# A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants

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## Abstract

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## 1. Introduction

## 2. Details of the Selected Process Unit

## 3. Archived Data Specification

## 4. Data Size Reduction Methods

#### 4.1. Frequency Domain and Statistical Analysis

#### 4.1.1. Frequency-Domain Method

^{−4}Hz) and continues up to step 9 (0.0078 Hz). In LT2, non-zero magnitudes start from step 13 to 1. It is clear that non-zero magnitude components are observed in some moments, but not for the entire signal existence duration. So, it can be stated that LT2 has instantaneous frequency components in greater frequencies in comparison with TT2.

^{2}term in Equation (3) will be very small and Equation (3) becomes

#### 4.1.2. Adaptive Sampling Rate Method Investigation

#### 4.1.3. Performance Index Based Analysis

#### 4.1.4. Combination of Frequency and Statistical Analysis Methods

#### 4.2. Traffic Model Analysis

#### 4.3. Correlation Analysis

## 5. Effect of Proposed Data Size Reduction Methods on Control System Performance and Plant Safety

- (A)
- It should be ensured that any important change in the selected process variables is not missed, which enables the control/safety system to take an on-time and accurate reaction as a response. Hopefully, by applying a time–frequency analysis method (DWT method), finding drastic changes in the process variables is possible. As a result, by choosing a fast enough sampling rate, based on the presented methodology and according to time–frequency analysis, a suitable scheme can be designed to guarantee not to miss important changes in the selected process variables.
- (B)
- Another issue which may cause the missing of important changes in the process variables is the high volume of calculations between two consecutive sampling times. To prove this concept, the Harris performance index is a reliable benchmarking tool [8]. Two essential factors that affect the Harris performance index are sampling rate and controller dead-time [7]. So, for each method, the effects on these two factors are evaluated to find out whether they have any side effects on the control system performance.

#### 5.1. Sampling Rate Changing Method

#### 5.2. Traffic Enhancement Method

#### 5.3. Correlation Analysis Method

- (C)
- Finally, there are many safety associated systems/equipment generally embedded in chemical plants, such as emergency shut down (ESD), safety and relief valves, which ensure a safe operation even if failures occur in the regular “control and monitoring” system.

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Process flow diagram (PFD) of a typical dehydration unit in a gas refinery plant. (DPT, PT, FT, TT, LT stand for differential pressure, pressure, flow, temperature, and level transmitters, FC and LC stand for flow and level controllers, and FCV and LCV stand for flow and level control valves).

**Figure 5.**Absolute maximum magnitude of time-varied frequency components in wavelet analysis as a percentage of absolute mean of process values in normal operation for the worst case scenario.

**Figure 9.**Absolute maximum magnitude of time-varied frequency components in wavelet analysis as a percentage of absolute mean of process value in an unscheduled shutdown case.

**Figure 10.**Variations in performance index due to sample pick-up interval change in four control blocks.

**Figure 11.**Flowchart for obtaining best sampling rate for control, monitoring and historian purposes algorithm.

**Figure 12.**A schematic of analog signal digitizing by hierarchical sampling rate: from the fastest (for controlling purpose) to the slowest (for historian purpose) sampling rates, a comparison between conventional methods and the proposed method.

**Figure 13.**Comparison of data size generated by different sampling-interval calculation methods in 90 min for controlling, monitoring and historian (for failure analysis) purposes.

**Figure 14.**An elementary link schedule showing a schedule for transmitter with an analog input, PID block, and a valve with an analog output block [16].

Process Value Name | Cut-Off Frequency (Hz) | Absolute Mean Value of Variable | Maximum Value of FFT |
---|---|---|---|

DPT1 | $5.77\times {10}^{-5}$ | 0.1008 | 0.1008 |

DPT2 | $5.9\times {10}^{-5}$ | 0.1438 | 0.1438 |

DPT3 | $5.5\times {10}^{-5}$ | 0.1827 | 0.1828 |

DPT4 | $5.8\times {10}^{-5}$ | 0.1424 | 0.1424 |

FT1 | $5.5\times {10}^{-5}$ | 220.2619 | 220.25 |

LT1 | $5.0\times {10}^{-5}$ | 13.8186 | 13.82 |

LT2 | $5.46\times {10}^{-5}$ | 3.3918 | 3.392 |

LT3 | $5.48\times {10}^{-5}$ | 30.0275 | 30.03 |

PT1 | $5.54\times {10}^{-5}$ | 65.6893 | 65.7 |

TT1 | $5.4\times {10}^{-5}$ | 63.7867 | 63.8 |

TT2 | $5.5\times {10}^{-5}$ | 33.6374 | 33.635 |

TT3 | $5.4\times {10}^{-5}$ | 18.6766 | 18.675 |

TT4 | $6\times {10}^{-5}$ | 21.1327 | 21.135 |

TT5 | $5.4\times {10}^{-5}$ | 10.6754 | 10.675 |

**Table 2.**Low and high cut-off frequencies for FFT analysis of process values deviation from mean value.

Process Value Name | Low Cut-Off Frequency (Hz) | High Cut-Off Frequency (Hz) | Maximum Value of FFT as a Percentage of Absolute Mean Value of Process Variable in Time Domain (%) |
---|---|---|---|

DPT1 | 0.0001 | 0.0002 | 1.19 |

DPT2 | 0.0042 | 0.0048 | 0.97 |

DPT3 | 0.0002 | 0.0088 | 0.93 |

DPT4 | 0.0001 | 0.0089 | 0.70 |

FT1 | 0.0001 | 0.0002 | 1.23 |

LT1 | 0.0001 | 0.0003 | 7.81 |

LT2 | 0.0001 | 0.0002 | 10.62 |

LT3 | 0.0005 | 0.0007 | 0.47 |

PT1 | 0.0001 | 0.0048 | 0.05 |

TT1 | 0.0001 | 0.0002 | 0.16 |

TT2 | 0.0001 | 0.0002 | 0.53 |

TT3 | 0.0001 | 0.0002 | 1.43 |

TT4 | 0.0001 | 0.0002 | 7.59 |

TT5 | 0.0001 | 0.0002 | 1.78 |

**Table 3.**Summarized auto-regressive moving average (ARMA) model parameters for the studied chemical process.

Subsystem | A | B | C | D |
---|---|---|---|---|

TT2 as output TT1 & FT1 as input (1, 1) | [1, −0.9981] | B_{1–78} = 0B _{79} = −0.007830 | [1, 0.7830] | 1 |

TT2 as output TT1 & FT1 as input (1, 2) | [1, −0.9981] | B_{1–54} = 0B _{55} = −5.4282e−4 | [1, 0.7830] | 1 |

LT2 as output TT1, FT1 & TT2 as input (1, 1) | [1, −0.3786] | B_{1–10} = 0B _{11} = −0.0081 | [1, 0.8698] | 1 |

LT2 as output TT1, FT1 & TT2 as input (1, 2) | [1, −0.3786] | B_{1–10} = 0B _{11} = 0.0438 | [1, 0.8698] | 1 |

LT2 as output TT1, FT1 & TT2 as input (1, 3) | [1, −0.3786] | B_{1–10} = 0B _{11} = −0.0480 | [1, 0.8698] | 1 |

LT1 as output TT1, FT1 & TT2 as input (1, 1) | [1, −0.9704] | B_{1–8} = 0B _{9} = −0.0333 | [1, −0.4690] | 1 |

LT1 as output TT1, FT1 & TT2 as input (1, 2) | [1, −0.9704] | B_{1–40} = 0B _{41} = −0.0229 | [1,−0.4690] | 1 |

LT1 as output TT1, FT1 & TT2 as input (1, 3) | [1, −0.9704] | B_{1–4} = 0B _{5} = 0.0181 | [1, −0.4690] | 1 |

DPT1 as output FT1 as input (1, 1) | [1, −0.9291] | B_{1–5} = 0B _{6} = 0.0567 | [1, 0.7887] | 1 |

Period of Sampling Rate (Sec) | |||||
---|---|---|---|---|---|

Existing Methods (Fixed Sampling Rates) | Methods Used in This Paper (Flexible Methods) | ||||

Process parameter | API 554 recommendation | Åström & Wittenmark, recommendation [1] | Wavelet analysis (10% criteria) | Performance index analysis | |

Controller | TT2 | 0.02 | 0.005 | ∞ | 6 |

LT2 | 0.02 | 0.01 | 0.08 | 0.07 | |

LT1 | 0.02 | 0.1 | 250 | 50 | |

DPT1 | 0.02 | 0.005 | 16 | 5 | |

Monitoring | TT2 | 1 | 1 | ∞ | 6 |

LT2 | 1 | 1 | 0.08 | 0.07 | |

LT1 | 1 | 1 | 250 | 50 | |

DPT1 | 1 | 1 | 16 | 5 | |

Historian Purpose | TT2 | <60 | 1 | ∞ | 6 |

LT2 | <60 | 1 | 0.08 | 0.07 | |

LT1 | <60 | 1 | 250 | 50 | |

DPT1 | <60 | 1 | 16 | 5 |

**Table 5.**Data generated by each method for different applications in control and monitoring systems.

Purpose | Controlling | Monitoring | Historian | |
---|---|---|---|---|

Method | ||||

Proposed approach | 43,336 | 43,336 | 43,336 | |

API 554 approach | 4,860,000 | 97,200 | 1260 | |

Åström & Wittenmark recommendation | 14,742,000 | 97,200 | 97,200 |

**Table 6.**Summary of data size reduction for control, monitoring and data storage obtained by different methods.

Decreasing Percent | |||||||
---|---|---|---|---|---|---|---|

Controlling | Monitoring | Historian | Traffic of Industrial Network | ||||

Sampling rate changing method | 99.11% of API 554 method | 99.71% of Åström & Wittenmark method | 55.42% of API 554 method | 55.42% in comparison with Åström & Wittenmark method | No decrease compared with API 554 method | 55.42% decrease compared with Åström & Wittenmark method | Associated with Hodson [16] method in comparison with existing methods in Foundation Fieldbus in Delta V system: 82% |

Linear correlation method | 99.19% of API 554 method, associated with sampling rate method | 55.56% of API 554 method, using only this method. | 59.67% of API 554 method, associated with sampling rate method | 55.56% of API 554 method, using only this method. | No decrease in comparison with API 554 method, associated with sampling rate method | No decrease in comparison with all methods, using only this method. | Associated with Hodson [16] and sampling rate change method in comparison with existing methods in Foundation Fieldbus in Delta V system: 85% |

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**MDPI and ACS Style**

Abbasinejad, R.; Hourfar, F.; Madhuranthakam, C.M.R.; Elkamel, A.
A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants. *Sustainability* **2020**, *12*, 639.
https://doi.org/10.3390/su12020639

**AMA Style**

Abbasinejad R, Hourfar F, Madhuranthakam CMR, Elkamel A.
A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants. *Sustainability*. 2020; 12(2):639.
https://doi.org/10.3390/su12020639

**Chicago/Turabian Style**

Abbasinejad, Reza, Farzad Hourfar, Chandra Mouli R Madhuranthakam, and Ali Elkamel.
2020. "A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants" *Sustainability* 12, no. 2: 639.
https://doi.org/10.3390/su12020639