Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort

2006 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
J. Stoffregen ◽  
K. J. Jungowski ◽  
H. Golshan
Keyword(s):  
Genetic Algorithm ◽  
Research Effort ◽  
Gas Pipeline ◽  
Stochastic Methods ◽  
Pipeline System ◽  
Operational Experience ◽  
Pipeline Network ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables

Operation of large gas pipeline networks calls for fulfilling variation in contractual volume obligations, and maintaining a certain range of linepack with minimum fuel consumptions to drive compressor units. This is often achieved with either operational experience or by utilization of optimization tools, which results in reduced hydraulic analysis time as well as improved pipeline operation as a whole. The main objective is to accurately identify the optimum set points for all compressor stations, control and block valves in the network, subject to several system and operational constraints. This implies multi-objective optimization of a highly constrained network with a large number of decision variables. Over the past three years, TransCanada has devoted a research effort in developing/integrating an optimization tool based on stochastic methods. It was found that it offers greater stability and is more suited for multi-objective optimizations of large networks with inherently large number of decision variables, than any gradient-based method. This paper describes the nature of the pipeline system under optimization, and discusses the basis for a Genetic-Algorithm-based tool employed. It summarizes the results of the past three years of research efforts outlining the selection criteria for the optimization parameters, integration with a robust steady-state thermal hydraulic simulator of the pipeline network and the notion that dynamic penalty parameters can affect convergence. The methodology is applied to a large gas pipeline network containing 22 compressor stations resulting in 54 decision variables and an optimization space of 1.85×1078 cases. Comparison of genetic algorithm optimization with traditional and manual optimization is demonstrated. Extensive effort has been devoted to reduce the computation time, which includes techniques to utilize various hybrid surrogate methods such as Kriging, Neural Networks, Response Surface, as well as exploitation of parallel processing.

Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

2004 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
K. J. Jungowski ◽  
G. Poissant ◽  
H. Golshan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Maximum Throughput ◽  
Multi Objective ◽  
Decision Variables ◽  
Pipeline Networks ◽  
Multiple Unit ◽  
Pareto Fronts ◽  
Selection Of

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

2021 ◽  
pp. 147592172110565
Author(s):  
Chungeon Kim ◽  
Hyunseok Oh ◽  
Byung Chang Jung ◽  
Seok Jun Moon
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Sensor Network ◽  
Sensor Placement ◽  
Mixed Integer ◽  
Working Fluid ◽  
Small Scale ◽  
Pipeline System ◽  
Optimal Sensor Placement ◽  
Pipeline Network

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

scholarly journals Study on the multi-objective optimization of reliability and operating cost for natural gas pipeline network

2021 ◽  
Vol 76 ◽  
pp. 42
Author(s):  
Kaituo Jiao ◽  
Peng Wang ◽  
Yi Wang ◽  
Bo Yu ◽  
Bofeng Bai ◽  
...  
Keyword(s):  
Natural Gas ◽  
Optimization Model ◽  
Network Reliability ◽  
Operating Cost ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Pipeline Network ◽  
Multi Objective ◽  
Natural Gas Pipeline ◽  
The Impact

The development of natural gas pipeline network towards larger scale and throughput has urged better reliability of the pipeline network to satisfy transportation requirement. Previously, studies of optimizing natural gas pipeline network have been mainly focused on reducing operating cost, with little concern on the reliability of pipeline network. For a natural gas pipeline network with a variety of components and complicated topology, a multi-objective optimization model of both reliability and operating cost is proposed in this study. Failure of each component and the state of pipeline network under failure conditions are taken into account, and minimum cut set method is employed to calculate the reliability of the pipeline network. The variables to be determined for the optimization objectives are the rotating speed of compressors and the opening of valves. Then the solving procedure of the proposed model is presented based on Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method and NS-saDE algorithm. The validity of the optimization model is ascertained by its application on a complicated pipeline network. The results illustrate that the optimization model can depict the relative relationship between reliability and operating cost for different throughput, by which the operation scheme with both satisfying reliability and operating cost can be obtained. In addition, the customer reliability and the impact of the failure of each pipeline on the whole network can be evaluated quantitatively to identify the consumers and pipelines of maintenance priority. The pipeline network reliability can be improved through proper monitoring and maintenance of these consumers and pipelines.

Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multi-Component Topology

2011 ◽  
Author(s):  
Zebin Zhou ◽  
Karim Hamza ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Algorithm Optimization ◽  
Two Stage ◽  
Component Structure ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Decision Variables ◽  
Loaded Structure ◽  
Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multi-component structures. Objectives include minimization of compliance, weight and as cost of assembly and manufacturing. Decision variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multi-component structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, we propose a new mutation operator based on decomposition templates and localized joints morphing. A cantilever-loaded structure is then used as a metric to study and compare various setups of single and two-stage GA approaches.

Conditioning TRANSPETRO’s Gas Pipeline Network to the Baía de Guanabara LNG Terminal New Regasification Profile

2014 ◽  
Author(s):  
Philipe B. Krause ◽  
Marcos Bruno B. Carnevale ◽  
Denis F. dos Santos ◽  
Rodrigo B. L. Jardim
Keyword(s):  
Natural Gas ◽  
Power Plants ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Pipeline Network ◽  
Short Period ◽  
Future Demands ◽  
Energy Grid ◽  
To Receive ◽  
Different Sources

Petrobras Transporte S.A. – TRANSPETRO’s Gas Pipeline System, composed by 7.3 thousand kilometers, 135 delivery stations and 21 compressor stations, has a very seasonally dependent operation. Highly linked with the Brazilian energy grid, during the dry season of the year a large part of the 77.3 million cubic meters of natural gas daily transportation are used to generate around 6.4 gigawatts to power the country. Additionally, the ever increasing number of power plants and distribution companies around the country demand more and more gas to be offered to supply the system. Among the different sources of natural gas available, the LNG is the most flexible for such seasonal operation. In order to support this current demand and to attend future demands, the regasification ability of Baía de Guanabara LNG Terminal was increased in December 2012, by changing the regasification vessel that supplies the southeast portion of the gas pipeline network, from 14 to 20 million cubic meters per day. To prepare to receive the new ship, some tests were performed to determine the operational limits on system survival time without LNG supply during vessel exchange. This assessment involved two different issues. The ship change operation occurred during a period of high consumption, when the LNG terminal was needed to sustain the network inventory. A long period without this supply, caused by the exchange of LNG vessel, would affect the deliveries. On the other hand, the new ship’s commissioning curve would introduce a large amount of natural gas into the system during a short period of time, demanding that the deliveries absorbed such volume. Four planning scenarios were assessed based on some expected pipeline supply and delivery conditions. The work was important as a reference for future changes on operating supply units of TRANSPETRO gas pipeline system, showing the importance of pipeline simulation both as a planning tool for pipeline logistic problems and as operational support.

Multi-objective transient peak shaving optimization of a gas pipeline system under demand uncertainty

2021 ◽  
Vol 147 ◽  
pp. 107260
Author(s):  
Qian Chen ◽  
Changchun Wu ◽  
Lili Zuo ◽  
Mahdi Mehrtash ◽  
Yixiu Wang ◽  
...  
Keyword(s):  
Demand Uncertainty ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Peak Shaving ◽  
Multi Objective

Optimization of Short-Term Peak Shaving Scenarios for a Gas Pipeline Network

2008 ◽  
Author(s):  
Lili Zuo ◽  
Changchun Wu ◽  
Li Fan ◽  
Meng Wang
Keyword(s):  
Initial Conditions ◽  
Optimal Operation ◽  
Simulation Software ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Short Term ◽  
Peak Shaving ◽  
Flow Rates ◽  
Pipeline Network ◽  
Gas Supply

PetroChina owns and operates the largest gas pipeline network in China of more than 10000 km in length, which includes the famous West-East gas pipeline, the first Shannxi-Beijing gas pipeline and the second Shannxi-Beijing gas pipeline etc. As an outstanding feature of the network, its two circuits of pipelines increases the flexibility of gas transmission and the guarantee of gas supply through the network. On the other hand, these two circuits complicate the topological structure, so that it is a challenge to work out an optimal operation scenario for the network. A steady and transient simulation model of the network has been built based on the gas pipeline network simulation software TGNET, and has been tuned by the historical operation data. By means of the model, several winter operation scenarios in 2007 have been simulated. The steady simulations of the network were carried out for the two planed daily flow-rates of West-East gas pipeline respectively, 41 MMSCMD and 45 MMSCMD. Given the steady operation scenarios determined by the steady simulations as the initial conditions, 4 typical short-term peak shaving scenarios in winter high load week have been analyzed, evaluated and optimized with transient simulations. The main difference of those peak shaving scenarios is the flow-rates of West-East gas pipeline and the regulating mode of underground gas storage named Dagang connected to Shanxi-Beijing gas pipeline system. The technologically and economically optimal peak shaving scenario and the optimal control pressure of end stations have been obtained. The research results shows that the actual control pressure of end stations are higher than the optimization results, indicating that the network has the potential of saving energy and reducing spending. These results not only guarantee the safety of gas supply but also reduce the spending of the gas pipeline network, offering an important value of direction for actual operation.

scholarly journals A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5542
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Liang Dong ◽  
Xiaotong Cui ◽  
Weijun Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Electromagnetic Analysis ◽  
Multi Objective Optimization ◽  
Side Channel Analysis ◽  
Multi Objective ◽  
The Past ◽  
Cryptographic Algorithm ◽  
Channel Analysis ◽  
Electromagnetic Signals

Correlation electromagnetic analysis (CEMA) is a method prevalent in side-channel analysis of cryptographic devices. Its success mostly depends on the quality of electromagnetic signals acquired from the devices. In the past, only one byte of the key was analyzed and other bytes were regarded as noise. Apparently, other bytes’ useful information was wasted, which may increase the difficulty of recovering the key. Multi-objective optimization is a good way to solve the problem of a single byte of the key. In this work, we applied multi-objective optimization to correlation electromagnetic analysis taking all bytes of the key into consideration. Combining the advantages of multi-objective optimization and genetic algorithm, we put forward a novel multi-objective electromagnetic analysis based on a genetic algorithm to take full advantage of information when recovering the key. Experiments with an Advanced Encryption Standard (AES) cryptographic algorithm on a Sakura-G board demonstrate the efficiency of our method in practice. The experimental results show that our method reduces the number of traces required in correlation electromagnetic analysis. It achieved approximately 42.72% improvement for the corresponding case compared with CEMA.

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