Method of real-time prognosis of flooding phenomenon in packed column
AssigneesNational Tsing Hua University, Chang Chun Plastics Co., Ltd., Chang Chun Petrochemical Co., Ltd.
InventorsYuan Yao, Bo-Fan Hseuh
Life of this patent
Estimating the term…
Abstract
A method of real-time prognosis of a flooding phenomenon in a packed column includes steps as follows. An online data collection step is conducted, wherein a plurality of values of the pressure drop are collected from the packed column under operation. A calculation step is conducted, wherein the values of the pressure drop are used to calculate a plurality of values of a steadiness index. A statistical step is conducted, wherein a value of a monitoring statistic is calculated based on the values of the steadiness index. A control step is conducted, wherein the value of the monitoring statistic is compared to a control limit, and an alarm is triggered when the value of the monitoring statistic is greater than the control limit.
Claims16
A method of real-time prognosis of a flooding phenomenon in a packed column, comprising:
- conducting an online data collection step, wherein a plurality of values of a pressure drop are collected from the packed column under operation;
- conducting a calculation step, wherein the values of the pressure drop are used to calculate a plurality of values of a steadiness index;
- conducting a statistical step, wherein a value of a monitoring statistic is calculated based on the values of the steadiness index;
- and
- conducting a control step, wherein the value of the monitoring statistic is compared to a control limit, and an alarm is triggered when the value of the monitoring statistic is greater than the control limit;
- and
- conducting an adjusting step after the alarm is triggered, wherein an operational condition of the packed column is adjusted to make the value of the monitoring statistic less than the control limit;
- wherein each of the values of the steadiness index is calculated by Formula (1), Formula (2) and Formula (3):
- S
- 2
- =
- ∑
- i
- =
- 1
- n
-
- (
- P
- i
- -
- P
- _
- )
- 2
- n
- -
- 1
- ;
- (
- 1
- )
- δ
- 2
- 2
- =
- ∑
- i
- =
- 1
- n
- -
- 1
-
- (
- P
- i
- +
- 1
- -
- P
- i
- )
- 2
- 2
-
- (
- n
- -
- 1
- )
- ;
- (
- 2
- )
- R
- =
- 2
-
- S
- 2
- δ
- 2
- ;
- (
- 3
- )
- wherein R is the steadiness index, S2 is an estimator of a sample variance, δ2/2 is another estimator of the sample variance, Pi is an ith value of the pressure drop, n is a sample size of the values of the pressure drop, P is a sample mean of the values of the pressure drop with the sample size of n, and i is an integral from 1 to n.
A method of real-time prognosis of the flooding phenomenon in a packed column, comprising:
- conducting an online data collection step, wherein a plurality of values of the pressure drop are collected from the packed column under operation;
- conducting a calculation step, wherein the values of the pressure drop are used to calculate a value of a steadiness index;
- and
- conducting a control step, wherein the value of the steadiness index is compared to a control limit, and an alarm is triggered when the value of the steadiness index is greater than the control limit;
- and
- conducting an adjusting step after the alarm is triggered, wherein an operational condition of the packed column is adjusted to make the value of the steadiness index less than the control limit;
- wherein each of the values of the steadiness index is calculated by Formula (1), Formula (2) and Formula (3):
- S
- 2
- =
- ∑
- i
- =
- 1
- n
-
- (
- P
- i
- -
- P
- _
- )
- 2
- n
- -
- 1
- ;
- (
- 1
- )
- δ
- 2
- 2
- =
- ∑
- i
- =
- 1
- n
- -
- 1
-
- (
- P
- i
- +
- 1
- -
- P
- i
- )
- 2
- 2
-
- (
- n
- -
- 1
- )
- ;
- (
- 2
- )
- R
- =
- 2
-
- S
- 2
- δ
- 2
- ;
- (
- 3
- )
- wherein R is the steadiness index, g is an estimator of a sample variance, δ2/2 is another estimator of the sample variance, Pi is an ith value of the pressure drop, n is a sample size of the values of the pressure drop, P is a sample mean of the values of the pressure drop with the sample size of n, and i is an integral from 1 to n.
Dependencies are inferred from the claim text.