The Experts below are selected from a list of 663 Experts worldwide ranked by ideXlab platform

Zachary Mark Aman - One of the best experts on this subject based on the ideXlab platform.

  • rapid assessments of hydrate Blockage risk in oil continuous flowlines
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Bruce W E Norris, Luis E Zerpa, Michael L Johns, Zachary Mark Aman
    Abstract:

    Abstract As industry moves toward the production of oil and gas resources in deep offshore environments, the prospective formation of natural gas hydrates under low temperature, high-pressure conditions poses an increasing risk of Pipeline Blockage. Successful management of this risk requires a precise forecast of the hydrate growth rate. Such predictions should incorporate quantitative descriptions of both deterministic and probabilistic hydrate phenomena. In this work, we present a first step towards such a quantitative risk assessment for systems that form hydrate slurries, based on a consideration of the stochastic nature of hydrate formation. Our framework introduces a new approach to risk assessment, by coupling a laboratory-derived probabilistic nucleation model with existing deterministic calculations for hydrate slurry viscosification. This new approach is used to extend a previously-described hydrate risk algorithm, the Hydrate Flow Assurance Simulation Tool (HyFAST), which enables the rapid assessment of hydrate slurry viscosification using the most advanced models available. Importantly, while the previous version of HyFAST was constrained to calculations in flowloop and autoclave geometries, for which it is sufficient to consider a single volume element, the new version of the HyFAST algorithm allows calculations for flowlines wherein a series of multiple volume elements are considered. The advanced hydrate formation models within this algorithm allow identification of critically important hydrate formation scenarios, and may serve as a screening tool for cases that then require study within rigorous hydrodynamic packages such as OLGA® or LedaFlow® to examine behaviour during key transient events. By coupling the outputs of our predictive algorithm and quantitative risk framework, we demonstrate a new capability to assess what constitutes an acceptable level of hydrate formation. We use this algorithm in a series of case studies that compare the effectiveness of common hydrate mitigation strategies over the life of a reservoir, including the optimization of a wellhead choke opening for both production rate and hydrate Blockage risk.

Yang Xue-cu - One of the best experts on this subject based on the ideXlab platform.

  • Pressure Prediction of Slime Transportation Pipeline Based on Parameter Optimized Support Vector Machine
    Coal Engineering, 2013
    Co-Authors: Yang Xue-cu
    Abstract:

    According to the Blockage problems of the slime transportation Pipeline applied in the coal rejects- fired thermal and electric power plant,with the actual site analysis,the paper defined that the pressure prediction of the master cylinder in the thick material pump of the slime transportation system would be the necessary premise to predict the Pipeline Blockage. The paper provided the pressure prediction model of the master cylinder in the thick material pump of the support vector machine based on the grid method and the grid search method was applied to the parameter optimization of the support vector machine. The simulation results showed that the parameter search optimized time of the support vector machine prediction model was 6. 55s. The prediction model was stable and the relative error was within 3% and could meet the actual engineering requirements. In comparison with the prediction model of the support vector machine based on the genetic algorithm,the GSVM prediction model would be better than the GA- SVM prediction model in the optimization time search and the stability search and could be applied to the real time pressure prediction system of the slime transportation system.

Wei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Investigation of Hydrate Agglomeration and Plugging Mechanism in Low-Wax-Content Water-in-Oil Emulsion Systems
    Energy & Fuels, 2018
    Co-Authors: Yang Liu, Bohui Shi, Lin Ding, Yu Yong, Ye Zhang, Shangfei Song, Juheng Yang, Wei Wang
    Abstract:

    Pipeline Blockage caused by hydrates and wax in subsea Pipelines is a major hazard for flow assurance in the petroleum industry. When hydrates and wax coexist in a flow system, the plugging risk is more severe. The effects of wax on hydrate formation, agglomeration process, flow properties, and plugging mechanisms were studied in a high-pressure flow loop using water-in-oil (w/o) emulsion systems. The flow properties of the system with the presence of wax were entirely different from those of the system without wax under the same experimental conditions. Three types of plugging were observed in the flow loop: rapid plugging, transition plugging, and gradual plugging. The interaction relationships between wax crystals, water droplets, and hydrate particles and the formation of wax–hydrate aggregates were proposed based on the particle video measurement (PVM) probe observation and the analysis of the fluid viscosity. The mechanisms of different plugging scenarios were presented, which were highly correlated...

  • Investigation of Hydrate Agglomeration and Plugging Mechanism in Low-Wax-Content Water-in-Oil Emulsion Systems
    2018
    Co-Authors: Yang Liu, Bohui Shi, Lin Ding, Yu Yong, Ye Zhang, Shangfei Song, Juheng Yang, Wei Wang
    Abstract:

    Pipeline Blockage caused by hydrates and wax in subsea Pipelines is a major hazard for flow assurance in the petroleum industry. When hydrates and wax coexist in a flow system, the plugging risk is more severe. The effects of wax on hydrate formation, agglomeration process, flow properties, and plugging mechanisms were studied in a high-pressure flow loop using water-in-oil (w/o) emulsion systems. The flow properties of the system with the presence of wax were entirely different from those of the system without wax under the same experimental conditions. Three types of plugging were observed in the flow loop: rapid plugging, transition plugging, and gradual plugging. The interaction relationships between wax crystals, water droplets, and hydrate particles and the formation of wax–hydrate aggregates were proposed based on the particle video measurement (PVM) probe observation and the analysis of the fluid viscosity. The mechanisms of different plugging scenarios were presented, which were highly correlated with the temperature and initial flow rate. The presence of wax would impact on the agglomeration process of hydrate particles leading to a catastrophic decrease in the transportation ability and an extremely high plugging risk after hydrate formation in the Pipeline

Luochun Wang - One of the best experts on this subject based on the ideXlab platform.

  • a cost effective method for the treatment of reject water from sludge dewatering process using supernatant from sludge lime stabilization
    Separation and Purification Technology, 2015
    Co-Authors: Zhen Zhou, Luman Jiang, Dalong Hu, Luochun Wang
    Abstract:

    Abstract This paper describes a cost-effective method for phosphorus removal from reject water of sludge dewatering process by using supernatant from sludge lime stabilization (SLS) processes, which are extensively used in China. Supernatant from SLS processes contains high concentration of calcium and high alkalinity hindering COD and ammonium nitrogen removal but favoring phosphate precipitation. Effects of pH and dosing ratio between supernatant and reject water on simultaneous removal of phosphorus and organic substances were evaluated. Both pH and Ca/P ratio increased with increasing dosing ratio of supernatant and reject water. The phosphorus removal achieved 90% when dosing ratio maintained above 15%. COD and humic substances were also effectively removed from reject water by adding SLS supernatant. X-ray diffraction analysis and morphology of harvested precipitates revealed that when pH increased from 7.49 to 9.77, the crystallinity increased, and hydroxyapatite converted to tri-calcium phosphate simultaneously. This method not only saves costs for chemical precipitants, but also favors nutrient removal by increasing alkalinity for nitrification and adding carbon source for denitrification, meanwhile reduces risks of Pipeline Blockage in wastewater treatment plants.

Xue-cun Yang - One of the best experts on this subject based on the ideXlab platform.

  • The Pipeline Positive Pressure Wave Signal Mutation Point Detection Based on EMD and Wavelet Transform
    2018 International Conference on Robots & Intelligent System (ICRIS), 2018
    Co-Authors: Xue-cun Yang, Linghong Kong
    Abstract:

    In order to detect mutation point of the positive pressure wave signal which is caused by fluid Pipeline Blockage, for the advantages and disadvantages of wavelet transform modulus maxima method and EMD(Empirical Mode Decomposition) method, the combination method of the two is put forward. Firstly, positive pressure wave signal is preprocessed by wavelet packet decomposition, and then is decomposed by EMD, and in the end, wavelet transform modulus maxima is done. The experimental data are used to compare with other methods to verify the validity and accuracy of the proposed method.

  • Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine
    Mathematical Problems in Engineering, 2015
    Co-Authors: Xue-cun Yang, Xiao-ru Yan, Chun-feng Song
    Abstract:

    For coal slurry Pipeline Blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of Pipeline Blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and Blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

  • The Coal Slurry Pipeline Pressure Prediction Research Based on Quantum Genetic BP Neural Network
    Applied Mechanics and Materials, 2013
    Co-Authors: Xue-cun Yang, Yuan Bin Hou, Linghong Kong
    Abstract:

    According to the coal slime Pipeline Blockage problem of coal gangue thermal power plant, after the analysis of the actual scene, it is sure that thick slurry pump master cylinder pressure prediction is the necessary premise of Blockage prediction. The thick slurry pump master cylinder pressure prediction model is proposed, which is based on QGA-BP (Quantum genetic Algorithm BP neural network). The simulation results show that the prediction model based on QGA-BP can be used to predict the paste pump outlet pressure, and the relative error is less than 8%, which can satisfy the engineering requirement .And compared with prediction model based on GA-BP(the genetic Algorithm BP neural network), The QGA-BP prediction model is better than GA-BP model in prediction accuracy and optimization time.