A novel optimization approach for oil and gas production process considering model parameters uncertainties

Gao, Xianwen; Tan, Lijing; Yuan, Qingyun; Wang, Lina

doi:10.1002/cjce.22560

Cited by 6 publications

(7 citation statements)

References 34 publications

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“…After optimization, the outbound pressures of stations 1, 3, 5, 6, 9, and 13 fell. In stations 4,8,11,12,13,16,17,19,20,22,24,26, and 29, the pressures increased (the pitted pressure was low in station 5 because of a pressure crossing), thereby reducing the energy consumption of the compressors.…”

Section: Optimized Operation Program After Calculations We Obtainedmentioning

confidence: 99%

“…But dynamic programming became the most successful algorithm for solving this kind of problem because of its advantages of ensuring global optimization and easy handling of nonlinear situations. [19][20][21][22][23] After years of effort by experts, many algorithms are now available to create natural gas pipeline optimization models, but research on and energy consumption models of gas pipelines with very large compressors based on dynamic programming is insufficient. In our research, based on the characteristics of long distances and multiple compressor stations, an optimal operating plan was determined that accommodated the actual conditions of a pipeline.…”

Section: Optimization Variables: Optimization Variablesmentioning

confidence: 99%

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Steady-state optimization operation of the west–east gas pipeline

Liu

Qian

et al. 2019

Advances in Mechanical Engineering

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A pipeline operation optimization model with minimum energy consumption as the objective function was established based on the dynamic programming method. The model was applied to a 3840 km gas pipeline whose designed pipeline capacity was 170 3 10 8 t/a. There were 40 stations in the line, including 22 compressor stations and 32 compressors. The solution time was controlled within 60 s to show that the algorithm was fast and effective. The number of startingup compressors in the optimized scheme is two more than that in the actual operation scheme, and the total pressure drop of the pipeline decreased by 3.40 MPa, the average efficiency of the gas turbine units increased by 4.234%, the average efficiency of the electric drive units increased by 4.875%, and the power decreased by 18,720.38 kW, confirming the validity and feasibility of the optimization model.

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Section: Optimized Operation Program After Calculations We Obtainedmentioning

confidence: 99%

Section: Optimization Variables: Optimization Variablesmentioning

confidence: 99%

Steady-state optimization operation of the west–east gas pipeline

Liu

Qian

et al. 2019

Advances in Mechanical Engineering

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“…High resolution allows smooth adjustment of a parameter. All this facilitates the development of an optimal automatic control system [19][20][21][22][23]. In industrial production, the use of a variable-frequency drive instead of an uncontrolled drive results in savings of electrical energy of up to 30-35%.…”

Section: Operationmentioning

confidence: 99%

“…• verification of flow meters with output signals 0-10V, 0(4) -5 (20) mA, 0-20000 Hz, RS 232 (485), "dry contact", "open collector", photoelectronic reading of signals from devices "star"; with a visual reading of parameters;…”

Section: Introductionmentioning

confidence: 99%

Development and Research of a Dynamic Flow Laboratory Bench Model

Muravyova¹,

Popkov²

2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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The article is devoted to the development and research of a flow laboratory bench model based on highprecision measuring instruments "KROHNE". The following instruments were used: electromagnetic flowmeter ALTOFLUX IFM 5080 K, mass meter Korimass 10G + MFM 4085 K/F, electromagnetic flow rate meter DWM 2000. Highly intelligent instruments allow more accurate selection of the necessary amount of raw materials and other initial components, also maintain process parameters, and maintain pressure, level, and temperature at a set level. This makes it possible to increase the yield of final products, to reduce the amount of produced waste, to reduce waste disposal costs and to reduce energy costs thanks to more precise process run. The conducted studies with uncontrolled and controlled drives depending on the power consumption, which depends on the flow, have shown that it is possible to obtain power savings from 34% to 75%.

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“…The multi-objective optimization technique has been widely utilized to address the multi-indicator characteristic problem. Non-dominated sorting genetic algorithm II (NSGA-II), [14] strength Pareto evolutionary algorithm 2 (SPEA2), [15] multiple objective particle swarm optimization (MOPSO), [16] multiple objective state transition algorithm (MOSTA), [17] multiple objective evolutionary algorithm (MOEA), [18] and grid-based evolutionary algorithm (GrEA) [19] are some of the most popular optimization algorithms for multi-objective problems. Among them, MOSTA, proposed by Han, [20] is a relatively new method that can improve the performance of multi-objective optimization by combining the traditional single-objective state transition algorithm (STA) [21][22][23][24][25][26] with the non-dominated quick sort method of the NSGA-II algorithm.…”

Section: Introductionmentioning

confidence: 99%

Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

Wang

et al. 2021

Can J Chem Eng

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As one of the main costs of industrial processes, energy consumption is an essential issue that cannot be ignored in the sustainable development of enterprises. Effective energy efficiency evaluation is important, but also challenging.The conventional method typically uses statistic standard for evaluation. However, infected by the market, the raw materials of an industrial process often fluctuate, which will affect the evaluation standard of energy efficiency. Taking this into account will improve the evaluation performance. To this end, a realtime dynamic energy efficiency evaluation and analysis method based on multi-objective state transition algorithm with reference vector (RV-MOSTA) is proposed to address the energy efficiency evaluation problem. The core of the paper is to realize the dynamic energy efficiency evaluation of multiple indicators considering inlet conditions of industrial processes. Therefore, a systematic set of special indicators are firstly developed for the energy efficiency evaluation. Then, RV-MOSTA is proposed to determine the evaluation standard, which considers the influence of imported inlet conditions and the optimization of multiple evaluation indicators. Furthermore, a degeneration diagnosis method by means of linear discriminant analysis (LDA) can identify the key variables that lead to energy efficiency degeneration. The proposed method is verified through numerical example and an industrial hydrocracking process.

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A novel optimization approach for oil and gas production process considering model parameters uncertainties

Cited by 6 publications

References 34 publications

Steady-state optimization operation of the west–east gas pipeline

Steady-state optimization operation of the west–east gas pipeline

Development and Research of a Dynamic Flow Laboratory Bench Model

Real‐time dynamic energy efficiency evaluation and analysis of industrial processes based on multi‐objective state transition algorithm with reference vector

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