Improving the performance of natural gas pipeline networks fuel consumption minimization problems

Abstract

in Wiley InterScience (www.interscience.wiley.com).As the gas industry has developed, gas pipeline networks have evolved over decades into very complex systems. A typical network today might consist of thousands of pipes, dozens of stations, and many other devices, such as valves and regulators. Inside each station, there can be several groups of compressor units of various vintages that were installed as the capacity of the system expanded. The compressor stations typically consume about 3-5% of the transported gas. It is estimated that the global optimization of operations can save considerably the fuel consumed by the stations. Hence, the problem of minimizing fuel cost is of great importance. Consequently, the objective is to operate a given compressor station or a set of compressor stations so that the total fuel consumption is reduced while maintaining the desired throughput in the line. Two case studies illustrate the proposed methodology. Case 1 was chosen for its simple and small-size design, developed for the sake of illustration. The implementation of the methodology is thoroughly presented and typical results are analyzed. Case 2 was submitted by the French Company Gaz de France. It is a more complex network containing several loops, supply nodes, and delivery points, referred as a multisupply multidelivery transmission network. The key points of implementation of an optimization framework are presented. The treatment of both case studies provides some guidelines for optimization of the operating performances of pipeline networks, according to the complexity of the involved problems. V V C 2009 American Institute of Chemical Engineers AIChE J, 56: 946-964, 2010

Simulation and Optimization Models of Steady-state Gas Transmission Networks

“…In [9] mixed integer models for the stationary case of gas network are proposed but without operating diagram constraints, feature included in our work, and for networks without cycles, which is not the case of Spanish gas network. Other related works are presented in [10] and [11].…”

Multi-Objective Genetic Algorithms for Chemical Engineering Applications

“…This explains why they have been adopted in this study, where two algorithms for dealing with continuous and mixedinteger problems are presented. Then, the numerical procedures are illustrated by chemical engineering problems often used in the literature as benchmarks: three biobjective ones (ammonia synthesis reactor, Babu and Angira [4], alkylation plant, Jones [25] and natural gas transportation network, Tabkhi et al [51]), a structural mixed-integer problem (Papalexandri and Dimkou [37]) and three multi-objective problems (Williams-Otto process, Chakraborti et al [9], new product development in the pharmaceutical industry, Blau et al [6] and economic and environmental study of the HDA process, Douglas [17]). After the complete set of solutions is displayed in the form of a Pareto front, the best solution is identified by means of multiple choice decision making (MCDM) procedures.…”

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

“…In an outstanding work done by Wu et al [6], a set of polynomial correlations including the surge and stonewall curves were developed to describe the feasible domain of a centrifugal compressor, thus overcoming the defects of the previous idealized compressor model. These constraints given by (11) have already been widely adopted in gas pipeline optimization models [27,30,38] in the past fifteen years. In reality, these constraints are similar to the compressor models embedded in some wellknown gas pipeline simulation tools, such as the Pipeline Studio and Synergi Pipeline Simulator [39].…”

“…Thus, minimizing the fuel cost would greatly contribute to the economic benefit. The minimum fuel cost problem (MFCP) represents the most popular topic in the field of the optimal operation of natural gas pipelines since 1968 [5,30]. The MFCP objective function can be extracted from (4) and expressed by the following [6,12,31]:…”

“…Equality constraints mainly represent the governing equations of gas flowing in pipelines, which include the mass balance, pressure, and temperature equations. The equality constraints are given by following equations [15,30]:…”