CN112183811A - Multi-cycle plan scheduling collaborative scheduling optimization method and system for refinery plant - Google Patents
Multi-cycle plan scheduling collaborative scheduling optimization method and system for refinery plant Download PDFInfo
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Abstract
The invention relates to the field of industrial scheduling, in particular to a multi-cycle scheduling collaborative scheduling optimization method and a multi-cycle scheduling collaborative scheduling optimization system for a refinery, which comprises the steps of establishing a device scheduling optimization model by taking device scheduling optimization constraint conditions into consideration and taking benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model; considering crude oil ratio optimization constraint conditions, establishing a crude oil ratio optimization model with the benefit maximization as a target, and obtaining a crude oil ratio scheme according to the crude oil ratio optimization model; calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme; and generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme. By using the method and the system, stable and safe production of enterprises is guaranteed, meanwhile, the benefit is effectively improved, and the performability of the plan scheduling collaborative scheduling optimization scheme is improved.
Description
Technical Field
The invention relates to the field of industrial scheduling, in particular to a multi-cycle plan scheduling collaborative scheduling optimization method and system for a refinery.
Background
The production planning and scheduling are not only one of the important links of the production and management of the enterprise, but also effective bases of production decisions, and the economic benefits of the enterprise are determined by the quality of the planning and scheduling scheme. The monthly production plan is mainly to make a processing plan of raw materials according to a monthly raw material purchasing plan, a product sales plan and a product sales estimated price, and reasonably arrange the processing amount of the device, the operation days, the product structure and the flow direction.
At present, planned scheduling of petrochemical oil refining enterprises usually uses single-cycle optimization, and a crude oil proportioning scheme and a device processing scheme are optimized by taking one month as a scheduling interval; or optimizing a processing scheme with maximized benefit by taking the day as a unit, and multiplying the processing scheme by a coefficient to be used as a monthly production plan; or directly optimizing and calculating the total processing amount under normal pressure and reduced pressure and the properties of the mixed oil without considering the crude oil proportion optimization. The lack of consideration for the arrival plan of crude oils, using only a rough crude oil inventory as a constraint, inevitably results in a disjointed monthly production plan from the actual production situation. In addition, the traditional model is too fixed to be constructed, so that the quick change of the support device during transformation or capacity expansion cannot be realized, and the maintenance workload is huge. The device yield model adopts a mode of specifying coefficients, so that the influence of operation parameters on the side line yield of the device is difficult to represent, and particularly, the difference is obvious under different operation schemes like that an ethylene device is greatly influenced by operation conditions.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-cycle planning and scheduling collaborative production optimization method and system for a refinery plant.
A multi-cycle planning and scheduling collaborative scheduling optimization method for a refinery plant comprises the following steps:
considering device scheduling optimization constraint conditions, establishing a device scheduling optimization model with the benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model;
considering crude oil ratio optimization constraint conditions, establishing a crude oil ratio optimization model with the benefit maximization as a target, and obtaining a crude oil ratio scheme according to the crude oil ratio optimization model;
calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme;
and generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
Preferably, the establishing a device scheduling optimization model with consideration of the device scheduling optimization constraint condition and with the goal of maximizing the benefit includes:
establishing an objective function:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)，
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the price of the mixed crude oil;
the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, intermediate tank storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
Preferably, the step of establishing the crude oil blending ratio optimization model by considering the crude oil blending ratio optimization constraint condition and taking the benefit maximization as the target comprises the following steps:
establishing an objective function:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
the crude oil proportioning optimization constraints comprise one or more of crude oil inventory constraints, mixed crude oil property constraints, total proportion constraints and crude oil variety number constraints which are totally involved in processing.
Preferably, the calculating the sustainable time of the crude oil proportioning plan according to the crude oil proportioning plan comprises:
calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory;
recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species;
searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event;
if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events;
and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
Preferably, the method further comprises the following steps:
constructing a crude oil formula library according to historical production data;
and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
A multi-cycle planned-scheduling collaborative scheduling optimization system for a refinery, comprising:
the first model establishing unit is used for establishing a device scheduling optimization model by taking the device scheduling optimization constraint condition into consideration and taking benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model;
the second model establishing unit is used for considering the crude oil proportion optimization constraint condition, establishing a crude oil proportion optimization model with the benefit maximization as the target, and obtaining a crude oil proportion scheme according to the crude oil proportion optimization model;
the time calculation unit is used for calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme;
and the scheme generating module is used for generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
Preferably, the first model building unit includes:
a first function establishing module, configured to establish an objective function:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)，
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the price of the mixed crude oil;
the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, intermediate tank storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
Preferably, the second model building unit includes:
a second function establishing module, configured to establish an objective function:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
the crude oil proportioning optimization constraints comprise one or more of crude oil inventory constraints, mixed crude oil property constraints, total proportion constraints and crude oil variety number constraints which are totally involved in processing.
Preferably, the time calculating unit is specifically configured to:
calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory;
recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species;
searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event;
if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events;
and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
Preferably, the method further comprises the following steps:
the crude oil formula library construction module is used for constructing a crude oil formula library according to historical production data;
and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
By using the present invention, the following effects can be achieved:
1. the method comprises the steps of establishing a device scheduling optimization model by taking device scheduling optimization constraint conditions into consideration and taking benefit maximization as a target, obtaining a device scheduling scheme according to the device scheduling optimization model, establishing a crude oil proportioning optimization model by taking crude oil proportioning optimization constraint conditions into consideration and taking benefit maximization as a target, obtaining a crude oil proportioning scheme according to the crude oil proportioning optimization model, calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme, generating a multi-cycle plan scheduling coordinated production scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme, and ensuring stable and safe production of enterprises and effectively improving benefit;
2. the influence of the crude oil arrival plan on the crude oil inventory is considered, the crude oil proportioning optimization, the storage tank discharging amount optimization and the material flow direction structure optimization in each period can be carried out, and the performability of the plan scheduling and collaborative scheduling optimization scheme is improved.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a schematic flow chart of a multi-cycle scheduling collaborative scheduling optimization method for a refinery plant according to an embodiment of the present invention;
FIG. 2 is a schematic flowchart of step S3 of the multi-cycle scheduling collaborative scheduling optimization method for a refinery according to an embodiment of the present invention;
FIG. 3 is a schematic flowchart of step S5 of the multi-cycle scheduling collaborative scheduling optimization method for a refinery according to an embodiment of the present invention;
FIG. 4 is a schematic flowchart of steps S501-S502 of a multi-cycle scheduling collaborative scheduling optimization method for a refinery according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a multi-cycle scheduling collaborative scheduling optimization system for a refinery according to an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a multi-cycle scheduling collaborative scheduling optimization system for a refinery according to an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a multi-cycle scheduling collaborative scheduling optimization system for a refinery according to another embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be further described below with reference to the accompanying drawings, but the present invention is not limited to these embodiments.
The basic idea of the invention is to consider the device scheduling optimization constraint condition and establish a device scheduling optimization model with the benefit maximization as the target, obtain a device scheduling scheme according to the device scheduling optimization model, consider the crude oil proportioning optimization constraint condition and establish a crude oil proportioning optimization model with the benefit maximization as the target, obtain a crude oil proportioning scheme according to the crude oil proportioning optimization model, calculate the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme, generate a multi-cycle planning scheduling coordinated production scheduling optimization scheme according to the sustainable time of the device scheduling scheme, the crude oil proportioning scheme and the crude oil proportioning scheme, and effectively improve the benefit while ensuring stable and safe production of enterprises.
Based on the above thought, an embodiment of the present invention provides a multi-cycle planning and scheduling collaborative scheduling optimization method for a refinery, as shown in fig. 1, including the following steps:
s1: and (4) considering the device scheduling optimization constraint condition, establishing a device scheduling optimization model with the benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model.
The device scheduling optimization model takes the properties of mixed crude oil, the atmospheric and vacuum processing capacity, the feeding and discharging flow of each device as variables, the device processing capacity, the product yield, the balance of feeding and discharging materials, the intermediate material inventory and the like as constraints, the benefit is maximized as a target, and the optimal processing mixed oil properties, the atmospheric and vacuum processing capacity and the device processing scheme under the current production condition are solved.
Specifically, an objective function is established:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the mixed crude price.
Wherein the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, tundish storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
And (3) balance constraint of equipment feeding and discharging:
in the formula, the spliterOut (i, j) is the flow rate of a discharge flow j of a splitter i; the flow rate of a feed stream j of the splitter i is the spliterIn (i, j); deltai is the balance allowance error of the material inlet and outlet of the flow divider; deltaj is the mixer feed and discharge balance tolerance.
And (3) yield constraint:
in the formula, cduOut (i, j) is the flow rate of a discharge flow j of an atmospheric pressure and vacuum pressure i; cduIn (i, j) is the flow rate of a feed stream j at the atmospheric pressure and the vacuum pressure i; deltak is the allowable error of the normal and reduced pressure side line yield; deltal is the allowable error of the secondary device side line yield; unitIn (i, j) is the flow of a feed stream j of a secondary plant i; unitOut (i, j) is the flow of a discharge stream j of the secondary device i; cduYield (i, j) is the yield of a discharge stream j of the atmospheric and vacuum pressure i, and is obtained by solving an established atmospheric and vacuum pressure yield data model; and the unitYield (i, j) is the yield of the discharge stream j of the secondary plant i and is obtained by solving the established secondary plant yield data model.
The establishment of the device yield data model comprises the following steps:
firstly, according to the actual production process flow of a refinery, a logical model of the production flow is constructed, primitives such as materials, devices and the like are respectively encoded, and refinery stream flow direction table data and a primitive encoding data set are formed. Each primitive and material on the production flow graph is encoded to ensure uniqueness and form a primitive-encoded data set. The primitives are distinguished by adopting the following categories: atmospheric and vacuum device, secondary device, node, storage tank, shunt, blender, the letter is increased respectively at the beginning of the code that corresponds: C. u, N, T, S, M are provided.
On the basis of the production logic representation of the last step, by means of an artificial intelligence and big data modeling method, factors such as the feeding property of the device, the operating conditions and the like are comprehensively considered, and a data-driven lateral line yield model and a property model are constructed for the device type representation.
The method comprises the following steps of firstly, collecting the feeding flow, feeding property and main operating conditions of a device, and corresponding device side line yield and side line property data, and constructing an input and output data matrix: x ═ feed ratio, feed properties, operating conditions ], Y1 ═ side draw yield, Y2 ═ side draw properties.
In the second step, because the production data directly obtained from the historical database is mostly incomplete and inconsistent dirty data, data mining cannot be directly carried out, or the mining result is not satisfactory. In order to improve the quality of data mining, preliminary preprocessing needs to be performed on the constructed modeling data, including missing value processing, abnormal value processing, input data normalization and the like, and the processed input and output data are Xnew, Y1new and Y2 new.
Step three, establishing a device yield data model:
according to input and output data after data preprocessing, artificial intelligent algorithms such as random forests, neural networks, Gaussian process regression and support vector machines are utilized to train the yield and property models of the device, modeling effects are compared, and the optimal model is selected for storage.
Device processing capacity constraint:
wherein unitFeedUp (i) is the upper limit of the processing capacity of the secondary device i; unitFeedLow (i) is the lower limit of the processing capacity of the secondary device i.
And (4) storage and restraint of the intermediate tank:
tankIn(i)-tankOut(i)≤htUp(i)-ht0(i)
-tankIn(i)+tankOut(i)≤ht0(i)-htLow(i)
wherein, tankIn (i) is the feed flow rate of the storage tank i; tankOut (i) is the discharge flow of the storage tank i; htUp (i) is the upper limit of the tank of tank i; htLow (i) is the lower inventory limit of tank i; ht0(i) is the initial tank quantity for tank i.
Product yield constraint:
productMatrix*m≤productLimitUp
-productMatrix*m≤-productLimitLow
in the formula, the product matrix is a product matrix; the product LimitUp is a product yield upper limit matrix; the productLimitLow is a product yield lower limit matrix.
Constraint of material properties:
propertyMatrix(i,:)'≤propertyConstraintUp(i,:)'
-propertyMatrix(i,:)'≤-propertyConstraintLow(i,:)'
in the formula, propertyMatrix is a flow property matrix, and each row corresponds to one property; propertyconstraintUp is an upper limit matrix of stream properties, and each row corresponds to one property; propertyConstraintLow is a lower limit matrix of stream properties, one property for each row.
And (3) production and consumption constraint of public works:
unituUtility(i,j)≤unituUtilityUp(i,j)
-unituUtility(i,j)≤-unituUtilityLow(i,j)
wherein unity utility (i, j) is the output/consumption of the jth utility of the ith secondary plant;
the unitutilityUp (i, j) is the upper limit of the yield/consumption of the jth public work of the ith secondary device;
unitutilitylow (i, j) is the lower limit of the production/consumption of the jth utility of the ith secondary plant.
S2: and (4) considering the crude oil ratio optimization constraint condition, establishing a crude oil ratio optimization model with the benefit maximization as a target, and obtaining a crude oil ratio scheme according to the crude oil ratio optimization model.
The crude oil proportioning optimization model takes crude oil proportion as variable, current crude oil stock, mixed crude oil property and the like as constraints, and the optimal crude oil proportioning scheme under the current production condition is solved with the aim of maximizing benefit.
Specifically, an objective function is established:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
wherein the crude oil proportioning optimization constraint conditions comprise one or more of crude oil inventory constraint, mixed crude oil property constraint, total proportion constraint and crude oil variety number constraint which is totally involved in processing
Crude oil inventory constraints:
diag(ones(1,numOfCrude)).×Xm1×x≤crudeStock-crudeStockLow
wherein numOfCrude is the number of crude oil types; the crudEstock is a crude oil current inventory matrix; crudes stock low is the crude oil lower limit matrix.
Hybrid crude property constraints:
crudePro×x≤opUp
-crudePro×x≤-opLow
wherein, the crudePro is a crude oil property matrix; opp is the upper limit of the properties of the mixed crude oil; (ii) a opLow is the mixed crude property lower limit.
Constraint of total ratio
∑x≤1+ratioTol
-∑x≤-(1-ratioTol)
Wherein, the ratioTol is the total crude oil proportion tolerance.
The number of crude oil types which are always involved in processing is constrained:
∑(x>minRatioTol)≤maxNumOfOil
-∑(x>minRatioTol)≤-minNumOfOil
wherein minratiitol is the minimum ratio tolerance; maxNumOfOil is the maximum value of the number of crude oil types participating in processing; minNumOfOil is the minimum number of crude oil species participating in processing.
S3: and calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme.
And calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportion calculated by the crude oil proportioning optimization model in the last step, the crude oil arrival plan in a future period of time, the current crude oil inventory and other known information.
As shown in fig. 2, step S3 specifically includes the following steps:
s301: calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory;
s302: recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species;
s303: searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event;
s304: if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events;
s305: and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
The influence of the crude oil arrival plan on the crude oil inventory is further considered through the step S3, the crude oil proportioning optimization, the storage tank discharging amount optimization and the material flow direction structure optimization of each period can be carried out, and the performability of the plan scheduling collaborative scheduling optimization scheme is improved.
S4: and generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
The plan scheduling collaborative scheduling optimization scheme comprises a device scheduling scheme, a crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme, and the model solving efficiency and the result feasibility are improved.
And transmitting the crude oil proportioning scheme of the first short period and the corresponding device processing scheme to the downstream to carry out crude oil scheduling and scheduling, updating the intermediate tank stored data, the crude oil stockroom stored data and the crude oil arrival event information according to the actual crude oil scheduling and scheduling scheme, taking the updated intermediate tank stored data, the crude oil stockroom stored data and the crude oil arrival event information as known conditions for planning and optimizing in the previous step, and updating the planned scheduling of the next period in a rolling manner.
In some embodiments, as shown in fig. 3, the method further comprises the steps of:
s5: constructing a crude oil formula library according to historical production data; and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
Each refinery accumulates a unique set of empirical crude oil formulations, taking into account plant characteristics, refinery geographical location, product characteristics, etc. According to expert knowledge and historical production data, an empirical crude oil formula library is constructed, powerful expert experience guarantee can be provided for solving a plan optimization scheduling model, and the condition that no feasible solution exists is effectively avoided.
As shown in fig. 4, the crude oil formulation library is constructed as follows:
s501: an initial crude oil formulation library is constructed based on expert experience.
And (4) quickly constructing initial data of the crude oil formula library according to expert experience. The crude oil formulation library data table field comprises: atmospheric and vacuum names, crude oil proportions and creation time.
S502: a library of supplemental crude oil formulations is mined from historical production data.
The crude oil database constructed according to expert experience inevitably omits some crude oil proportioning schemes. And collecting crude oil processing historical information data, searching a crude oil proportioning scheme from the historical data based on a clustering algorithm, comparing the crude oil proportioning scheme with a crude oil formula library constructed by expert experience, screening the crude oil formula scheme which is not established to the crude oil formula library, and supplementing the crude oil formula library with rationality after the crude oil formula scheme is confirmed by the expert.
In addition, the established crude oil formula library can be continuously expanded and updated along with the production, and has better adaptability.
Based on the above embodiment, the disclosed method for optimizing multi-cycle scheduling collaborative scheduling of a refinery correspondingly discloses a system for optimizing multi-cycle scheduling collaborative scheduling of a refinery, as shown in fig. 5, including:
the first model establishing unit is used for establishing a device scheduling optimization model by taking the device scheduling optimization constraint condition into consideration and taking benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model;
the second model establishing unit is used for considering the crude oil proportion optimization constraint condition, establishing a crude oil proportion optimization model with the benefit maximization as the target, and obtaining a crude oil proportion scheme according to the crude oil proportion optimization model;
the time calculation unit is used for calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme;
and the scheme generating module is used for generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
As shown in fig. 6, the first model building unit includes:
a first function establishing module, configured to establish an objective function:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)，
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the price of the mixed crude oil;
the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, intermediate tank storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
As shown in fig. 6, the second model building unit includes:
a second function establishing module, configured to establish an objective function:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
the crude oil proportioning optimization constraints comprise one or more of crude oil inventory constraints, mixed crude oil property constraints, total proportion constraints and crude oil variety number constraints which are totally involved in processing.
The time calculation unit is specifically configured to: calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory; recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species; searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event; if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events; and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
In some embodiments, as shown in fig. 7, the present system further comprises:
the crude oil formula library construction module is used for constructing a crude oil formula library according to historical production data; and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
It can be understood that the multi-cycle scheduling collaborative scheduling optimization system for a refinery provided by the embodiment of the present invention can execute the multi-cycle scheduling collaborative scheduling optimization method for a refinery provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.
Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A multi-cycle planning scheduling collaborative scheduling optimization method for a refinery is characterized by comprising the following steps:
considering device scheduling optimization constraint conditions, establishing a device scheduling optimization model with the benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model;
considering crude oil ratio optimization constraint conditions, establishing a crude oil ratio optimization model with the benefit maximization as a target, and obtaining a crude oil ratio scheme according to the crude oil ratio optimization model;
calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme;
and generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
2. The method of claim 1, wherein the establishing a model of plant scheduling optimization with the objective of maximizing profit in consideration of plant scheduling optimization constraints comprises:
establishing an objective function:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)，
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the price of the mixed crude oil;
the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, intermediate tank storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
3. The method of claim 1, wherein the creating a crude oil proportioning optimization model with consideration of crude oil proportioning optimization constraints and with the goal of maximizing profit comprises:
establishing an objective function:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
the crude oil proportioning optimization constraints comprise one or more of crude oil inventory constraints, mixed crude oil property constraints, total proportion constraints and crude oil variety number constraints which are totally involved in processing.
4. The method as claimed in claim 1, wherein the calculating the sustainable time of the crude oil blending plan according to the crude oil blending plan comprises:
calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory;
recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species;
searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event;
if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events;
and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
5. The multi-cycle planning and scheduling collaborative production optimization method for the refinery according to any one of claims 1 to 4, further comprising:
constructing a crude oil formula library according to historical production data;
and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
6. A multi-cycle schedule collaborative scheduling optimization system for a refinery, comprising:
the first model establishing unit is used for establishing a device scheduling optimization model by taking the device scheduling optimization constraint condition into consideration and taking benefit maximization as a target, and obtaining a device scheduling scheme according to the device scheduling optimization model;
the second model establishing unit is used for considering the crude oil proportion optimization constraint condition, establishing a crude oil proportion optimization model with the benefit maximization as the target, and obtaining a crude oil proportion scheme according to the crude oil proportion optimization model;
the time calculation unit is used for calculating the sustainable time of the crude oil proportioning scheme according to the crude oil proportioning scheme;
and the scheme generating module is used for generating a multi-period plan scheduling collaborative scheduling optimization scheme according to the device scheduling scheme, the crude oil proportioning scheme and the sustainable time of the crude oil proportioning scheme.
7. The multi-cycle planned scheduling collaborative scheduling optimization system for a refinery of claim 6, wherein the first model building unit comprises:
a first function establishing module, configured to establish an objective function:
y＝-(priceCoefficient×m-sumOfCrude×crudePrice)，
in the formula, priceCoefficient is a price coefficient matrix of variables; m is a variable; sumOfCrude is the total amount of crude oil processing; crudePrice is the price of the mixed crude oil;
the device scheduling optimization constraint conditions comprise one or more of equipment feeding and discharging balance constraint, yield constraint, device processing capacity constraint, intermediate tank storage constraint, product yield constraint, material property constraint and utility production and consumption constraint.
8. The multi-cycle planned scheduling collaborative scheduling optimization system for a refinery of claim 6, wherein the second model building unit comprises:
a second function establishing module, configured to establish an objective function:
y＝x×crudePrice.×Xm1，
wherein, the crudePricde is a price matrix of the crude oil; x is a variable, namely the crude oil proportion; xm1 is the total crude oil processed;
the crude oil proportioning optimization constraints comprise one or more of crude oil inventory constraints, mixed crude oil property constraints, total proportion constraints and crude oil variety number constraints which are totally involved in processing.
9. The multi-cycle planned scheduling collaborative scheduling optimization system for a refinery of claim 6, wherein the time calculation unit is specifically configured to:
calculating the available time of each kind of oil according to the crude oil proportioning scheme and the current crude oil inventory;
recording the minimum time of the properties of the oil as an initial value of available time, and recording the corresponding oil species as key oil species;
searching the earliest available arrival event which is firstly closed to the key oil seeds, and calculating the remaining available voyage event;
if the earliest available arrival event is not empty, updating the inventory and voyage events, and outputting the available time of the crude oil proportion, the crude oil inventory processed according to the proportion and the remaining voyage events;
and if the earliest available arrival event is not empty, dividing the crude oil amount in the arrival event on a crude oil inventory according to a time proportion to update the crude oil inventory, recalculating the available time length of the crude oil, the key oil type, searching the related earliest available arrival event, updating the inventory and the voyage event, and outputting the available time length of the crude oil proportion, the crude oil inventory processed according to the proportion and the residual voyage event.
10. The multi-cycle planning and scheduling collaborative production optimization system for the refinery according to any one of claims 6 to 9, further comprising:
the crude oil formula library construction module is used for constructing a crude oil formula library according to historical production data;
and when the crude oil proportioning scheme cannot be obtained according to the crude oil proportioning optimization model, taking the formula which meets the conditions and has the minimum cost in the crude oil formula library as the crude oil proportioning scheme.
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