The Experts below are selected from a list of 897 Experts worldwide ranked by ideXlab platform

Priyadarshi Mahapatra - One of the best experts on this subject based on the ideXlab platform.

  • dynamic maximization of oxygen yield in an elevated pressure air separation unit using multiple model predictive control
    IFAC Proceedings Volumes, 2013
    Co-Authors: Priyadarshi Mahapatra, Stephen E. Zitney, Wayne B Bequette
    Abstract:

    In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the Flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain Optimal Flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.

  • Application of linear multiple model predictive control (MMPC) framework towards dynamic maximazation of oxygen yield in an elevated-pressure air separation unit
    2012
    Co-Authors: Priyadarshi Mahapatra, Stephen E. Zitney, B. Wayne Bequette
    Abstract:

    In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the Flowrate of liquid nitrogen (LN2) stream connecting high-pressure and low-pressure ASU columns plays an important role in the total oxygen yield. It has been observed that this yield reaches a maximum at a certain Optimal Flowrate of LN2 stream. At nominal full-load operation, the Flowrate of LN2 stream is maintained near this optimum value, whereas at part-load conditions this Flowrate is typically modified in proportion with the load-change (oxygen demand) through a ratio/feed-forward controller. Due to nonlinearity in the entire ASU process, the ratio-modified LN2 Flowrate does not guarantee an Optimal oxygen yield at part-load conditions. This is further exacerbated when process disturbances in form of “cold-box” heat-leaks enter the system. To address this problem of dynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. This approach has been used in previous studies to handle large ramp-rates of oxygen demand posed by the gasifier in an IGCC plant. In this study, the proposed algorithm uses linear step-response “blackbox” models surrounding the operatingmore » points corresponding to maximum oxygen yield points at different loads. It has been shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost compared to a firstprinciple based nonlinear MPC. Introduction« less

Yongsheng Liu - One of the best experts on this subject based on the ideXlab platform.

  • Design and hydraulic modeling of pulse grinding bits for horizontal wells
    KeAi, 2018
    Co-Authors: Zheng Wei, Deli Gao, Yongsheng Liu
    Abstract:

    If cuttings carrying performance is poor and cuttings removal is not in time during the drilling of horizontal wells, drilling cuttings will accumulate in the lower sections, leading to backing pressure, BHA binding and even drill pipe sticking. In this paper, a new type of Helmholtz pulse grinding bits suitable for horizontal wells was designed based on the theory of Helmholtz oscillation chamber to generate pulse, jet pump and high pressure jet after the formation of cuttings beds was analyzed. In this type of bit, a high-speed pulse jet is used to assist rock breaking, a reverse jet is used to remove the cuttings at the bottom of the bit under negative pressure, and its inner grinding structure is used to reduce the particle size of cuttings. By using this bit, efficient cuttings removal and rock breaking will be both realized, the chip hold-down effect will be reduced and the cuttings beds in a horizontal well will be also removed. Then, the hydraulic models were established for a pulse generation device, an efficient rock breaking device and a reverse swabbing device, respectively. It is shown from the simulation results that the Optimal resonance Flowrate increases with the increase of the diameters of an inlet chamber and a feedback chamber and with the decrease of the diameter of a resonance chamber, and it is approximately in linear relationship with each factor. The Optimal Flowrate ratio of the reverse swabbing device increases first and then decreases with the increase of dimensionless Flowrate ratio, and decreases with the increase of dimensionless area ratio. It is indicated from example analysis that the inherent frequency of Helmholtz oscillation chamber is 24.00 Hz, the Optimal oscillation Flowrate is 23.92 L/s and the Optimal Flowrate ratio is 0.59. Based on case studies, the accuracy of hydraulic models is verified. It is concluded that this new type of bits provides a new solution to the accumulation of cuttings beds. Keywords: Horizontal well, Pulse grinding bit, Helmholtz oscillation chamber, Pulse jet, Reverse jet, Optimal resonance flow rate, Optimal Flowrate rati

  • Design and hydraulic modeling of pulse grinding bits for horizontal wells
    Natural Gas Industry B, 2018
    Co-Authors: Wei Zheng, Gao Deli, Yongsheng Liu
    Abstract:

    Abstract If cuttings carrying performance is poor and cuttings removal is not in time during the drilling of horizontal wells, drilling cuttings will accumulate in the lower sections, leading to backing pressure, BHA binding and even drill pipe sticking. In this paper, a new type of Helmholtz pulse grinding bits suitable for horizontal wells was designed based on the theory of Helmholtz oscillation chamber to generate pulse, jet pump and high pressure jet after the formation of cuttings beds was analyzed. In this type of bit, a high-speed pulse jet is used to assist rock breaking, a reverse jet is used to remove the cuttings at the bottom of the bit under negative pressure, and its inner grinding structure is used to reduce the particle size of cuttings. By using this bit, efficient cuttings removal and rock breaking will be both realized, the chip hold-down effect will be reduced and the cuttings beds in a horizontal well will be also removed. Then, the hydraulic models were established for a pulse generation device, an efficient rock breaking device and a reverse swabbing device, respectively. It is shown from the simulation results that the Optimal resonance Flowrate increases with the increase of the diameters of an inlet chamber and a feedback chamber and with the decrease of the diameter of a resonance chamber, and it is approximately in linear relationship with each factor. The Optimal Flowrate ratio of the reverse swabbing device increases first and then decreases with the increase of dimensionless Flowrate ratio, and decreases with the increase of dimensionless area ratio. It is indicated from example analysis that the inherent frequency of Helmholtz oscillation chamber is 24.00 Hz, the Optimal oscillation Flowrate is 23.92 L/s and the Optimal Flowrate ratio is 0.59. Based on case studies, the accuracy of hydraulic models is verified. It is concluded that this new type of bits provides a new solution to the accumulation of cuttings beds.

Wayne B Bequette - One of the best experts on this subject based on the ideXlab platform.

  • dynamic maximization of oxygen yield in an elevated pressure air separation unit using multiple model predictive control
    IFAC Proceedings Volumes, 2013
    Co-Authors: Priyadarshi Mahapatra, Stephen E. Zitney, Wayne B Bequette
    Abstract:

    In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the Flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain Optimal Flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.

D A Alkhasova - One of the best experts on this subject based on the ideXlab platform.

  • Evaluating the Effect from Constructing Binary Geothermal Power Units Based on Spent Petroleum and Gas Boreholes in the South Regions of Russia
    Thermal Engineering, 2018
    Co-Authors: A. B. Alkhasov, D A Alkhasova
    Abstract:

    The article substantiates the possibility of efficiently harnessing the geothermal resources available in the North Caucasian region through constructing binary geothermal power plants (GeoTPPs) using idle petroleum and gas wells. The power capacities of GeoTPPs are evaluated, and the basic characteristics of these power plants in case of constructing them in the promising areas are determined. The overall useful GeoTPP capacity equal to approximately 330 MW can be obtained from using the entire fleet of idle wells available in these areas. Diagrams confirming the possibility of reaching the Optimal Flowrate of geothermal heat carrier circulating in the geothermal circulation system loop are presented. This Flowrate corresponds to a binary GeoTPP’s maximal useful power output. The article shows, taking the Ternair geothermal field as an example, that it is inefficient to use medium-enthalpy thermal waters for generating energy at a binary GeoTPP involving reinjection of a spent heat carrier. It is shown that good prospects can be expected from applying a hybrid geothermal and combined-cycle technology, by means of which it is possible to use lowenthalpy (80–100°С) thermal waters for generating electricity in a highly efficient manner. In accordance with such technology, geothermal heat is used in the binary GeoTPP cycle for heating low-boiling working fluid to its evaporation temperature. The working fluid is evaporated and superheated by using the heat of exhaust gases from a gas turbine power unit. Owing to combined use of the thermal water heat potential and the heat of exhaust gases from a gas turbine power plant in a hybrid process system, it becomes possible to obtain high power performance indicators of hybrid geothermal and combined-cycle power plants. This conclusion is confirmed by the results from numerical evaluations carried out as applied to the Ternair geothermal field. With the fully harnessed resource potential of the Ternair field, the total capacity of hybrid geothermal and combined-cycle power plants may reach 60 MW, a level that would make it possible to relieve a significant part of energy, environmental, economic, and social problems faced by the city of Makhachkala.

Stephen E. Zitney - One of the best experts on this subject based on the ideXlab platform.

  • dynamic maximization of oxygen yield in an elevated pressure air separation unit using multiple model predictive control
    IFAC Proceedings Volumes, 2013
    Co-Authors: Priyadarshi Mahapatra, Stephen E. Zitney, Wayne B Bequette
    Abstract:

    In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the Flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain Optimal Flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.

  • Application of linear multiple model predictive control (MMPC) framework towards dynamic maximazation of oxygen yield in an elevated-pressure air separation unit
    2012
    Co-Authors: Priyadarshi Mahapatra, Stephen E. Zitney, B. Wayne Bequette
    Abstract:

    In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the Flowrate of liquid nitrogen (LN2) stream connecting high-pressure and low-pressure ASU columns plays an important role in the total oxygen yield. It has been observed that this yield reaches a maximum at a certain Optimal Flowrate of LN2 stream. At nominal full-load operation, the Flowrate of LN2 stream is maintained near this optimum value, whereas at part-load conditions this Flowrate is typically modified in proportion with the load-change (oxygen demand) through a ratio/feed-forward controller. Due to nonlinearity in the entire ASU process, the ratio-modified LN2 Flowrate does not guarantee an Optimal oxygen yield at part-load conditions. This is further exacerbated when process disturbances in form of “cold-box” heat-leaks enter the system. To address this problem of dynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. This approach has been used in previous studies to handle large ramp-rates of oxygen demand posed by the gasifier in an IGCC plant. In this study, the proposed algorithm uses linear step-response “blackbox” models surrounding the operatingmore » points corresponding to maximum oxygen yield points at different loads. It has been shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost compared to a firstprinciple based nonlinear MPC. Introduction« less