JPWO2020059109A1 - Driving method, support device, learning device, and refinery operation condition setting support system - Google Patents

Abstract

The operation method for operating the device for distilling crude oil to produce a plurality of fractions is that when the oil type is switched, the amount of water contained in the crude oil after the switching or the flow rate of each of the plurality of fractions is determined. An estimated design process, a pre-preparation step to prepare in advance for accepting the crude oil after switching based on the water content or flow rate of the crude oil after switching, and a switching process to start accepting crude oil after switching. It includes an adjustment step for adjusting operating conditions for distilling crude oil after switching. In the adjustment step, the target set value of the control amount for controlling the device is adjusted according to the state value indicating the state of the device for distilling the crude oil after switching.

Description

The present invention relates to an operation method for operating an apparatus for manufacturing a petroleum product, a support device, a learning device, and a refinery operation condition setting support system that can be used for the operation method.

In refineries for refining crude oil to produce petroleum products, crude oil collected from various wells is used as a raw material depending on the market price, the amount of oil produced in each well, and the transportation status of crude oil from each well. Accepted. The crude oil received in the crude oil tank is introduced into an atmospheric distillation column and separated into a plurality of fractions having different boiling points. The fractions are further processed and upgraded by downstream equipment as needed to produce petroleum products.

When switching the oil type of the crude oil to be processed (hereinafter referred to as "oil type switching"), due to changes in the composition of hydrocarbon fractions and water contained in the crude oil, the atmospheric distillation column and heating The operating state of equipment such as furnaces can fluctuate rapidly. Conventionally, an experienced operator has set appropriate operating conditions by adjusting set values of various control amounts.

Japanese Unexamined Patent Publication No. 5-189062

However, whether or not the optimum operating condition can be achieved depends on the experience and skill of the operator, so it is necessary to rely on a small number of experienced and highly skilled skilled operators, which imposes a heavy burden. Was there. In addition, it was difficult for skilled operators to clarify the complicated interrelationships and key points of the numerous operation patterns, operation procedures, etc. in the operation for switching the oil type of crude oil. It was difficult to educate other operators on what points to keep in mind when adjusting operating conditions.

In order to solve such a problem, a technique has been proposed in which the operation method of a skilled operator is statistically processed and expressed by a logistic function or the like and used (see, for example, Patent Document 1). In refineries, such an approach is limited because the operating states of many devices are set by a large number of controls and they interfere with each other in a complex manner.

The present invention has been made in view of such a situation, and an object of the present invention is to provide a technique for supporting the setting of operating conditions capable of realizing suitable operation of a refinery.

In order to solve the above problems, the operation method of a certain aspect of the present invention is an operation method for operating an apparatus for distilling crude oil to produce a plurality of fractions, and is used when switching oil types. , Design process to estimate the amount of water contained in the crude oil after switching or the flow rate of each of multiple distillates, and advance preparation for accepting the crude oil after switching based on the amount of water or flow rate of the crude oil after switching. It is provided with a pre-preparation step for performing the above, a switching step for starting the acceptance of crude oil after switching, and an adjusting step for adjusting operating conditions for distilling the crude oil after switching. In the adjustment step, the target set value of the control amount for controlling the device is adjusted according to the state value indicating the state of the device for distilling the crude oil after switching.

Another aspect of the present invention is a support device. This device is a device for distilling crude oil to produce multiple fractions, and is designed to estimate the amount of water contained in the crude oil after switching or the flow rate of each of the multiple fractions when switching oil types. A pre-preparation process for preparing in advance for accepting the crude oil after switching based on the process and the water content or flow rate of the crude oil after switching, a switching process for starting acceptance of crude oil after switching, and a post-switching process. By the acquisition unit and the acquisition unit that acquire the information necessary to proceed with each process included in the operation method when the operation method including the adjustment process for adjusting the operating conditions for distilling crude oil is executed. It is provided with a presentation unit that presents the acquired information.

Yet another aspect of the present invention is a learning device. This device has a state value acquisition unit that acquires a state value indicating the state of the device for distilling crude oil, and a recommended value of a control amount for controlling the device at the time of oil type switching based on the state value. It is equipped with a learning unit that learns measures for calculation by machine learning.

Yet another aspect of the present invention is a refinery operating condition setting support system. This refinery operation condition setting support system learns a support device that supports the setting of operation conditions of a device for distilling crude oil to produce a plurality of distillates, and a policy used in the support device by machine learning. A learning device is provided, and the learning device calculates a recommended value of a control amount for controlling the device at the time of oil type switching based on an acquisition unit that acquires a state value indicating the state of the device and the state value. The support device is provided with a learning unit that learns the measures to be taken by machine learning, and the support device is a state value acquisition unit that acquires a state value indicating the state of the device at the time of oil type switching, and a learning device based on the state value. A calculation unit that calculates the recommended value of the control amount for controlling the device using the policy learned by, and the calculated recommended value is presented to the operator, or the calculated recommended value is used as the control amount. It includes an output unit to be set in the device as a target set value.

It should be noted that any combination of the above components and the conversion of the expression of the present invention between methods, devices, systems, recording media, computer programs and the like are also effective as aspects of the present invention.

According to the present invention, it is possible to provide a technique for supporting the setting of operating conditions capable of realizing suitable operation of a refinery.

It is a figure which shows the structure of the refinery schematicly. It is a figure which shows the whole structure of the refinery operation condition setting support system which concerns on embodiment. It is a figure which shows the procedure of the oil type switching operation. It is a figure which shows the detail of the pre-acceptance preparation (S16) in the oil type switching operation. It is a figure which shows the detail of charge pump flow rate control setting switching (S18) in oil type switching operation. It is a figure which shows the detail of the crude oil tank switching (S20) in the oil type switching operation. It is a figure which shows the detail of the flow rate adjustment (S22) to a heating furnace in an oil type switching operation. It is a figure which shows the detail of the fine adjustment (S24) of the flow rate of an intermediate fraction in an oil type switching operation. It is a figure which shows the detail of the tower top temperature adjustment and other points to be noted confirmation (S28) in the oil type switching operation. It is a figure which shows the detail of the flow rate adjustment (S30) of each reflux in oil type switching operation. It is a figure which shows the structure of the learning apparatus which concerns on embodiment. It is a figure which shows the structure of the operation condition setting support device and control device which concerns on embodiment.

The refinery operating condition setting support system according to the embodiment supports the setting of the refinery operating conditions. In this embodiment, a case of supporting the setting of operating conditions at the time of oil type switching in a refinery will be described in particular. The refinery operation condition setting support system should manually manage the procedure of oil type switching operation, which was conventionally executed by skilled operators, and adjust the information necessary to properly proceed with the oil type switching operation. By presenting the control amount, points to be noted, etc. to the operator, the operator is assisted in setting the operating conditions. In addition, the refinery operation condition setting support system sets recommended values for a plurality of control amounts for controlling a plurality of devices according to a plurality of state values indicating the states of a plurality of devices provided in the refinery. It is calculated using the measures (functions) learned by learning, and the calculated recommended value is presented to the operator to support the operator in setting the operating conditions.

FIG. 1 schematically shows the configuration of the refinery 3. The crude oil stored in the crude oil tanks 10a and 10b is extracted from the crude oil tanks 10a and 10b by the charge pump 11, preheated by heat exchange with the reflux fraction of the distillation column 15, and mixed with the injected water. It is introduced into the desalting device (dessolter) 12. In FIG. 1, charge pumps 11 are provided at the outlets of the crude oil tanks 10a and 10b, but in another example, crude oil may be extracted from a plurality of crude oil tanks by a common charge pump. In the desalting apparatus 12, impurities such as water, salt, iron and mud contained in the crude oil are removed as wastewater. The crude oil that has passed through the desalting apparatus 12 is further heated by heat exchange with each distillate extracted from the distillation column 15 and the bottom oil and the like, and is introduced into the pre-boiling column 13. Of the crude oil introduced into the pre-boiling column 13, the evaporated low boiling point fraction is directly introduced into the distillation column 15, and the high boiling point fraction of the liquid is heated in the heating furnace 14 and then introduced into the distillation column 15. The load on the heating furnace 14 can be reduced by preliminarily charging the low boiling point fraction contained in the preheated crude oil into the distillation column 15. The pre-boiling column 13 may not be provided, and in that case, all the crude oil may be heated in the heating furnace 14 and then introduced into the distillation column 15. Further, a sub-distillation column may be provided in place of the pre-boiling column 13, and in that case, the fractional distillation in the sub-distillation column may be separated as a product without being introduced into the distillation column 15.

In the distillation column 15, crude oil is separated into a plurality of fractions having different boiling points. Each fraction extracted from the distillation column 15 is introduced into the stripper 16. In the stripper 16, each fraction is contacted with superheated steam to adjust the flash point, and the low boiling point fraction is recirculated to the distillation column 15. Each fraction that has passed through the stripper 16 is cooled by crude oil before distillation in a heat exchanger to be obtained as kerosene, light gas oil, and heavy gas oil fractions. The low boiling point fraction extracted from the top of the distillation column 15 is temporarily stored in the top oil receiving tank (overhead accumulator) 17, and the gas component is used as a raw material for liquefied petroleum gas or sent to a gas recovery device. Introduced, the liquid component is petroleum. The bottom oil extracted from the bottom of the distillation column 15 is cooled by the crude oil before distillation in the heat exchanger to obtain normal pressure residual oil.

When switching the crude oil to be processed by the distillation column 15 from, for example, the crude oil stored in the crude oil tank 10a to the crude oil of a different oil type stored in the crude oil tank 10b, the desalting device 12, the pre-boiling column 13, and the heating furnace The operating state of each device such as 14, the distillation column 15, the stripper 16, and the top oil receiving tank 17 may change abruptly, and the composition and flow rate of each distillate extracted from the distillation column 15 may change. In a conventional refinery, in order to prevent each distillate produced during the oil type switching operation from deviating from the required specifications, until the oil type switching operation is completed and a stable steady operation is started. , The flow rate for extracting each distillate from the distillation column 15 was suppressed to be lower than that in the steady operation. Therefore, during the oil type switching operation, more crude oil is extracted from the distillation column 15 as the bottom oil, and the yield of atmospheric residual oil, which is a lower value fraction, increases, resulting in higher value. The yields of gasoline and kerosene, which are fractions, were decreasing. Therefore, in order to improve the production efficiency at the refinery 3, the yield of a higher value distillate is increased, the amount of tower bottom oil is reduced, and the oil type switching operation is constantly performed even during the oil type switching operation. There was a need for technology that could reduce the time required to move to operation.

The refinery operation condition setting support system of the present embodiment appropriately manages each process of the oil type switching operation according to the procedure of the oil type switching operation executed by a skilled operator, and is operated by the operator. Help set conditions. As a result, the oil type switching operation can be optimized and leveled at a high level regardless of the operator's experience and skill, so that the yield of high-value fractions during the oil type switching operation can be improved. At the same time, the time required to shift from the oil type switching operation to the steady operation can be shortened. As a result, the production efficiency and profitability of the refinery can be improved.

FIG. 2 shows the overall configuration of the refinery operating condition setting support system according to the embodiment. The refinery operating condition setting support system 1 is a learning for learning the refinery 3 for refining crude oil to produce petroleum products and the measures used to support the setting of operating conditions at the refinery 3. The device 40 is provided. The refinery 3 and the learning device 40 are connected by an arbitrary communication network 2 such as the Internet or an in-house connection system, and are operated in any operation form such as on-premises, cloud, or edge computing.

The refinery 3 learns from a control target device 5 such as an atmospheric distillation column and a heating furnace installed in the refinery 3, a control device 20 that sets a control amount for controlling the operating conditions of the control target device 5. It is provided with an operating condition setting support device 30 that supports the setting of the operating conditions of the refinery 3 by using the measures learned by the device 40. The operation condition setting support device 30 manages the procedure of the oil type switching operation, and information necessary for properly advancing the oil type switching operation, a state value to be stopped, a control amount to be adjusted, points to be noted, and the like. To the operator. Further, the operating condition setting support device 30 uses a policy learned by machine learning to set recommended target setting values of a plurality of control amounts according to a plurality of state values indicating the states of the plurality of control target devices 5. Calculate and present to the operator.

FIG. 3 shows the procedure of the oil type switching operation. The oil type switching operation includes a design step (S1), a preparatory step (S2), a switching step (S3), and an adjustment step (S4). In the design process (S1), first, the product yield obtained from the crude oil after switching is set based on the information such as the sampling location of the crude oil stored in the crude oil tank 10, and the water content is confirmed by a sample test or the like. At the same time (S10), the flow rates of the plurality of distillates extracted from the distillation column 15 are set (S12). Moisture content and fraction flow rates may be estimated using any known technique, such as a linear programming (LP) model.

Subsequently, in the pre-preparation step (S2), the time step of the oil type switching operation is set (S14), and the desalting device 12 and the pre-boiling tower are based on the water content of the crude oil after the switching or the flow rate of the distillate. For devices such as 13, the heating furnace 14, the distillation column 15, the stripper 16, and the top oil receiving tank 17, preparations are made in advance for accepting crude oil after switching (S16), and the flow control of the charge pump 11 is set. Switch from automatic to manual (S18).

Subsequently, in the switching step (S3), the crude oil tank 10 from which the crude oil is extracted is switched by the charge pump 11 to start accepting the crude oil after the switching (S20).

Subsequently, in the adjustment step (S4), the operating conditions for distilling the crude oil after switching are adjusted. In the adjustment step (S4), the flow rate of the intermediate distillate (kerosene, light oil, heavy light oil) is adjusted (S22) to the heating furnace 14 while giving priority to preventing the off-spec of the intermediate distillate. Fine adjustment (S24), quality adjustment of intermediate fraction (S26), column top temperature adjustment and confirmation of other points to be noted (S28), flow rate adjustment of each reflux (S30), crude oil preheating flow rate balance adjustment (S32), Includes confirmation of whether each setting is within the operating index (S34) and confirmation of the quality of the intermediate distillate (S36) (in no particular order). If the intermediate distillate satisfies the predetermined quality (Y in S36), the oil type switching operation is completed and the steady operation is started. If the intermediate distillate does not meet the predetermined quality (N in S36), the process returns to S24 and the quality adjustment of the intermediate distillate is continued. These steps are executed at the time step set in S14.

FIG. 4 shows the details of the pre-acceptance preparation (S16) in the oil type switching operation. In the pre-acceptance preparation (S16), the flow rate of water injected into the crude oil before introducing the crude oil into the desalting apparatus 12, based on the water content of the crude oil after switching or the flow rate of the distillate, the flow rate of the pre-boiling column 13. The liquid level, the flow rate of crude oil introduced from the heating furnace 14 to the distillation column 15, the liquid level of the top oil receiving tank 17, and the flow rate of each distillate of kerosene, light light oil, and heavy light oil are adjusted. For example, when it is estimated that the amount of water contained in the crude oil after switching is larger than that before switching, the flow rate of water injected into the crude oil is reduced in advance. Further, when the composition ratio of the low boiling point fraction contained in the crude oil after switching is estimated to be higher than that of the crude oil before switching, a larger amount of low boiling point fraction than before switching evaporates in the pre-boiling tower 13 to liquid. Since it is expected that the surface will drop, the liquid level should be raised in advance. After the crude oil is landed from the tanker to the crude oil tank, the mud water content of the crude oil can be reduced by allowing it to stand in the crude oil tank for a sufficient time to separate the mud water content. Unlike the composition ratio of each fraction of crude oil, it can be adjusted in advance. When the water content of the crude oil is reduced to a level that does not affect the operation in advance, the water content of the crude oil after switching may not be taken into consideration in the pre-acceptance preparation (S16).

FIG. 5 shows the details of the charge pump flow rate control setting switching (S18) in the oil type switching operation. When the charge pump 11 to be operated is switched and the crude oil tank 10 for extracting crude oil is switched, the liquid level of the crude oil tank 10 changes from a low place to a high place, so that the pump discharge pressure rises sharply and the flow rate fluctuates. Therefore, the function of automatically controlling the flow rate of the charge pump 11 is temporarily switched off so that fine adjustments can be made manually.

FIG. 6 shows the details of the crude oil tank switching (S20) in the oil type switching operation. When the charge pump 11 to be operated is switched and the crude oil is extracted from the crude oil tank 10 storing the crude oil after the switching, the flow rate of the charge pump 11, the length of the pipe from the crude oil tank 10 to the desalting device 12, etc. After a lapse of time approximately determined based on the above, the crude oil after switching arrives at the desalting device 12. The flow rate of water injected into the crude oil introduced into the desalting apparatus 12 is adjusted in advance in S16 based on the amount of water estimated in S10, but the water remaining in the crude oil tank 10 and the crude oil There is a possibility that the crude oil contains a different amount of water than the estimated amount due to the water mixed in the crude oil during transportation, so check the amount of water in the desalination device 12 and adjust if necessary. I do. When the amount of water contained in the crude oil is large, for example, an overcurrent is generated in the desalting apparatus 12, the interface becomes difficult to see, the liquid level and the temperature of the front boiling column 13 fluctuate, and the inlet temperature of the heating furnace 14 is high. Or the pressure of the distillation column 15 rises, so check these state values. When it is confirmed that the amount of water contained in the crude oil is large, the amount of water injected into the crude oil is reduced, the amount of defoaming agent injected is increased, or the amount of superheated steam introduced into the distillation column 15 is increased. It may be reduced. As described above, if it is confirmed in advance that the water content of the crude oil has been reduced to a level that does not affect the operation, it may not be necessary to make adjustments regarding the water content.

FIG. 7 shows the details of the flow rate adjustment (S22) to the heating furnace in the oil type switching operation. After the crude oil after switching to the desalting device 12 arrives, and after a lapse of time that is roughly determined based on the length and flow rate of the pipes from the desalting device 12 to the pre-boiling tower 13 and the heating furnace 14, the pre-boiling tower Since the crude oil after switching reaches the heating furnace 14 and the heating furnace 14, the liquid level of the front boiling tower 13 is confirmed, and the flow rate to the heating furnace 14 is adjusted if necessary.

FIG. 8 shows the details of the fine adjustment (S24) of the flow rate of the intermediate distillate in the oil type switching operation. By the steps up to S22, preparations for introducing the crude oil after switching into the distillation column 15 are ready, and it can be expected that the equipment upstream of the distillation column 15 is basically in a state where automatic operation is possible. After that, fine adjustment is made so that the amount of the distillate produced from the crude oil after switching becomes the optimum amount within the specifications required for each distillate. First, the flow rate of the pump for extracting each intermediate fraction from the stripper 16 is adjusted. The flow rate of naphtha extracted from the top of the tower is adjusted according to the liquid level of the top oil receiving tank 17. The flow rates of kerosene, light gas oil, and heavy gas oil extracted from the stripper 16 are gradually adjusted to the optimum flow rates while checking the quality of each intermediate distillate. The quality of each intermediate fraction is analyzed by an online analyzer or the like.

FIG. 9 shows the details of the tower top temperature adjustment and other points to be noted confirmation (S28) in the oil type switching operation. If the tower top temperature drops below the specified value, acidic substances may condense at the tower top, and the acidic substances may cause corrosion of the equipment material. Therefore, the tower top temperature should not drop below the specified value. Adjust the flow rate of the distillate extracted from the tower and the flow rate of the reflux recirculated to the top of the tower. In addition, check whether the conditions such as the design temperature, pressure limit, and flow velocity limit of each device are satisfied, and make adjustments as necessary.

FIG. 10 shows the details of the flow rate adjustment (S30) of each reflux in the oil type switching operation. The crude oil before distillation is heated by being introduced into the heat exchanger from the distillation column 15, and the flow rate of a plurality of reflux that is cooled and recirculated to the distillation column 15 is preheated by the temperature of each tray in the distillation column 15. The temperature distribution of the distillation column 15 is optimized by adjusting it according to the temperature of the crude oil and the like, and energy saving is also achieved.

In the oil type switching operation according to the above procedure, the control amount for adjusting the flow rate and the liquid level may be set manually by the operator, but the refinery operation condition setting support system of the present embodiment may be performed manually. In 1, in order to further improve the production efficiency in the refinery 3, the recommended value of each control amount in the oil type switching operation is calculated by using the policy learned by machine learning.

FIG. 11 shows the configuration of the learning device according to the embodiment. The learning device 40 includes a state value acquisition unit 41, an action determination unit 42, a reward value acquisition unit 43, an action value function update unit 44, a neural network 45, a learning control unit 46, a simulator 47, an operation data acquisition unit 48, and an operation data holding unit. A unit 49 and a simulator learning unit 50 are provided. Speaking of hardware components, these configurations are realized by the CPU, memory, programs loaded in the memory, etc. of any computer, but here, the functional blocks realized by their cooperation are drawn. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways by hardware only, software only, or a combination thereof.

The operation data acquisition unit 48 includes a state value indicating the state of each controlled device 5 when the refinery 3 is operated, a target set value of the control amount set by each control device 20, the environment and state of the refinery 3, and the like. The measured values and the like indicating the above are acquired as operation data from the refinery 3 and stored in the operation data holding unit 49.

The simulator learning unit 50 learns the simulator 47 that simulates the behavior of the refinery 3 by machine learning. The simulator learning unit 50 refers to the driving data stored in the driving data holding unit 49 as teacher data, and learns the difference from the simulator 47. The simulator 47 may simulate the operating behavior of the entire refinery 3, and may include a desalting device 12, a front boiling column 13, a heating furnace 14, a distillation column 15, a stripper 16, a tower top oil receiving tank 17, and the like. It may be a combination of devices that simulate the driving behavior of each of the devices. When the simulator 47 is composed of a combination of a plurality of simulators simulating each control target device 5, the simulator learning unit 50 first learns each of the plurality of simulators and individually adjusts the accuracy of each simulator. After the improvement, the entire simulator 47 in which a plurality of simulators are combined may be learned. By learning the simulator 47 using the operation data when the refinery 3 was operated in the past, the simulator created for general purpose can be adjusted according to the environment and configuration of the refinery 3. , The accuracy of estimation by the simulator can be improved.

The learning control unit 46 acquires a measure for the operation condition setting support device 30 to calculate a recommended value of the control amount to be set in each control target device 5 in the oil type switching operation by deep reinforcement learning.

Reinforcement learning seeks measures that allow an agent placed in an environment to act on the environment and maximize the rewards obtained from that action. The agent takes action on the environment, the environment updates the state, evaluates the behavior, and informs the agent of the state and reward in chronological order, maximizing the expected value of the total reward obtained. Optimize behavioral value functions and strategies so that. More specifically, the action determination unit 42 determines the target setting value of the control amount for controlling the oil type switching operation in the refinery 3, and the state value acquisition unit 41 determines the determined target setting value. A plurality of state values indicating the states of the refinery 3 set and operated after a predetermined time are acquired, the reward value acquisition unit 43 acquires the reward value for the state, and the action value function update unit 44 obtains. Optimize behavioral value functions and strategies based on rewards received.

In the present embodiment, the state s of the refinery 3 defined by the state values of the plurality of controlled target devices 5 and the option of the action a in which the target set value of the control amount is input to the plurality of controlled target devices 5 in the state s. Since the number of combinations of is enormous, deep reinforcement learning in which the action value function is approximated by the neural network 45 is executed. The algorithm for deep reinforcement learning may be DQN (Deep Q-Learning Network), DDQN (Double DQN), or any other algorithm. The neural network 45 may be a feedforward neural network such as a multi-layer perceptron neural network, a simple perceptron neural network, or a convolutional neural network, or may be any other type of neural network. All the state values indicating the states of all the controlled target devices 5 are input to the input layer of the neural network 45, and the target setting values of all the control amounts input to all the controlled target devices 5 are input from the output layer. The value of is output.

The learning control unit 46 determines the learning policy and content, and executes deep reinforcement learning. In the present embodiment, the learning control unit 46 uses the past operation data in the refinery 3 stored in the operation data holding unit 49, and measures are taken from the behavior of the oil type switching operation executed in the past in the refinery 3. The operation result learning mode for learning the above and the virtual operation learning mode for learning the policy from the behavior of the oil type switching operation simulated under unknown operation conditions are controlled by using the simulator 47.

The learning control unit 46 sets initial conditions such as oil type and water content before and after switching, starts a trial, determines a target set value of the control amount, and uses the determined target set value of the control amount. Acquiring a plurality of state values indicating the states of the controlled refinery 3 after a predetermined time is executed according to the above procedure of the oil type switching operation, and when the procedure of the oil type switching operation is completed, one trial is completed. Then set the initial conditions again and start the next trial. When the learning control unit 46 satisfies a predetermined condition such that the obtained reward value is less than a predetermined value and it is clear that the running trial does not give a good result, the oil type is used. The trial may be terminated and the next trial may be started before the switching operation procedure is completed.

In the driving performance learning mode, the learning control unit 46 sets the target set value actually set by the operator in the past according to the past driving data stored in the driving data holding unit 49, and the target set value. Is set and the acquisition of a plurality of state values is repeated after the actual operation is performed. That is, the action determination unit 42 determines the setting of the target set value actually set by the operator in the past as the next action according to the operation data stored in the operation data holding unit 49, and the state value acquisition unit 41 Acquires a plurality of state values stored in the operation data holding unit 49 as state values indicating the states of each controlled target device 5 after the target set value is set. Since the trial proceeds according to the operation data stored in the operation data holding unit 49, the learning may proceed without going through the action determination unit 42. The reward value acquisition unit 43 acquires the reward value for the state of the refinery 3 indicated by the past operation data, and the action value function update unit 44 is a neural based on the reward value acquired by the reward value acquisition unit 43. The action value function represented by the network 45 is updated. As a result, the quality of control by the operator in the oil type switching operation actually executed in the past can be reflected in the action value function expressed by the neural network 45. Details of the calculation of the reward value and the update of the action value function will be described later.

In the virtual driving learning mode, the learning control unit 46 sets the target set value by the action determination unit 42 and acquires a plurality of state values after a predetermined time simulated by the simulator 47 in which the target set value is set. repeat. The action determination unit 42 determines the target setting values of the plurality of control amounts to be input to the simulator 47. The action determination unit 42 determines the target setting values of a plurality of control quantities at random or based on the action value function expressed by the neural network 45. The action determination unit 42 randomly determines the target setting value of the control amount according to an arbitrary known algorithm such as the ε-greedy method, or the control amount that maximizes the expected value based on the action value function. You may choose whether to determine the target set value. As a result, it is possible to efficiently proceed with learning and shorten the time until learning converges while trying a wide variety of options. Further, the action determination unit 42 may select an action that has not been selected in the past driving data stored in the driving data holding unit 49. This makes it possible to search for actions that the operator did not select in the past oil type switching operation but can produce good results. The learning control unit 46 may set a state value reflecting the influence of the disturbance in the simulator 47 at a random timing and learn an appropriate method of coping with the disturbance.

The state value acquisition unit 41 acquires a plurality of state values indicating the states of the plurality of control target devices 5 from the simulator 47. The reward value acquisition unit 43 acquires the reward value for the state of the refinery 3 indicated by the plurality of state values acquired by the state value acquisition unit 41. This reward value is a numerical value of the quality of the oil type switching operation executed at the refinery 3. More specifically, the reward value is (1) the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, (2) the yield of a plurality of fractions, and (3) adjustment. Based on at least one of the amount of energy consumed in the process, (4) the degree of satisfaction of the operating conditions required in the adjustment process, (5) the operator's evaluation of the operating conditions in the adjustment process, or a combination thereof. It is quantified. The weight of each of these factors for quantifying the reward value may be determined according to the operation policy of the refinery 3. The reward value may be quantified based on another valuation factor in place of any of the above valuation factors or in addition to the above valuation factors.

When the reward value is quantified based on the evaluation factor (5), the evaluation by the operator used in the evaluation factor (5) may be provided from the operator terminal 60 to the learning device 40. The operator terminal 60 includes an evaluation acquisition unit 61 and an evaluation transmission unit 62. The evaluation acquisition unit 61 operates the status of the oil type switching operation executed in the refinery 3 and the status of the oil type switching operation virtually executed in the simulator 47 of the learning device 40 via a display device or the like. Present it to the staff and obtain an evaluation of the driving situation from the operator via an input device or the like. The evaluation transmission unit 62 transmits the evaluation by the operator acquired by the evaluation acquisition unit 61 to the learning device 40 via a communication device or the like. The operator terminal 60 may be realized by the learning device 40, may be realized by the operation condition setting support device 30 or the control device 20 of the refinery 3, or may be realized as a device different from them. good.

The action value function update unit 44 updates the action value function represented by the neural network 45 based on the reward value acquired by the reward value acquisition unit 43. The action value function update unit 44 outputs a set of action value functions taken by the action decision unit 42 in a certain state s, and as a result of the action taken by the action decision unit 42 in a certain state s, the reward value acquisition unit 43 The weight of the neural network 45 is trained so as to approach the expected value of the sum of the reward values obtained by the above and the reward values that would be obtained if the optimum action is continued thereafter. That is, the action value function update unit 44 is the sum of the reward value actually obtained by the reward value acquisition unit 43 and the expected value of the reward value that will be obtained thereafter multiplied by the time discount, and the action value. Adjust the weight of each connection in each layer of the neural network 45 to reduce the error between the output value of the function. As a result, the weight is updated so that the action value calculated by the neural network 45 approaches the true value, and learning proceeds.

The learning in the driving performance learning mode and the learning in the virtual driving learning mode may be executed any number of times, in an order, or in a combination. For example, first, in the driving performance learning mode, learning is advanced using past driving data, and when the quality of setting the target set value in the past driving is reflected in the action value function to some extent, the virtual driving learning mode Therefore, learning may be advanced for a wide range of options under more diverse driving conditions.

It may be verified by using the past driving data whether or not the trained neural network 45 can be used to make an accurate action decision. For example, as in the operation record learning mode, the action determination unit 42 uses the trained neural network 45 in parallel while proceeding with the trial of the oil type switching operation according to the operation data stored in the operation data holding unit 49. Decide what to do next. When the action determined by the action determining unit 42 is different from the past driving record stored in the driving data holding unit 49, the quality of the action determined by the action determining unit 42 is determined based on the reward value obtained thereafter. If it is evaluated that the behavior is not good, the behavior determination unit 42 does not select the behavior, or the behavior determination unit 42 determines the behavior similar to the past driving performance. , Adjust the neural network 45. The quality of the action determined by the action determination unit 42 may be evaluated based on, for example, the accumulated value of the reward value for the past driving performance that has been subsequently advanced according to the driving data up to a predetermined time, or the action. The subsequent driving behavior when the determination unit 42 takes the determined action may be estimated by the simulator 47, and further evaluated based on the integrated value of the reward value for the estimated driving behavior up to a predetermined time.

In this figure, the learning device 40 is shown as a single device for the sake of simplification of the description, but the learning device 40 is realized by a plurality of servers by using cloud computing technology, distributed processing technology, and the like. You may. As a result, the time required to improve the learning accuracy can be significantly reduced.

FIG. 12 shows the configurations of the operating condition setting support device and the control device according to the embodiment. The control device 20 includes a control unit 21 and an operation panel 22.

The operation panel 22 displays various state values indicating the operating state of the refinery 3 and target setting values of various control amounts set by the control device 20 on the display device, and sets targets of various control amounts. Accepts value input from the operator.

The control unit 21 includes a state value acquisition unit 23, a state value transmission unit 24, and a set value input unit 25. These functional blocks can also be realized in various forms by hardware only, software only, or a combination thereof.

The state value acquisition unit 23 acquires various state values indicating the operation state and operation result of the refinery 3 from various sensors and measuring instruments provided in the control target device 5 and the like, and displays the operation panel 22. Display on. The state value transmission unit 24 transmits the state value acquired by the state value acquisition unit 23 to the operation condition setting support device 30 and the learning device 40. The set value input unit 25 inputs target set values of various control amounts received from the operator by the operation panel 22 to the control target device 5, and displays them on the display device of the operation panel 22. The set value input unit 25 may automatically input the recommended value of the control amount acquired from the operation condition setting support device 30 to the control target device 5.

The operating condition setting support device 30 includes a control unit 31.

The control unit 31 includes a state value receiving unit 32, a recommended value calculating unit 33, a recommended value output unit 34, a policy updating unit 35, an information presenting unit 36, and a procedure management unit 37. These functional blocks can also be realized in various forms by hardware only, software only, or a combination thereof.

The state value receiving unit 32 acquires a plurality of state values from the state value transmitting unit 24 of the control device 20. The recommended value calculation unit 33 calculates the recommended values of a plurality of control amounts from the plurality of state values received by the state value receiving unit 32 by using the policy learned by the learning device 40. The recommended value output unit 34 outputs the recommended values of the plurality of control amounts calculated by the recommended value calculation unit 33 to the operation panel 22 of the control device 20 or the set value input unit 25. The policy update unit 35 acquires the policy relearned by the learning device 40 and updates the recommended value calculation unit 33.

The procedure management unit 37 holds the above-mentioned oil type switching operation procedure, and during the oil type switching operation, the procedure of the oil type switching operation and the information necessary for appropriately advancing each process of the oil type switching operation. The information presentation unit 36 is made to present the control amount to be adjusted, points to be noted, and the like. The information presenting unit 36 presents the above information on the operation panel 22 of the control device 20.

As a result, the setting of operating conditions in the oil type switching operation, which was conventionally operated by relying on intuition based on the experience of a skilled operator, can be optimized and leveled at a high level. Efficiency can be improved. Further, since the load on the heating furnace 14 can be reduced and the efficiency in the heat exchanger can be improved, the energy consumed in the refinery 3 can be reduced. In addition, since adjustment and maintenance of the target set value are not required, the load of system management and maintenance can be reduced.

The present invention has been described above based on examples. This embodiment is an example, and it will be understood by those skilled in the art that various modifications are possible for each of these components and combinations of each processing process, and that such modifications are also within the scope of the present invention. ..

An operating method of an aspect of the present invention is an operating method for operating an apparatus for distilling crude oil to produce a plurality of fractions, and is included in the crude oil after switching at the time of oil type switching. A design process that estimates the water content or the flow rate of each of a plurality of distillates, and a pre-preparation process that makes advance preparations for accepting the crude oil after switching based on the water content or flow rate of the crude oil after switching, and switching. It is provided with a switching step of starting the acceptance of the crude oil after the switching and an adjusting step of adjusting the operating conditions for distilling the crude oil after the switching. In the adjustment step, the target set value of the control amount for controlling the device is adjusted according to the state value indicating the state of the device for distilling the crude oil after switching.

In the preparatory step, based on the oil type, water content, or flow rate after switching, the flow rate of water injected into the crude oil before introducing the crude oil into the desalination device, the device for temporarily storing the crude oil. Liquid level, the flow rate of crude oil introduced into the distillation column that distills crude oil from the heating furnace for heating crude oil, the liquid level of the device for temporarily storing the distillate distilled from the distillation column, Alternatively, the flow rates of the plurality of distillates may be adjusted.

The device for temporarily storing crude oil may include a pre-boiling tower.

The adjustment process includes a process of acquiring a state value, a calculation process of calculating a recommended value of a controlled amount based on the state value, and presenting the calculated recommended value to the operator, or presenting the calculated recommended value to the operator. It may include a step of setting the device as a target set value.

In the calculation process, the recommended value may be calculated using the policy learned by machine learning.

The policy may be learned by reinforcement learning.

The measures are the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, the yields of multiple fractions, the amount of energy consumed in the adjusting process, and the operating conditions required in the adjusting process. It may be learned by reinforcement learning using a reward value based on at least one of the sufficiency of the above and the evaluation by the operator for the operating condition in the adjustment process, or a combination thereof.

The policy may be learned by reinforcement learning using the state value when the device has been operated in the past and the reward value based on the target setting value.

The policy may be learned by reinforcement learning using a reward value based on a state value when a target set value is set in a simulator that simulates the operating condition of the device.

Another aspect of the present invention is a support device. This device is a device for distilling crude oil to produce multiple fractions, and is designed to estimate the amount of water contained in the crude oil after switching or the flow rate of each of the multiple fractions when switching oil types. A pre-preparation process for preparing in advance for accepting the crude oil after switching based on the process and the water content or flow rate of the crude oil after switching, a switching process for starting acceptance of crude oil after switching, and a post-switching process. By the acquisition unit and the acquisition unit that acquire the information necessary to proceed with each process included in the operation method when the operation method including the adjustment process for adjusting the operating conditions for distilling crude oil is executed. It is provided with a presentation unit that presents the acquired information.

In the adjustment process, the state value acquisition unit that acquires the state value indicating the state of the device and the recommended value of the control amount for controlling the device using the policy learned by machine learning are calculated based on the state value. The calculation unit may be provided, and an output unit that presents the calculated recommended value to the operator or sets the calculated recommended value as the target setting value of the control amount in the apparatus may be provided.

The measures are the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, the yield of multiple fractions obtained by distilling the crude oil, the amount of energy consumed in the adjustment process, and the amount of energy consumed in the adjustment process. It may be learned by reinforcement learning using a reward value based on at least one of the satisfaction of the operating conditions required in the adjusting process and the evaluation by the operator for the operating condition in the adjusting process, or a combination thereof.

Yet another aspect of the present invention is a learning device. This device has a state value acquisition unit that acquires a state value indicating the state of the device for distilling crude oil, and a recommended value of a control amount for controlling the device at the time of oil type switching based on the state value. It is equipped with a learning unit that learns measures for calculation by machine learning.

The learning unit requires the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, the yield of a plurality of fractions obtained by distilling the crude oil, the amount of energy consumed, and the required amount. The policy may be learned by reinforcement learning using a reward value based on at least one of the satisfaction of the driving conditions and the evaluation by the operator for the driving situation or a combination thereof.

1 Refinery operation condition setting support system, 3 Refinery, 5 Control target device, 10 Crude oil tank, 11 Charge pump, 12 Demineralizer, 13 Front boiling tower, 14 Heating furnace, 15 Distillation tower, 16 Stripper, 17 Tower top Oil receiving tank, 20 control device, 21 control unit, 22 operation panel, 23 status value acquisition unit, 24 status value transmission unit, 25 setting value input unit, 26 evaluation acquisition unit, 27 evaluation transmission unit, 30 operating condition setting support device, 31 Control unit, 32 Status value reception unit, 33 Recommended value calculation unit, 34 Recommended value output unit, 35 Policy update unit, 36 Information presentation unit, 37 Procedure management unit, 40 Learning device, 41 State value acquisition unit, 42 Action decision Unit, 43 Reward value acquisition unit, 44 Action value function update unit, 45 Neural network, 46 Learning control unit, 47 Simulator, 48 Driving data acquisition unit, 49 Operation data holding unit, 50 Simulator learning unit, 60 Operator terminal, 61 Evaluation acquisition unit, 62 evaluation transmission unit.

The present invention can be used in a refinery operating condition setting support system that supports the setting of operating conditions that can realize suitable operation of the refinery.

Claims (14)

An operating method for operating equipment for distilling crude oil to produce multiple fractions.
When switching oil types, a design process that estimates the amount of water contained in the crude oil after switching or the flow rate of each of the plurality of fractions, and
A pre-preparation step for making advance preparations for accepting the crude oil after switching based on the water content of the crude oil after switching or the flow rate.
The switching process to start accepting crude oil after switching, and
An adjustment process that adjusts the operating conditions for distilling crude oil after switching, and
With
In the adjustment step, the operation method is characterized in that a target set value of a control amount for controlling the device is adjusted according to a state value indicating a state of the device for distilling crude oil after switching. In the preparatory step, a device for temporarily storing the flow rate of water injected into the crude oil and the crude oil before introducing the crude oil into the desalination apparatus based on the water content of the crude oil after switching or the flow rate. Liquid level, the flow rate of crude oil introduced into the distillation column that distills crude oil from the heating furnace for heating crude oil, and the liquid level of the device for temporarily storing the distillate distilled from the distillation column. The operation method according to claim 1, wherein the level or the flow rate of the plurality of distillates is adjusted. The adjustment step is
The process of acquiring the state value and
A calculation step of calculating a recommended value of the control amount based on the state value, and
The process of presenting the calculated recommended value to the operator or setting the calculated recommended value as the target setting value in the device, and
The operation method according to claim 1 or 2, wherein the method comprises. The operation method according to claim 3, wherein in the calculation step, the recommended value is calculated using the policy learned by machine learning. The driving method according to claim 4, wherein the policy is learned by reinforcement learning. The measures include the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, the yields of the plurality of fractions, the amount of energy consumed in the adjusting step, and the requirements in the adjusting step. The claim is characterized in that it is learned by reinforcement learning using a reward value based on at least one of the satisfaction of the operating conditions to be performed and the evaluation by the operator for the operating condition in the adjustment process, or a combination thereof. The operation method according to 5. The operation method according to claim 6, wherein the measure is learned by reinforcement learning using the state value and the reward value based on the target set value when the device has been operated in the past. 6. The operation method described in. In an apparatus for distilling crude oil to produce a plurality of fractions, a design process for estimating the amount of water contained in the crude oil after switching or the flow rate of each of the plurality of fractions at the time of oil type switching, and a design process. A pre-preparation step for preparing in advance for accepting the crude oil after switching based on the water content of the crude oil after switching or the flow rate, a switching step for starting acceptance of the crude oil after switching, and the crude oil after switching. An adjustment step that adjusts the operating conditions for distilling, and an acquisition unit that acquires information necessary for advancing each step included in the operating method when the operating method including the operation method is executed.
A presentation unit that presents the information acquired by the acquisition unit, and a presentation unit.
A support device characterized by being provided with. In the adjustment step, a state value acquisition unit that acquires a state value indicating the state of the device, and a state value acquisition unit.
Based on the state value, a calculation unit that calculates a recommended value of a control amount for controlling the device using a policy learned by machine learning, and a calculation unit.
An output unit that presents the calculated recommended value to the operator or sets the calculated recommended value as the target set value of the control amount in the device.
9. The support device according to claim 9. The measures include the time required to reach a predetermined operating state after switching the oil type of the crude oil to be processed, the yields of a plurality of fractions obtained by distilling the crude oil, and the energy consumed in the adjustment step. Learned by reinforcement learning using at least a reward value based on the quantity, the degree of satisfaction of the operating conditions required in the adjustment process, and the operator's evaluation of the operating conditions in the adjustment process, or a combination thereof. The support device according to claim 10, wherein the support device is characterized by the above. A state value acquisition unit that acquires a state value indicating the state of the device for distilling crude oil, and a state value acquisition unit.
Based on the state value, a learning unit that learns a policy for calculating a recommended value of a control amount for controlling the device at the time of oil type switching by machine learning, and a learning unit.
A learning device characterized by being provided with. The learning unit describes the time required from switching the oil type of the crude oil to be processed until it reaches a predetermined operating state, the yields of a plurality of fractions obtained by distilling the crude oil, and the amount of energy consumed. 13. The learning device described in. A support device that assists in setting the operating conditions of the device for distilling crude oil to produce multiple fractions, and
A learning device that learns the measures used in the support device by machine learning,
With
The learning device is
An acquisition unit that acquires a state value indicating the state of the device, and
Based on the state value, a learning unit that learns a policy for calculating a recommended value of a control amount for controlling the device at the time of oil type switching by machine learning, and a learning unit.
With
The support device is
A state value acquisition unit that acquires a state value indicating the state of the device when the oil type is switched, and a state value acquisition unit.
A calculation unit that calculates a recommended value of a control amount for controlling the device by using the policy learned by the learning device based on the state value.
An output unit that presents the calculated recommended value to the operator or sets the calculated recommended value as the target set value of the control amount in the device.
A refinery operating condition setting support system characterized by being equipped with.

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