A method for optimizing the operation of a thermodynamic system of a liquefied natural gas plant

Abstract

The present disclosure relates to a computer-implemented method (100) at a processing unit (11, 203) for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system (1) of a liquefied natural gas, LNG, plant. The present disclosure also relates to server (200) for providing cloud computing services that is remotely located with respect to a thermodynamic system (1) and which comprises a processing unit (203) for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system (1) of the LNG plant.

Description

A method for optimizing the operation of a thermodynamic system of a liquefied natural gas plantDescriptionTECHNICAL FIELD

[0001] The present disclosure concerns a method for optimizing the operation of a thermodynamic system of a liquefied natural gas (LNG) plant.

[0002] The subject matter disclosed herein also refers to a computer-implemented method performed at a processing unit for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system of an LNG plant.

[0003] The subject matter disclosed herein also refers to a server comprising a processing unit to execute the above method.

[0004] The subject matter disclosed herein also refers to a computer program comprising one or more instructions that when executed by processing unit, causes the processing unit to perform the above method.BACKGROUND ART

[0005] An LNG refrigeration process can be considered as a cryogenic refrigeration cycle where the LNG is cooled down at atmospheric pressure to become liquid. The LNG can then be transported by a ship up to the regassification station and delivered to the final user distribution network.

[0006] An LNG refrigeration process is an energy intense process which requires a significant amount of power.

[0007] A process optimization could increase the LNG production for the same amount of power absorbed by the LNG refrigerant process or vice versa reduce the amount of power demanded keeping constant the LNG production flow. This provides a direct improvement in the overall LNG refrigerant process efficiency and reduces the total emission to atmosphere with the consequent reduction of the carbon footprint.

[0008] The process optimization is a well-known challenge in the LNG sector and it could be performed by producing a data-driven or physic-based modelling, purely based on the plant historical and current data or in a more complex description through a thermodynamic equation describing the balance between the mass and energy.

[0009] In particular, the optimization of the refrigeration cycle including refrigerant composition, temperature and pressure profile for maximizing the heat exchanger efficiency is explored. The numerical convergence is challenged by the tight non-linear temperature development along the cooling curve, moreover the complexity of the process, the complication of cryogenic equation of state associated with a lack of domain expertise could lead to not-feasible solutions or high uncertainty associated to the results that might jeopardize the application of the proposed solution.

[0010] Long computational time to achieve a stable solution might be subject to a significant variation on actual boundary conditions making the solution not effective. In case of multi-module configuration then the optimization study shall be carried out keeping into account the specific module peculiarity that increases the effort of parallel simultaneous process optimizations. Analysis are generally stand-alone elaborations without involving the equipment health status generating a theoretical solution that cannot be implemented in an actual plant.

[0011] Currently, optimization of an LNG refrigeration process requires a deep knowledge of the process flow and a deep understanding of the relationship between different process parameters. Optimization in LNG refrigeration process is a multivariable system with several constrains based on the component, equipment and process limits. The health status of each component might also not be optimal. Based on the specific functionality and criticality of the equipment, its incipient failure modes and their degradation evolution tendency the LNG process module might have different operating envelope that shall not be neglected in developing a theoretical solution.

[0012] Therefore, there is a need for a method for optimizing the operation of a thermodynamic system of an LNG plant. In particular, there is a need for a method capable of determining one or more optimization parameters of a refrigeration module of the thermodynamic system to adjust the operation of the refrigeration module.SUMMARY

[0013] Certain aspects commensurate in scope with the originally claimed disclosure are summarized below. These aspects are not intended to limit the scope of the claimed disclosure, but rather these aspects are intended only to provide a brief summary of possible forms of the disclosure. Indeed, the full disclosure may encompass a variety of forms that may be similar to or different from the aspects set forth below.

[0014] In one aspect, the subject matter disclosed herein is directed to a method for optimizing the operation of a thermodynamic system to provide multiple advantages to the customer.

[0015] In particular, the subject matter relates to a computer-implemented method at a processing unit for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system of an LNG plant, wherein the thermodynamic system comprises: one or more refrigeration modules, wherein each refrigeration module comprises a compressor and a heat exchanger, which allows the continuous cooling of a natural gas stream to produce an LNG stream by using a mixed refrigerant fluid passed by the compressor through the heat exchanger; at least one diagnostic module, which allows for continuous monitoring of the one or more refrigeration modules; and the method comprising the steps of: obtaining a target operating condition of a refrigeration module of the one or more refrigeration modules acquiring, from the at least one diagnostic module, operating data of the LNG plant, wherein the operating data comprises current operating parameters of the refrigeration module; determining a current LNG production performance and a health status of the refrigeration module on the basis of the acquired operating data; determining a maximum loading capability of the refrigeration module using a first artificial intelligence algorithm, wherein the first artificial intelligence algorithm is trained on the relationship between the health status of the refrigeration module and a maximum theoretical power achievable by the refrigeration module on the basis of the current operating parameters determining based on the maximum loading capability and the target operating condition, using a second artificial intelligence algorithm, the one or more optimization parameters of the refrigeration module that adjust the operation of the refrigeration module to match the target operating condition, wherein the second artificial intelligence algorithm is trained on the relationship between the maximumloading capability, the target operating condition and the one or more optimization parameters.

[0016] In another aspect, the subject matter disclosed herein is directed to at least one server for providing cloud computing services that is remotely located with respect to a thermodynamic system, the server comprising a storage unit, for storing one or more optimization parameters; a communication unit, for transmitting data to the Internet and receiving data from the Internet; and the processing unit, wherein the processing unit executes program code stored in the storage unit perform the method for optimizing the operation of a thermodynamic system.

[0017] In a further aspect, the subject matter disclosed herein is directed to a system for controlling the operation of a thermodynamic system of an LNG plant, the system comprising: a server for providing cloud computing services; one or more user devices; a cloud gateway interfacing with the server through the Internet and configured to exchange data to and from the server and the one or more user devices; a plant control unit for controlling the thermodynamic system in response to data received from the cloud gateway; wherein the cloud gateway is configured to initiate a secure communication channel to receive a message comprising one or more optimization parameters from the server; and extract the one or more optimization parameters from the message and provide the one or more optimization parameters to the plant control unit.

[0018] In a further aspect, the subject matter disclosed herein is directed to a computer program product comprising one or more instructions that when executed by processing unit, causes the processing unit to optimize the operation of a thermodynamic system.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] A more complete appreciation of the disclosed embodiments of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:Figure 1 is a schematic view of a refrigeration module of a thermodynamic system according to a preferred embodiment of the invention;Figure 2 is a flowchart of the method for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system of an LNG plant, according to a preferred embodiment of the invention;Figure 3 is a flowchart of the method directed to obtain a target operating condition of a refrigeration module of the thermodynamic system;Figure 4 is a flowchart of the method directed to determine a current LNG production performance and a health status of a refrigeration module of the thermodynamic system;Figure 5 is a schematic view that shows a plant control unit which controls the thermodynamic system and communicates, through Internet, with a server for providing cloud computing services; andFigure 6 is a flowchart of the method for controlling the transmission of one or more optimization parameters.DETAILED DESCRIPTION OF EMBODIMENTS

[0020] The present disclosure concerns a method for optimizing the operation of a thermodynamic system 1 of an LNG plant to allow the liquefaction of the natural gas G.

[0021] The thermodynamic system 1 may comprise one or more refrigeration modules 10 that may be remotely connected to each other. The refrigeration module 10 of the thermodynamic system 1 allows the continuous cooling of a natural gas G stream using a mixed refrigerant fluid MR. By cooling natural gas G to a predefined temperature, the natural gas G becomes liquid and its volume decreases several times, this makes it possible to transport the LNG by shipping through a vessel.

[0022] Figure 1 is a schematic view of a refrigeration module 10 of the thermodynamic system 1. As shown in figure 1, the refrigeration module 10 comprises a compressor 12 and a heat exchanger 14. The refrigeration module 10 allows thecontinuous cooling of a natural gas G stream to produce an LNG stream by using a mixed refrigerant fluid MR passed by the compressor 12 through the heat exchanger 14.

[0023] The compressor 12 is configured for compressing the mixed refrigerant fluid MR to high pressure and may include a rotary / centrifugal compressor, comprising a turbine driven by an electric motor.

[0024] The compressor 12 can be a single compressor or may comprise a plurality of compressors, such as for example multi-stage compressors for the compression of mixed refrigerants in refrigeration cycles. However, in other embodiments, a different type of compressor 12 can be installed, such as an alternating compressor or the like.

[0025] The compressor 12 may comprise a plurality of inlet guide vanes (IGVs), such as variable inlet guide vanes (VIGVs), that regulate the flow and pressure that enter in the first stage of the compressor 12 through separate passages for reducing turbulences and providing a compressor inlet pressure drop.

[0026] An interstage liquid / gas separator may be provided between the low-pressure compressor section and the high-pressure compressor section of the compressor 12, to remove the liquefied fraction of the mixture from the gaseous fraction, such that the gaseous fraction of the natural gas G can be delivered to the high-pressure compressor section for further compression.

[0027] The heat exchanger 14 carries on the liquefaction and cooling process of the natural gas Gby absorbing heat from the natural gas G. The heat exchanger 14 is fluid- dynamically connected to the compressor 12 and may be part of a cold box unit of the refrigeration module 10.

[0028] The heat exchanger 12 may be for example a multi-stream heat exchanger, wherein the natural gas G stream has to be liquefied and sub-cooled. In particular, the heat exchanger 12 allows the mixed refrigerant fluid MR inside to vaporize to cool and liquefy the natural gas G.

[0029] However, in some other embodiments, the number and / or type of heat exchangers 12 can be different, for example the heat exchanger 12 may be a brazed aluminum plate-fin heat exchanger (BAHX), or a wound coil heat exchanger, etc..

[0030] The refrigeration module 10 may comprise one or more Joule Thompson (JT) expansion valve (not shown in figure 1) in fluid communication with the heat exchanger 14 and configured to expand the cold, compressed natural gas G from high pressure to low pressure producing a refrigeration stream of expanded natural gas G. The JT valve(s) may be part may be part of a cold box unit of the refrigeration module 10.

[0031] The refrigeration module 10 may include a separator device, such as a cold gas separator equipment 16, that is capable of separating the mixed refrigerant fluid MR from the gaseous fraction thereof, in such a way that the mixed refrigerant fluid MR and the gaseous fraction enters into the heat exchanger 14 separately through separated relevant pipes.

[0032] The mixed refrigerant fluid MR used by the heat exchanger 12 is a mixture of fluids such as light hydrocarbons and nitrogen, which, compared to pure refrigerant fluids, do not condense and evaporate at constant temperature but over a range of temperatures, providing efficient cooling also in applications with high temperature variations.

[0033] In particular, the optimal mixed refrigerant fluid MR composition for the different refrigerant cycles in the LNG plants determines the maximum capacity and performance for the same LNG plant.

[0034] The mixed refrigerant fluid MR composition may include at least one of the following refrigerant components: nitrogen, methane, ethylene, propane, and isopentane. However, the composition of the mixed refrigerant MR may change over time during different seasons of the year. For instance, in the summer time there are several heavy hydrocarbons (HCC) in the mixed refrigerant MR while in the winter time, when the ambient temperature is colder, there are few heavy hydrocarbons in the same mixed refrigerant MR.

[0035] The refrigeration module 10 may also comprise a make-up section (not shown in figure 1) to perform injection of compounds in the refrigeration module. The makeup section may include one or more valves comprising at least one of a make-up valve, a drain valve, and a vent valve to perform make-up operations concerning the mixed refrigerant fluid MR which is normally done manually by the operator. The make-upsection may comprise one or more respective valve actuators 18, each of which controls the aperture and timing of a respective valve of the make-up section.

[0036] The refrigeration module 10 may comprise a processing unit 11 configured to execute program code comprising one or more instructions that causes the processing unit 11 to perform the method 100 described with reference to figure 2.

[0037] Referring now to figure 2 there is shown a flowchart of the method 100 for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system 1 of an LNG plant, according to a preferred embodiment of the invention. The method 100 allows to increase the LNG plant reliability and availability by benefitting of a prognostic analysis of the thermodynamic system 1.

[0038] The method 100 is a computer-implemented method 100 that can be implemented at a processing unit 11, 203, such as a processing unit 11 that is part of the thermodynamic system 1 as described above or a processing unit 203 provided on a remotely located server 200, such as the processing unit 203 described with reference to figure 5.

[0039] In general, the processing unit 11, 203 executes program code comprising one or more instructions that causes the processing unit 11, 203 to perform the method 100 for determining one or more optimization parameters in order to optimize the operation of the thermodynamic system 1. The program code can be stored in a dedicated memory of the processing unit 11, 203 or in a separated storage unit.

[0040] At step 101 of the method 100 it is obtained a target operating condition of a refrigeration module 10. The target operating condition of the refrigeration module 10 is indicative of the optimum production capability (e.g. production flow) of the one or more refrigeration modules 10 required to provide sufficient LNG to fill a predefined number of tanks of a vessel.

[0041] Step 101 is better explained with reference to figure 3 which shows a flowchart of a method directed to obtain a target operating condition of a refrigeration module 10 of the thermodynamic system 1. The method allows to harmonize the production of LNG in the thermodynamic system 1 while considering the vessel LNG storage capability.

[0042] In particular, step 101 may comprise initial sub steps in which one or more current weather and sea conditions and one or more forecasted weather and sea conditions are acquired. This is shown in respectively steps 1011 and 1012 of figure 3.

[0043] After receiving the current and forecasted conditions, at step 1013 a vessel estimated time of arrival (ETA) is obtained. The ETA is based on the current vessel position, the one or more current weather and sea conditions and the one or more forecasted weather and sea conditions obtained in the previous steps 1011 and 1012. In this way it is possible to predict the vessel arrival depending on the current and forecasted weather and sea conditions.

[0044] Step 101 may also comprise the sub step 1014 in which it is obtained a forecasted level of produced LNG in a tank of the LNG plant at the vessel ETA, based on a current storage level of the tank and an estimate actual production flow of the refrigeration module 10.

[0045] Subsequently, at step 1015, it is determined a general target operating condition of the one or more refrigeration modules 10 based on the forecasted level of produced LNG.

[0046] Finally, at step 1016, it is determined a target operating condition of a refrigeration module 10 based on the general target operating condition determined at step 1015.

[0047] Based on the target operating condition of the one or more refrigeration modules 10, the processing unit 11, 203 may generate at least one command signal to deactivate or activate at least one of the one or more refrigeration modules 10. In this way one or more refrigeration module can either be deactivated to decrease capacity or be activated to increase capacity in order to better harmonize the production of the thermodynamic system as a function of the vessel LNG storage capability and the vessel ETA. In other words, this solution allows to maximize the production accordingly with the shipping capacity / availability, without causing further delays and without saturating the LNG plant storage capacity.

[0048] Referring again to figure 2 the method 100 for determining one or more optimization parameters further comprises step 102 in which it is acquired, from at least one diagnostic module 13, operating data of the LNG plant.

[0049] The diagnostic module 13 may be configured to process and analyze extensive datasets utilizing a suite of advanced algorithms, digital twins, and engineering models. These computational methodologies operate in real-time to generate early warnings, facilitate predictive maintenance, and provide optimization insights. By dynamically assessing system parameters, the diagnostic module 13 is able to predict efficiency degradation, anticipates increases in emissions, and determines optimal maintenance intervals, thereby enhancing operational reliability and performance of the LNG plant.

[0050] The diagnostic module 13 allows to monitor the thermodynamic system 1 by interfacing with one or more of sensors / modules to acquire operating data of the LNG plant. The acquired data may be stored in a dedicated memory after being acquired and be accessible to the processing unit 11, 203.

[0051] The operating data comprises current operating parameters of the refrigeration module 10 and may comprise at least one of: a. a current opening status of Inlet Guide Vanes, IGV, of the compressor 12; b. a current inlet pressure of the compressor 12; c. a current interphase compressor pressure of the compressor 12; d. a current outlet pressure of the compressor 12; e. a current power absorbed by the compressor 12; f. a current location of a first operating point of the compressor 12 in a compression ratio vs compressor flow rate map that characterize the operating range of the compressor 12; g. a current location of second first operating point of the compressor 12 in a compression efficiency vs compressor flow rate map that characterize the operating range of the compressor 12;h. a current composition of the natural gas G stream; i. a current liquid level into a liquid-vapor separator of the refrigeration module 10; j . a current temperature variation across the one or more valves, such as the one or more JT expansion valves or on-off valves, of the refrigeration module 10; k. a current opening status of the one or more valves; l. a current temperature profile of different portions of the heat exchanger 14; m. a current opening status of a bypass valve for heating the cold gas separator equipment 16 at the heat exchanger 14; n. a liquid production of the cold gas separator equipment 16; o. a ratio between the LNG production and the mixed refrigerant MR current circulation rate; and p. a current pressure and a current temperature of the cold gas separator equipment 16.

[0052] At step 103, the method determines a current LNG production performance and a health status of the refrigeration module 10 on the basis of the acquired operating data.

[0053] Figure 4 shows a flowchart of a method for determining a current LNG production performance and a health status of a refrigeration module 10 of the thermodynamic system 1. This method may be performed, for example, by a dedicated MBS module (Module Breakdown Structure) that identifies for each module, subsystem and instrument of the thermodynamic system a corresponding health status to identify how close the thermodynamic system 1 is to a critical condition or by the processing unit 11, 203 itself.

[0054] As shown in figure 4, at step 1031, operating data is acquired from the one ormore sensors of the refrigeration module 10. The operating data comprises a series of data points, each of which is associated with a timestamp and a respective parameter of the refrigeration module 10, such as the refrigeration module parameter(s) listed above.

[0055] The operating data may be data stored in dedicated database in a timeseries format. Operating data may also comprise proprietary knowledge, design limits, operating conditions and / or trip and alarm levels. Operating data may also comprise data about operating conditions, such as thermodynamic and rotodynamic parameters concerning past performances or related performances from similar units.

[0056] Subsequently, the processing unit 11, 203 performs the following steps for each one of the parameters of the refrigeration module 10.

[0057] At step 1032 the processing unit 11, 203 determines a degree of severity according to the acquired operating data associated to the chosen parameter. The degree of severity can be determined by an analytic model based on a failure mode and effects analysis (FMEA), using for example literature knowledge enriched by more specific domain knowledge. The degree of severity or severity index indicates how much severe might be the potential failure identified by the analytic. The presence of a potential failure signature might not necessarily indicate that the equipment is at a critical stage, but rather be a warning that the current health status can evolve to a significant performance degradation if suitable corrective actions are not performed.

[0058] At step 1033 the processing unit 11, 203 determines a residual useful life, RUL, to achieve the critical status based on the progression along the time of the data points associated to the parameter. The RUL is the remaining time the equipment is likely to operate before it needs to be repaired or replaced.

[0059] At step 1034 the processing unit 11, 203 calculates a criticality index for the chosen parameter by combining the RUL and the degree of severity.

[0060] Finally, at step 1035, the processing unit 11, 203 determines a health status of the refrigeration module 10 based on the one or more criticality index calculated at step 1034. Power penalties which depend on the health status are thus associated for each equipment according to the criticality index.

[0061] Referring again to figure 2 the method 100 for determining the one or more optimization parameters further comprise step 104 in which it is determined a maximum loading capability of the refrigeration module 10 using a first artificial intelligence algorithm.

[0062] The first artificial intelligence algorithm is an algorithm trained on the relationship between the health status of the refrigeration module 10 and a maximum theoretical power achievable by the refrigeration module 10 on the basis of the current operating parameters.

[0063] The maximum theoretical power achievable by the refrigeration module 10 may be determined on the basis of the current operating parameters and at least one of: a local temperature of the refrigeration module 10; a component degradation status of the refrigeration module 10; a predicted temperature of the refrigeration module 10 based on weather and wind forecast at the location of the refrigeration module 10; and a predicted component degradation status of the refrigeration module 10.

[0064] The maximum theoretical power achievable by the refrigeration module 10 is based on the local refrigeration module temperature as well as the component degradation status of the refrigeration module 10.

[0065] The maximum theoretical power is representative of the performance achievable by the “healthily” refrigeration module 10 keeping into account the degradation (ageing, fouling etc...) of each component thereof as well as the impact of ambient temperature changes on the refrigeration module 10.

[0066] For example, the maximum theoretical power achievable by the refrigeration module 10 may be defined using the following equation:(1) Maximum Theoretical Power = (LocalModuleTemp, CompDegrStatus)

[0067] Wherein the CompDegrStatus represents the degradation level, or forecasted degradation level, of the components of the refrigeration module 10 and may be estimated using a digital twin that calculates or predicts the component field performance and the associated deviation in performance due to degradation for ageing, fouling, or other factors affecting component efficiency.

[0068] The CompDegr Status may include information about the driver efficiency, the driven efficiency and other equipment efficiency that can be accounted as efficiency loss for driven and driver equipment of the refrigeration module 10.

[0069] The LocalModuleTemp is the measured local module temperature of the refrigeration module 10. For example, the refrigeration module temperature may be affected by hot air coming from other nearby modules and therefore the local module temperature needs to be measured and considered for determining the maximum theoretical power.

[0070] The LocalModuleTemp can also be estimated / forecasted considering the actual location of the refrigeration module 10, the LNG plant layout including the presence of nearby heat sources, the wind and weather current and forecasted conditions, and the heat exchanger saturation model. The estimation can be based on the use of a digital twin module that calculates or predicts the local module temperature of the refrigeration model based on this data.

[0071] Data elaborated in steps 103 and 104 allow to understand each module health status and the maximum theoretical power achievable by the refrigeration module 10 to have a more realistic and “up to date” power capability that better reflects the actual status of the refrigeration module 10. Moreover, in this way it is possible to integrate real time equipment health status information into the optimization process.

[0072] The maximum loading capability of the refrigeration module 10 can be defined using the following equation:(2) Max Module Power = Theoretical Maximum Power - Power Limitation

[0073] Power Limitation represents penalties associated to the health status of each module / component that reduce the overall theoretical max power available.

[0074] The Max Module Power is the maximum loading capability of the refrigeration module 10. This provides a more realistic and “up to date” power capability that better reflects the actual / forecasted status of the refrigeration module 10, considering each module health status determined in step 103 as well as the maximum theoretical power achievable by the refrigeration module 10.

[0075] The maximum loading capability of the refrigeration module 10 may be better understood with reference to the example provided below for the calculation of the maximum loading capability in case of an electrical motor installed in cooling sections (cold box) of the refrigeration module 10 is damaged.

[0076] In this case, an anomaly associated with the function of electrical motor can be detected by an abnormal variation in vibration pattern associated with specific failure mode of the electrical motor, such as bearing degradation or other failure mode such as: motor stator incipient failure, voltage imbalance, corrosion, high temperature, overtorque etc.

[0077] The analytic model may then judge based on an FMEA analysis the degree of severity of the failure mode and the likelihood of occurrence based on statistical data and real time acquired data.

[0078] The criticality index associated with the specific equipment provides an indication a power penalty that reduce the overall theoretical max power available. The maximum module power can then be calculated using equation 2.

[0079] The method 100 for determining the one or more optimization parameters also comprises step 105 in which the one or more optimization parameters of the refrigeration module 10 are determined based on the maximum loading capability and the target operating condition. The one or more optimization parameters can be used to adjust the operation of the refrigeration module 10 to match the target operating condition.

[0080] The one or more optimization parameters are determined at step 105 using a second artificial intelligence algorithm, which is trained on the relationship between the maximum loading capability, the target operating condition and the one or more optimization parameters.

[0081] As described with reference to embodiments of the present disclosure, each of the artificial intelligence (Al) algorithms / models, or Machine Learning (ML) models, used in steps 104 and 105 can be any one of a data driven model, a physics based model, or a hybrid model which is both a data driven and physic based model. In an embodiment of the present disclosure, the AI / ML model can be a neural network, suchas a convolutional neural network (CNN). For example, the first and / or second artificial intelligence algorithm may be a hybrid model that combines physical knowledge with machine learning capabilities to improve predictive performance and robustness. This approach allows to reduce computational time and have the model ready without the need of very long training period.

[0082] The process optimization provides operative setting for the refrigeration module 10 and the perfect mixed refrigerant composition setting to operate the module refrigeration cycle at the maximum efficiency regardless the refrigeration module loading demand and without the risk of relaying on parameter(s) adjustments performed by non- sufficiently skilled operators.

[0083] The one or more optimization parameters may be displayed on a user interface, such as a unified platform, to allow the operator to manually apply one or more optimization parameters to the thermodynamic system 1 to optimize the operation of a thermodynamic system 1 and / or to confirm application of these parameters in case the one or more optimization parameters exceed the allowable operative range. The user interface can be a graphic user interface that displays information associated with the LNG plant. The user interface includes, for example, one or more of a display or touch screen display (for example, a liquid crystal display (LCD), a light emitting display (LED), an organic light emitting display (OLED), or a microelectromechanical system (MEMS) display).

[0084] The one or more optimization parameters may comprise at least one of a. at least one target composition of the mixed refrigerant fluid MR that increases the heat exchange efficiency of the heat exchanger 14; b. an optimal temperature set point across one or more valves, such as Joule- Thompson (JT) valves or on-off valves, of the refrigeration module 10; c. a temperature needed to liquefy the natural gas G to a target temperature in the heat exchanger 14; and d. a pressure set point and a temperature set point for a cold gas separator equipment 16 of the refrigeration module 10 to remove heavy hydro-carbon, HHC, from the natural gas G.

[0085] The at least one target composition of the mixed refrigerant fluid MR provides the optimal mixture adjustment that improves the heat exchanger efficiency by minimizing the distance between the heat exchanger temperature curves, which represent the composite hot and cold curves.

[0086] In this way it is possible to cooldown natural gas G to target temperature (e.g. -160°C) through the most efficient refrigeration process.

[0087] The process optimization at step 105 may also determine adjustment parameters for the mixed refrigerant fluid MR flow rate, circulation rate, pressure and temperature to maximize production by the compressor absorbed power in line with total driver power capability.

[0088] Based on selected refrigeration module type and cooling medium for the specific refrigeration module the mixed refrigerant fluid MR can be pre-cooled after recompression before entering into the heat exchanger 12. The optimal mixed refrigerant fluid MR inlet temperature in heat exchanger 12 increases the heat exchange efficiency in the main heat exchanger BAHX by reducing pre-cooling duty.

[0089] The process optimization at step 105 may be based on current MR composition related parameters including the ambient temperature, current natural gas G composition, the current pressure and temperature needed to cooldown natural gas to target temperature (i.e. -160°C) through the most efficient refrigeration circulation. The current refrigeration temperature / pressure profile along the circuit can be provided using commercially available simulation models.

[0090] For example, the mixed refrigerant fluid MR cooling set point of the heat exchanger 14 may depend upon the ambient temperature. The optimization may maximize the cooling of mixed refrigerant fluid MR at limit of heat exchanger saturation during warm weather and boost the LNG production of the heat exchanger 14 during cold conditions. In this manner, it is possible to overcome the seasonal production loss due to a not-optimized process and provide optimum production rate along the year.

[0091] The optimization process at step 105 may be based on the determination ofoptimal pressure set point and a temperature set point for a cold gas separator equipment 16 of the refrigeration module 10 to remove HHC from the natural gas G. Indeed, natural gas G composition may be affected by heavy HHC that may condensate during the cooling process in the heat exchanger 14 with the risk to plug the heat exchanger channels and impact the performance of the thermodynamic system 1. Excessive condensation might also increase the liquid level into the cold gas separator equipment 16 and allow HHC in liquid phase to be back into heat exchanger 16 affecting the functioning of the cold gas separator equipment 16. On the other hand, a poor condensation due to hotter temperature might not be effective for HHC removal.

[0092] The cold gas separator equipment 16 can be used to remove the HHC from natural gas steam G by creating the right conditions in terms of pressure and temperature to extract the HHC by condensation. For example, by mixing a hot stream with a cold stream of the natural gas G within the heat exchanger 14 it is possible to decrease the temperature of the cold gas separator equipment 16. In addition, it is possible to further decrease the temperature by using an expansion valve in the cold stream. As such the optimal pressure set point and temperature set point allow the HHC removal without condensation of other valuable natural gas components.

[0093] Once the one or more optimization parameters have been established, the processing unit 11, 203 may modify the at least one composition of the mixed refrigerant fluid MR. The optimization may be performed by controlling at least one valve actuator 18 of the thermodynamic system 1.

[0094] At step 106 it is determined, based on the current operating parameters and the one or more optimization parameters, the optimum valve actuation sequence to modify the at least one composition of the mixed refrigerant fluid MR throughout the refrigeration module 12. The determination of the one or more optimization parameters may be based on the initial refrigerant fluid MR composition measured at the compressor inlet to start the iteration process for refrigerant fluid MR adjustment.

[0095] At step 107 of the method 100 it is generated at least one signal for controlling at least one valve actuator 18 of the thermodynamic system 1, based on the determined optimum valve actuation sequence. The at least one signal allows to adjust the at least one composition of the mixed refrigerant fluid MR according to the at least one targetcomposition.

[0096] The valve actuator(s) 18 controls the aperture of a respective valve that controls the flow of a respective component of the mixed refrigerant fluid MR. The valve actuator(s) 18 may include at least one of a make-up valve, a drain valve, and a vent valve.

[0097] Step 107 may include a step in which the aperture and timing of the respective valve is controlled.

[0098] The determining the optimum valve actuation sequence comprises applying a methodology that tracks the relationship between the injected component of the mixed refrigerant fluid MR and an estimated heat exchange efficiency of the heat exchanger 14 based on the current operating parameters. The methodology may ensure the application of stable process to implement the one or more optimization parameters to the thermodynamic system.

[0099] This methodology can include for example an “Advanced Process Control” methodology, such as Model Predictive Control (MPC), that tracks the relationship between controlled and manipulated variables by process step-test that define the variable relationship upon process perturbation. The correlation between the controlled and manipulated variables makes the mixed refrigerant fluid MR mixture adjustment more reliable and predictable.

[0100] The Advanced Process Control methodology may comprise an initial step in which the valve is actuated based on the determined optimum valve actuation sequence in small steps until the target is achieved.

[0101] The valve actuation steps are defined based on the difference between the actual mixed refrigerant fluid MR composition and the target mixed refrigerant fluid MR composition as defined by the at least one target composition of the mixed refrigerant fluid MR. These actuation steps are small enough to avoid introducing a process instability during actuation. For example, the mixed refrigerant fluid MR component integration can be performed by opening one or more make-up valves for each specific component for a certain time according to the integration mass needed, for example a time comprised between 5 to 15 minutes.

[0102] For each small actuation step, the effect of the change introduced by the valve actuation is observed for a predetermined amount of time that is sufficient for creating an observable variation and the effect is compared against the expected result, for example the predetermined amount of time can be comprised between 30 to 60 minutes. This observation time allows the mixed refrigerant fluid MR integrated components to spread into the main refrigeration stream flow and the heat exchanger temperature to stabilize.

[0103] If the comparison indicates that the observed change corresponds to the expected result, the next small actuation step can be performed, otherwise a correction is applied. The correction can be calculated by a controller that applies Advanced Process Control methodology on the basis of on an updated Advanced Process Control model and a new tuning of the parameters of the Advanced Process Control model.

[0104] Figure 5 is a schematic view of a system for controlling the operation of a thermodynamic system 1 of an LNG plant. The system comprises at least one server 200 for providing cloud computing services; one or more user devices 301, 302; a cloud gateway 400 interfacing with the server 200 through the Internet 500 and configured to exchange data to and from the server 200 and the one or more user devices 301, 302; and a plant control unit 600 for controlling the thermodynamic system 1 in response to data received from the cloud gateway 400.

[0105] The server(s) 200 shown in figure 5 may be part of a cloud infrastructure and can be remotely located with respect to a thermodynamic system 1.

[0106] Each server 200 comprises a storage unit 201, for storing one or more optimization parameters; a communication unit 202, for transmitting data to the Internet 500 and receiving data from the Internet 500; and a processing unit 203.

[0107] The processing unit 203 executes program code stored in the storage unit 201 to perform the method 100 described above.

[0108] The processing unit 203 may be further configured to enable the communication unit 202 to transmit the one or more generated optimization parameters to a plant control unit 600 for controlling the thermodynamic system 1 accordingly to the one or more optimization parameters.

[0109] The method for controlling the transmission of the one or more optimization parameters is better described with reference to figure 6.

[0110] At step 601, during automatic operation, the processing unit 203 transmits the one or more generated optimization parameters to a plant control unit 600 for controlling the thermodynamic system 1 accordingly to the one or more generated optimization parameters.[OHl] In this way the automatic control of functionalities of the thermodynamic system 1 are active, and the server 200 generates the one or more optimization parameters automatically, at a predefined rate, and / or upon a request from a plant control unit 600.

[0112] Operator confirmation may be required in case the one or more optimization parameters exceed the allowable operative range or if the server 200 exceeds the predefined generation rate.

[0113] At step 602, the processing unit 203 receives through the Internet 500, a request for manual operation mode that comprises one or more manual optimization parameters to be applied from a user device 301, 302.

[0114] At step 603, in response to receiving 602 such a request for manual operation mode from a user device 301, 302, the processing unit 203 transmits the one or more manual optimization parameters to the plant control unit 600 for controlling the thermodynamic system 1 accordingly to the one or more manual optimization parameters without regard to the one or more optimization parameters generated by the processing unit 203. In this way the automatic functionalities may be disabled, and the plant control unit 600 may no longer accept the one or more optimization parameters generated by the server 200.

[0115] The cloud gateway 400 can be an Edge gateway and be configured to initiate a secure communication channel to receive a message comprising one or more optimization parameters from the server 203. The cloud gateway 400 can be further configured to extract the one or more optimization parameters from the message and provide the one or more optimization parameters to the plant control unit 600.

[0116] The one or more optimization parameters may comprise one or more manualoptimization parameters or one or more optimization parameters generated by the processing unit 203.

[0117] The plant control unit 600 may be configured to determined, based on the current operating parameters and the one or more optimization parameters, the optimum valve actuation sequence to modify the at least one composition of the mixed refrigerant fluid MR throughout the refrigeration module 12. The plant control unit 600 may receive periodic or “on-demand” updates comprising the one or more optimization parameters from the server 200.

[0118] In this way, the remote valve actuation may be achieved through an Edge to Cloud mixed solution, whereby part the actuation logic is entrusted to the plant control unit 600 (i.e. local controller or Edge) and the provision of one or more optimization parameters is entrusted to the at least one server 200 (i.e. the Cloud). The Edge to Cloud mixed solution allows to have a dedicated area for high computational capability for the provision of one or more optimization parameters.

[0119] A first advantage of the present disclosure is to optimize the operation of a thermodynamic system to provide multiple advantages to the customer, by finding the best process setup for process parameters like flow, pressure, temperature, and refrigerant gas composition.

[0120] A second advantage is to adjust heating and cooling curves to maximize plant production or suggesting customer to reduce production so as not to fill all available storage tanks while the cargo ship is not available.

[0121] A third advantage is to reduce human factor / expertise in the field to understand the health status of a thermodynamic system depending on a plurality of independent parameters. Indeed, through a unified platform it is possible to monitor process, equipment data, health status, KPIs (key performance indicator), and events in critical areas of the LNG plant and this allows customers and OEMs to jointly perform the proactive diagnostic and expertise advisory required to dive the plant towards operational excellence is not found. To make sure there are no instability-related problems, new proposed operation conditions for process optimization are dynamically checked. In other words, the unified platform unifies digital capability through analytics and algorithm to improve plant operation safety and “plant to plant”production consistency.

[0122] While aspects of the invention have been described in terms of various specific embodiments, it will be apparent to those of ordinary skill in the art that many modifications, changes, and omissions are possible without departing form the spirit and scope of the claims. In addition, unless specified otherwise herein, the order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments.

[0123] The subject matter described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them. The subject matter described herein can be implemented as one or more computer program products, such as one or more computer programs tangibly embodied in an information carrier (e.g., in a machine-readable storage device), or embodied in a propagated signal, for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers). A computer program (also known as a program, software, software application, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file. A program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0124] The processes and logic flows described in this specification, including the method steps of the subject matter described herein, can be performed by one or more programmable processors executing one or more computer programs to perform functions of the subject matter described herein by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus of the subject matter described herein can be implemented as, specialpurpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0125] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processor of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, (e.g., EPROM, EEPROM, and flash memory devices); magnetic disks, (e.g., internal hard disks or removable disks); magneto-optical disks; and optical disks (e.g., CD and DVD disks). The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0126] To provide for interaction with a user, the subject matter described herein can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, (e.g., a mouse or a trackball), by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0127] The techniques described herein can be implemented using one or more modules. As used herein, the term “module” refers to computing software, firmware, hardware, and / or various combinations thereof. At a minimum, however, modules are not to be interpreted as software that is not implemented on hardware, firmware, or recorded on a non-transitory processor readable recordable storage medium (i.e., modules are not software per se). Indeed “module” is to be interpreted to alwaysinclude at least some physical, non-transitory hardware such as a part of a processor or computer. Two different modules can share the same physical hardware (e.g., two different modules can use the same processor and network interface). The modules described herein can be combined, integrated, separated, and / or duplicated to support various applications. Also, a function described herein as being performed at a particular module can be performed at one or more other modules and / or by one or more other devices instead of or in addition to the function performed at the particular module. Further, the modules can be implemented across multiple devices and / or other components local or remote to one another. Additionally, the modules can be moved from one device and added to another device, and / or can be included in both devices.

[0128] The subj ect matter described herein can be implemented in a computing system that includes a back-end component (e.g., a data server), a middleware component (e.g., an application server), or a front-end component (e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described herein), or any combination of such back-end, middleware, and front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

[0129] Reference has been made in detail to the embodiments of the disclosure, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the disclosure, not limitation of the disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. Reference throughout the specification to "one embodiment" or "an embodiment" or “some embodiments” means that the particular feature, structure or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrase "in one embodiment" or "in an embodiment" or "in some embodiments" in various places throughout the specification is not necessarily referring to the same embodiment s). Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

[0130] When elements of various embodiments are introduced, the articles “a”, “an”, “the”, and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including”, and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.

Claims

CLAIMS1. A computer-implemented method (100) at a processing unit (11, 203) for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system (1) of a liquefied natural gas, LNG, plant, wherein the thermodynamic system (1) comprises: one or more refrigeration modules (10), wherein each refrigeration module (10) comprises a compressor (12) and a heat exchanger (14), which allows the continuous cooling of a natural gas (G) stream to produce an LNG stream by using a mixed refrigerant fluid (MR) passed by said compressor (12) through said heat exchanger (14); at least one diagnostic module (13), which allows for continuous monitoring of said one or more refrigeration modules (10); and the method (100) comprising the steps of: obtaining (101) a target operating condition of a refrigeration module (10) of said one or more refrigeration modules; acquiring (102), from said at least one diagnostic module (13), operating data of the LNG plant, wherein said operating data comprises current operating parameters of the refrigeration module (10); determining (103) a current LNG production performance and a health status of the refrigeration module (10) on the basis of the acquired operating data; determining (104) a maximum loading capability of the refrigeration module (10) using a first artificial intelligence algorithm, wherein the first artificial intelligence algorithm is trained on the relationship between the health status of the refrigeration module (10) and a maximum theoretical power achievable by the refrigeration module (10) on the basis of said current operating parameters; determining (105) based on said maximum loading capability and said target operating condition, using a second artificial intelligence algorithm, the one or more optimization parameters of the refrigeration module (10) that adjust the operation of the refrigeration module (10) to match the target operating condition, wherein said second artificial intelligence algorithm is trained on the relationship between said maximum loading capability, said target operating condition and said one or more optimization parameters.

2. The method (100) of claim 1, wherein the one or more optimization parameters comprise at least one of: at least one target composition of said mixed refrigerant fluid (MR) that increases the heat exchange efficiency of said heat exchanger (14); an optimal temperature set point across one or more valves of said refrigeration module (10); a temperature needed to liquefy said natural gas (G) to a target temperature in said heat exchanger (14); and a pressure set point and a temperature set point for a cold gas separator equipment (16) of the refrigeration module (10) to remove heavy hydro-carbon, HHC, from said natural gas (G).

3. The method (100) of claim 2, wherein said current operating parameters comprise at least one of: a current opening status of Inlet Guide Vanes, IGV, of said compressor (12); a current inlet pressure of said compressor (12); a current interphase compressor pressure of said compressor (12); a current outlet pressure of said compressor (12); a current power absorbed by said compressor (12); a current location of a first operating point of said compressor (12) in a compression ratio vs compressor flow rate map; a current location of second first operating point of said compressor (12) in a compression efficiency vs compressor flow rate map; a current composition of the natural gas (G) stream; a current liquid level into a liquid-vapor separator of said refrigeration module (io); a current temperature variation across said one or more valves of said refrigeration module (10); a current opening status of said one or more valves; a current temperature profile of different portions of said heat exchanger (14); a current opening status of a bypass valve for heating said cold gas separator equipment (16) at said heat exchanger (14); a liquid production of said cold gas separator equipment (16); a ratio between the LNG production and the mixed refrigerant (MR) currentcirculation rate; and a current pressure and a current temperature of the cold gas separator equipment (16).

4. The method (100) of any one of claims 1-3, comprising determining the maximum theoretical power achievable by the refrigeration module (10) on the basis of said current operating parameters and at least one of: a local temperature of said refrigeration module (10); a component degradation status of said refrigeration module (10); a predicted temperature of the of said refrigeration module (10) based on weather and wind forecast at a location of the refrigeration module (10); and a predicted component degradation status of said refrigeration module (10);5. The method (100) of any one of claims 1-4, comprising the steps of: determining (106), based on said current operating parameters and said one or more optimization parameters, the optimum valve actuation sequence to modify the at least one composition of said mixed refrigerant fluid (MR) throughout the refrigeration module (12), generating (107) at least one signal for controlling at least one valve actuator (18) of the thermodynamic system (1), based on the determined optimum valve actuation sequence, to adjust the at least one composition of said mixed refrigerant fluid (MR) according to the at least one target composition, wherein each said at least one valve actuator (18) controls the aperture of a respective valve that controls the flow of a respective component of said mixed refrigerant fluid (MR).

6. The method (100) of claim 5, wherein controlling at least one valve actuator (18) comprises controlling the aperture and timing of the respective valve, said valve comprising one among a make-up valve, a drain valve, and a vent valve; and wherein determining the optimum valve actuation sequence comprises applying a methodology that tracks the relationship between the injected component of the mixed refrigerant fluid (MR) and an estimated heat exchange efficiency of the heat exchanger (14) based on said current operating parameters.

7. The method (100) of any one of claims 1-6, wherein determining (103) acurrent LNG production performance and a health status of the refrigeration module (10) on the basis of the acquired operating data comprises: acquiring (1031), by one or more sensors of the refrigeration module (10), operating data comprising a series of data points, each data point associated with a timestamp and a respective parameter of the refrigeration module (10); for each of said parameter: determining (1032) a degree of severity according to the acquired operating data associated to said parameter, determining (1033) a residual useful life, RUL, to achieve the critical status based on the progression along the time of the data points associated to said parameter, calculating (1034) a criticality index for said parameter by combining said RUL and said degree of severity, and determining (1035) a health status of the refrigeration module (10) based on said one or more criticality index.

8. The method (100) of any one of claims 1-7, wherein obtaining (101) a target operating condition of the refrigeration module (10), comprises: acquiring (1011) one or more current weather and sea conditions; acquiring (1012) one or more forecasted weather and sea conditions; obtaining (1013) a vessel estimated time of arrival, ETA, based on a vessel current position, said one or more current weather and sea conditions and said one or more forecasted weather and sea conditions; obtaining (1014) a forecasted level of produced LNG in a tank of the LNG plant at the vessel ETA based on a current storage level of said tank and an estimate actual production flow of the refrigeration module (10); determining (1015) a general target operating condition of the one or more refrigeration modules (10) based on said forecasted level of produced LNG, wherein the target operating condition of the refrigeration module (10) is indicative of the optimum production capability of the one or more refrigeration modules (10) required to provide sufficient LNG to fill a predefined number of tanks of said vessel; determining (1016) a target operating condition of a refrigeration module (10) based on said general target operating condition.

9. The method of any one of claims 1-8, comprising generating based on said target operating condition of said one or more refrigeration modules (10), at least one command signal to deactivate or activate at least one of said one or more refrigeration modules (10).

10. The method (100) of any one of claims 1-9, wherein said processing unit (11) is part of the thermodynamic system (1).

11. At least one server (200) for providing cloud computing services that is remotely located with respect to a thermodynamic system (1), said server (200) comprising: a storage unit (201), for storing one or more optimization parameters; a communication unit (202), for transmitting data to the Internet (500) and receiving data from the Internet (500); and the processing unit (203), wherein the processing unit (203) executes program code stored in the storage unit (201) perform the method of any one of claims 1-9.

12. The server (200) of claim 11, wherein the processing unit (203) is further configured to enable the communication unit (202) to: transmit (601) said one or more generated optimization parameters to a plant control unit (600) for controlling said thermodynamic system (1) accordingly to the one or more optimization parameters; and in response to receiving (602) a request for manual operation mode from a user device (301, 302) through the Internet (500), said request comprising one or more manual optimization parameters to be applied without regard to the one or more optimization parameters generated by the processing unit (203), transmit (603) said one or more manual optimization parameters to the plant control unit (600) for controlling said thermodynamic system (1) accordingly to the one or more manual optimization parameters.

13. A system for controlling the operation of a thermodynamic system (1) of a liquefied natural gas, LNG, plant, the system comprising:- a server (200) for providing cloud computing services according to claim 11 or claim 12;- one or more user devices (301, 302);- a cloud gateway (400) interfacing with said server (200) through the Internet (500) and configured to exchange data to and from said server (200) and said one or more user devices (301, 302); - a plant control unit (600) for controlling said thermodynamic system (1) in response to data received from said cloud gateway (400); wherein the cloud gateway (400) is configured to initiate a secure communication channel to receive a message comprising one or more optimization parameters from said server; and extract the one or more optimization parameters from said message and provide the one or more optimization parameters to said plant control unit (600).

14. Computer program product comprising one or more instructions that when executed by processing unit (11, 203), causes the processing unit (11, 203) to perform the method of any one of claims 1-10.

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