Process of liquefying a gaseous, methane-rich feed to obtain...

Refrigeration – Cryogenic treatment of gas or gas mixture – Liquefaction

Reexamination Certificate

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C062S619000, C062S657000

Reexamination Certificate

active

06272882

ABSTRACT:

The present invention relates to a process of liquefying a gaseous, methane-rich feed to obtain a liquefied product. The liquefied product is commonly called liquefied natural gas. The liquefaction process comprises the steps of:
(a) supplying the gaseous, methane-rich feed at elevated pressure to a first tube side of a main heat exchanger at its warm end, cooling, liquefying and sub-cooling the gaseous, methane-rich feed against evaporating refrigerant to get a liquefied stream, removing the liquefied stream from the main heat exchanger at its cold end and passing the liquefied stream to storage as liquefied product;
(b) removing evaporated refrigerant from the shell side of the main heat exchanger at its warm end;
(c) compressing in at least one refrigerant compressor the evaporated refrigerant to get high-pressure refrigerant;
(d) partly condensing the high-pressure refrigerant and separating the partly-condensed refrigerant into a liquid heavy refrigerant fraction and a gaseous light refrigerant fraction;
(e) sub-cooling the heavy refrigerant fraction in a second tube side of the main heat exchanger to get a sub-cooled heavy refrigerant stream, introducing the heavy refrigerant stream at reduced pressure into the shell side of the main heat exchanger at its mid-point, and allowing the heavy refrigerant stream to evaporate in the shell side; and
(f) cooling, liquefying and sub-cooling at least part of the light refrigerant fraction in a third tube side of the main heat exchanger to get a sub-cooled light refrigerant stream, introducing the light refrigerant stream at reduced pressure into the shell side of the main heat exchanger at its cold end, and allowing the light refrigerant stream to evaporate in the shell side.
Australian patent No. AU-B-75 223/87 discloses a process of controlling a liquefaction process. The known control process has different strategies for three cases, (1) where the production of liquefied product is below a desired rate, it should be increased by adjusting the composition of the refrigerant taking into account the temperature difference at the cold end of the main heat exchanger; (2) where the production is above a desired rate, it should be decreased by decreasing the suction pressure of the refrigerant compressor; and (3) where the production is at its desired rate, the overall facility efficiency should be optimized by maintaining the refrigerant inventory in a predetermined range. In the cases (1) and (2) the refrigerant inventory and composition and the refrigerant compression ratio should be optimized with respect to overall efficiency.
When the production is at its desired rate, optimization starts with verifying the refrigerant inventory. Then the following refrigerant-related variables are subsequently adjusted: the ratio of the mass flows of heavy refrigerant fraction and light refrigerant fraction, the nitrogen content of the refrigerant and the C
3
:C
2
ratio to achieve peak efficiency. Then the compression ratio of the refrigerant compressor(s) is adjusted to achieve peak efficiency. The last optimization step is adjusting the speed of the refrigerant compressor(s).
When other critical parameters, such as temperature difference at the cold or the warm end of the main heat exchanger would fall below or exceed a predetermined value or range an alarm is set, and the automatic control process is aborted.
A drawback of the known control process is that it requires continuously adjusting the composition of the refrigerant in order to optimize the production. Further drawbacks are that the optimization is done sequentially and that the automatic process control cannot handle a situation wherein, for example the temperature difference at the warm end of the heat exchanger is outside a predetermined range.
To overcome these drawbacks the process of liquefying a gaseous, methane-rich feed to obtain a liquefied product according to the present invention is characterized in that the process further comprises controlling the liquefaction process using an advanced process controller based on model predictive control to determine simultaneously control actions for a set of manipulated variables in order to optimize at least one of a set of parameters whilst controlling at least one of a set of controlled variables, wherein the set of manipulated variables includes the mass flow rate of the heavy refrigerant fraction, the mass flow rate of the light refrigerant fraction and the mass flow rate of the methane-rich feed, wherein the set of controlled variables includes the temperature difference at the warm end of the main heat exchanger and the temperature difference at the mid-point of the main heat exchanger, and wherein the set of parameters to be optimized includes the production of liquefied product.
In the specification and in the claim the expression ‘optimizing a variable’ is used to refer to maximizing or minimizing the variable and to maintaining the variable at a predetermined value.
Model predictive control or model based predictive control is a well-known technique, see for example Perry's Chemical Engineers' Handbook, 7th Edition, pages 8-25 to 8-27. A key feature of model predictive control is that future process behaviour is predicted using a model and available measurements of the controlled variables. The controller outputs are calculated so as to optimize a performance index, which is a linear or quadratic function of the predicted errors and calculated future control moves. At each sampling instant, the control calculations are repeated and the predictions updated based on current measurements. A suitable model is one that comprises a set of empirical step-response models expressing the effects of a step-response of a manipulated variable on the controlled variables.
An optimum value for the parameter to be optimized can be obtained from a separate optimization step, or the variable to be optimized can be included in the performance function.
Before model predictive control can be applied, one determines first the effect of step changes of the manipulated variables on the variable to be optimized and on the controlled variables. This results in a set of step-response coefficients. This set of step-response coefficients forms the basis of the model predictive control of the liquefaction process.
During normal operation, the predicted values of the controlled variables are regularly calculated for a number of future control moves. For these future control moves a performance index is calculated. The performance index includes two terms, a first term representing the sum over the future control moves of the predicted error for each control move and a second term representing the sum over the future control moves of the change in the manipulated variables for each control move. For each controlled variable, the predicted error is the difference between the predicted value of the controlled variable and a reference value of the controlled variable. The predicted errors are multiplied with a weighting factor, and the changes in the manipulated variables for a control move are multiplied with a move suppression factor. The performance index discussed here is linear.
Alternatively, the terms may be a sum of squared terms, in which case the performance index is quadratic.
Moreover, constraints can be set on manipulated variables, change in manipulated variables and on controlled variables. This results in a separate set of equations that are solved simultaneously with the minimization of the performance index.
Optimization can be done in two ways; one way is to optimize separately, outside the minimization of the performance index, and the second way is to optimize within the performance index.
When optimization is done separately, the parameters to be optimized are included as controlled variables in the predicted error for each control move and the optimization gives a reference value for the controlled variables.
Alternatively, optimization is done within the calculation of the performance index, and this gives a third term

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