Disruptions and Malfunction Control in ORC using Spiral Predictive Model

Fareed ud Din; Abdul Rehman Raza; Muhammad Azam

doi:doi:10.11648/j.epes.20130206.14

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Disruptions and Malfunction Control in ORC using Spiral Predictive Model

Fareed ud Din, Abdul Rehman Raza, Muhammad Azam

Published in American Journal of Electrical Power and Energy Systems (Volume 2, Issue 6)

Received: 3 November 2013 Published: 20 November 2013

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Abstract

This paper provides a critical and analytical assay in the process vicinity of an Organic Rankine Cycle (ORC) resulting in a representation of a controlling model named as Spiral Model as the best approach to implement for an efficient Plant Management (PM) and Risk Mitigation Planning (RMP), focusing on the robust and elegant energy production. There have been so many predictive and sensing process models presented for a gist and substantial control of the ORC plant in recent years but the proposed Spiral Predictive Model (SPM), eliminating all the limitation of all previously implemented models, provides the robustness by performing all the roles in increments; e.g. in the changing controllers, complex time-frequency characteristics, fault detectors for turbines against disruptions and the multi-switching techniques needs to be cascaded ahead of time with predictive and detective techniques. The proposed model optimizes the performance of ORC by response tracking and recursive correction which relegates the errors and sudden disturbance in the process flow. Fast response and recursive correction nicely handles Demand Response (DR) and parameters variations at different working modules which ultimately provide the dynamic performance capability. This study will be elaborating efficient model design and implementation to conjure up a well-designed working flow in an ORC plant.

Published in	American Journal of Electrical Power and Energy Systems (Volume 2, Issue 6)
DOI	10.11648/j.epes.20130206.14
Page(s)	144-148
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Spiral Predictive Model (SPM), Organic Rankine Cycle (ORC), Demand Response (DR), Plant Management (PM), Risk Mitigation Planning (RMP)

References

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APA Style

Fareed ud Din, Abdul Rehman Raza, Muhammad Azam. (2013). Disruptions and Malfunction Control in ORC using Spiral Predictive Model. American Journal of Electrical Power and Energy Systems, 2(6), 144-148. https://doi.org/10.11648/j.epes.20130206.14

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ACS Style

Fareed ud Din; Abdul Rehman Raza; Muhammad Azam. Disruptions and Malfunction Control in ORC using Spiral Predictive Model. Am. J. Electr. Power Energy Syst. 2013, 2(6), 144-148. doi: 10.11648/j.epes.20130206.14

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AMA Style

Fareed ud Din, Abdul Rehman Raza, Muhammad Azam. Disruptions and Malfunction Control in ORC using Spiral Predictive Model. Am J Electr Power Energy Syst. 2013;2(6):144-148. doi: 10.11648/j.epes.20130206.14

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@article{10.11648/j.epes.20130206.14,
  author = {Fareed ud Din and Abdul Rehman Raza and Muhammad Azam},
  title = {Disruptions and Malfunction Control in ORC using Spiral Predictive Model},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {2},
  number = {6},
  pages = {144-148},
  doi = {10.11648/j.epes.20130206.14},
  url = {https://doi.org/10.11648/j.epes.20130206.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20130206.14},
  abstract = {This paper provides a critical and analytical assay in the process vicinity of an Organic Rankine Cycle (ORC) resulting in a representation of a controlling model named as Spiral Model as the best approach to implement for an efficient Plant Management (PM) and Risk Mitigation Planning (RMP), focusing on the robust and elegant energy production. There have been so many predictive and sensing process models presented for a gist and substantial control of the ORC plant in recent years but the proposed Spiral Predictive Model (SPM), eliminating all the limitation of all previously implemented models, provides the robustness by performing all the roles in increments; e.g. in the changing controllers, complex time-frequency characteristics, fault detectors for turbines against disruptions and the multi-switching techniques needs to be cascaded ahead of time with predictive and detective techniques. The proposed model optimizes the performance of ORC by response tracking and recursive correction which relegates the errors and sudden disturbance in the process flow. Fast response and recursive correction nicely handles Demand Response (DR) and parameters variations at different working modules which ultimately provide the dynamic performance capability.  This study will be elaborating efficient model design and implementation to conjure up a well-designed working flow in an ORC plant.},
 year = {2013}
}

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TY  - JOUR
T1  - Disruptions and Malfunction Control in ORC using Spiral Predictive Model
AU  - Fareed ud Din
AU  - Abdul Rehman Raza
AU  - Muhammad Azam
Y1  - 2013/11/20
PY  - 2013
N1  - https://doi.org/10.11648/j.epes.20130206.14
DO  - 10.11648/j.epes.20130206.14
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 144
EP  - 148
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20130206.14
AB  - This paper provides a critical and analytical assay in the process vicinity of an Organic Rankine Cycle (ORC) resulting in a representation of a controlling model named as Spiral Model as the best approach to implement for an efficient Plant Management (PM) and Risk Mitigation Planning (RMP), focusing on the robust and elegant energy production. There have been so many predictive and sensing process models presented for a gist and substantial control of the ORC plant in recent years but the proposed Spiral Predictive Model (SPM), eliminating all the limitation of all previously implemented models, provides the robustness by performing all the roles in increments; e.g. in the changing controllers, complex time-frequency characteristics, fault detectors for turbines against disruptions and the multi-switching techniques needs to be cascaded ahead of time with predictive and detective techniques. The proposed model optimizes the performance of ORC by response tracking and recursive correction which relegates the errors and sudden disturbance in the process flow. Fast response and recursive correction nicely handles Demand Response (DR) and parameters variations at different working modules which ultimately provide the dynamic performance capability.  This study will be elaborating efficient model design and implementation to conjure up a well-designed working flow in an ORC plant.
VL  - 2
IS  - 6
ER  -

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Author Information

Fareed ud Din

Faculty of Engineering and Technology, Lahore, Pakistan
Abdul Rehman Raza

Faculty of Engineering and Technology, Lahore, Pakistan
Muhammad Azam

Faculty of Engineering and Technology, Lahore, Pakistan

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Plain Text BibTeX RIS

APA Style

Fareed ud Din, Abdul Rehman Raza, Muhammad Azam. (2013). Disruptions and Malfunction Control in ORC using Spiral Predictive Model. American Journal of Electrical Power and Energy Systems, 2(6), 144-148. https://doi.org/10.11648/j.epes.20130206.14

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ACS Style

Fareed ud Din; Abdul Rehman Raza; Muhammad Azam. Disruptions and Malfunction Control in ORC using Spiral Predictive Model. Am. J. Electr. Power Energy Syst. 2013, 2(6), 144-148. doi: 10.11648/j.epes.20130206.14

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AMA Style

Fareed ud Din, Abdul Rehman Raza, Muhammad Azam. Disruptions and Malfunction Control in ORC using Spiral Predictive Model. Am J Electr Power Energy Syst. 2013;2(6):144-148. doi: 10.11648/j.epes.20130206.14

Copy | Download

@article{10.11648/j.epes.20130206.14,
  author = {Fareed ud Din and Abdul Rehman Raza and Muhammad Azam},
  title = {Disruptions and Malfunction Control in ORC using Spiral Predictive Model},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {2},
  number = {6},
  pages = {144-148},
  doi = {10.11648/j.epes.20130206.14},
  url = {https://doi.org/10.11648/j.epes.20130206.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20130206.14},
  abstract = {This paper provides a critical and analytical assay in the process vicinity of an Organic Rankine Cycle (ORC) resulting in a representation of a controlling model named as Spiral Model as the best approach to implement for an efficient Plant Management (PM) and Risk Mitigation Planning (RMP), focusing on the robust and elegant energy production. There have been so many predictive and sensing process models presented for a gist and substantial control of the ORC plant in recent years but the proposed Spiral Predictive Model (SPM), eliminating all the limitation of all previously implemented models, provides the robustness by performing all the roles in increments; e.g. in the changing controllers, complex time-frequency characteristics, fault detectors for turbines against disruptions and the multi-switching techniques needs to be cascaded ahead of time with predictive and detective techniques. The proposed model optimizes the performance of ORC by response tracking and recursive correction which relegates the errors and sudden disturbance in the process flow. Fast response and recursive correction nicely handles Demand Response (DR) and parameters variations at different working modules which ultimately provide the dynamic performance capability.  This study will be elaborating efficient model design and implementation to conjure up a well-designed working flow in an ORC plant.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Disruptions and Malfunction Control in ORC using Spiral Predictive Model
AU  - Fareed ud Din
AU  - Abdul Rehman Raza
AU  - Muhammad Azam
Y1  - 2013/11/20
PY  - 2013
N1  - https://doi.org/10.11648/j.epes.20130206.14
DO  - 10.11648/j.epes.20130206.14
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 144
EP  - 148
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20130206.14
AB  - This paper provides a critical and analytical assay in the process vicinity of an Organic Rankine Cycle (ORC) resulting in a representation of a controlling model named as Spiral Model as the best approach to implement for an efficient Plant Management (PM) and Risk Mitigation Planning (RMP), focusing on the robust and elegant energy production. There have been so many predictive and sensing process models presented for a gist and substantial control of the ORC plant in recent years but the proposed Spiral Predictive Model (SPM), eliminating all the limitation of all previously implemented models, provides the robustness by performing all the roles in increments; e.g. in the changing controllers, complex time-frequency characteristics, fault detectors for turbines against disruptions and the multi-switching techniques needs to be cascaded ahead of time with predictive and detective techniques. The proposed model optimizes the performance of ORC by response tracking and recursive correction which relegates the errors and sudden disturbance in the process flow. Fast response and recursive correction nicely handles Demand Response (DR) and parameters variations at different working modules which ultimately provide the dynamic performance capability.  This study will be elaborating efficient model design and implementation to conjure up a well-designed working flow in an ORC plant.
VL  - 2
IS  - 6
ER  -

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