic algorithm. Although the model contains branched and annular pipes, it is mainly aimed at urban pipe networks.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Optimization Model of Large Parallel Natural Gas Pipeline Network: Using Compressors with Multiple Operating Modes

Liu

Peng

Ji³

et al. 2022

Energy Fuels

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In long-distance parallel pipelines, compressor stations are built together, and connecting pipes and cut-off valves are arranged between pipelines, which makes it possible for different stations to operate jointly or independently. In order to reduce pipeline energy consumption, this paper considers the valve switch state, deeply analyzes the station operation mode, and establishes the optimal model of minimum energy consumption of a parallel pipeline. Taking the second and third parallel pipelines of China’s west-east gas transmission as an example, the phasor particle swarm optimization algorithm is used to solve the model. The results show that after optimization, the internal flow was redistributed and energy consumption was reduced by 15.54%.

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“…The control variables employed are mass flow shared among the compressors and compressor performance curve constraints: head verse flow; efficiency verses flow; temperature verse pressure drop of aerial coolers. Similarly, Zhang and Liu (2017), noted the limitations of GA, although GA solves several solutions at a time, it is still limited by lacking an exact way to adjust the fitness values, second, early maturity leads to a locally optimal solution rather than the global solution, and third, slow convergence as it approaches the solution. Due to these limitations, they proposed, a new formula for fitness function, crossover, and mutation probability, which resulted in less calculation time and higher accuracy, they compared the modified algorithm with an unmodified GA and observed significant improvement in optimized flow.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a gas network fuel consumption with genetic algorithm

Takerhi

Dąbrowski

2022

Energy Exploration & Exploitation

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In this paper genetic algorithm (GA) was used for the optimization of two natural gas network, the study focuses on fuel consumption minimization of the second gas network. Studies using GA to simultaneously optimize a gas network and a compressor station based on the constraint of the gas network and compressor station are limited. The relationship between several compressor speed and the total fuel consumption and the relationship between the sum of pressure drop in compressor station loops and compressor inlet flow are assessed for optimization. For this purpose, the optimization of two networks was presented and the results were compared with result from analytical methods. The first network problem was to determine the flow rates and node pressures under a given load, which serves as a guide in using GA. Comparison with result from analytical solution showed good agreement of predicted values. The second network consisted of two compressor stations, each containing six compressors with the objective to optimize the fuel consumption of the system. The simulation was divided into two main simulations, the first simulation was the flow optimization, which results in the optimum flow, based on the fitness function, which is the summation of pressure loss for each compressor station. In the second simulation which was the speed optimization, the optimum set of compressor speed was obtained which depends on the fitness function, which is the minimum fuel consumption, the flow obtained from the flow optimization served as input and remained constant. All speeds were simulated together. The predicted results were compared with literature and showed GA can be used for compressor station fuel optimization but still requires improvement. Based on the outcomes, data on natural gas network operations should be accessible to encourage studies on fuel consumption and CO2 emissions minimization.

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Study on optimal operation of natural gas pipeline network based on improved genetic algorithm

Cited by 13 publications

References 14 publications

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

Energy Consumption Optimization Model of Large Parallel Natural Gas Pipeline Network: Using Compressors with Multiple Operating Modes

Optimization of a gas network fuel consumption with genetic algorithm

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