CN102768702A - Oil refining production process schedule optimization modeling method on basis of integrated control optimization - Google Patents

Abstract

The invention provides an oil refining production process schedule optimization modeling method on the basis of integrated control optimization, and the method includes steps as follows: step A, initializing an upper computer; step B, through optimization operation mode classification and statistic analysis on operation data of all devices under advanced control, obtaining statistic models under different optimization operation modes achieved by all devices under the advanced control; and step C, during the schedule optimization operation process, after obtaining large amount of operation data under all optimization operation modes, carrying out data-based on-line correction on a yield model, an energy consumption model and a performance index model of a device. The solution of the invention can efficiently solve the application problems faced by an oil refining enterprise during schedule optimization model production and advanced control implementation under the condition that reaction raw material variation and operation fluctuation are difficult to obtain.

Description

Oil refining production run optimizing scheduling modeling method based on integrated Control and Optimization

Technical field

The invention belongs to process industry optimizing scheduling field of intelligent control technology, particularly a kind of oil refining production run optimizing scheduling model and implementation method.

Background technology

Progressively raising along with the oil refining enterprise automaticity; The enforcement of advanced control and integrated optimization technology; The opportunity of oil refining enterprise optimizing scheduling is increasingly mature and put on the agenda next; Each big technical service company of the world also has dispatcher software to release; But because the oil refining enterprise scheduling problem has process complicacy, operator scheme control and many particular difficulty that are different from the scheduling of general enterprise such as modeling is difficult, the mode switch cost is big, uncertainty; Up to the present also do not have and to describe and find the solution, carry into execution a plan for oil refining enterprise scheduling provides comparatively feasible scheduling, cause the production scheduling of oil refining enterprise still to be dispatched, also exist the very large potentiality of taping the latent power with artificial experience.The present crude capacity of China has reached 500,000,000 tons, and annual oil refining enterprise inside will used up crude resources more than 3,000 ten thousand tons, is higher than the output in 1 year of China's second largest oil field Shengli Oil Field.(present domestic refining crude oil energy consumption per ton reaches 70-95 kilogram mark oil apparently higher than international most advanced level for China petroleum refining industry energy consumption, material consumption; Be merely 53.2 kilograms of mark oil/tons and refine oil the advanced level of comprehensive energy consumption abroad); Therefore the product oil rate also is starkly lower than international most advanced level; The potentiality of taping the latent power are big, promptly can improve product yield through the oil refining Optimization of Production Dispatching, by the annual 500000000 tons of crude charging capacities in the present whole nation; The direct economic benefit highly significant has huge economic benefit and social benefit at the discharging that reduces CO2 and other harmful objects, increase high-value product yield and aspects such as quality, raising safety in production level simultaneously equally.Therefore propose oil refining and produced the new way that optimizing scheduling is found the solution and realized, can realize effectively that the total solution of optimizing scheduling just seems necessary, and have a extensive future.

The optimizing scheduling modeling problem of oil refining enterprise is different with the optimizing scheduling modeling of general enterprise; The particular difficulty that the following aspects is arranged specifically: the charging crude oil that (1) is different; Its various product oil product yields, oil product label, sulfur content, content of beary metal etc. and processing cost, energy consumption are different, promptly can not describe the oil refining production run with a single scheduling model; (2) even charging crude oil is constant; Because the production and processing scheme is different; Then various product oil yields, oil product label, sulfur content, content of beary metal etc. are also different with processing cost, energy consumption; Particularly not steady in the process units operation, in the time of can not carrying out according to the desired operation scheme of scheduling scheme, then scheduling model is difficult to adapt to this uncertainty.

These specific questions make existing scheduling model be difficult to play a role; The single model of optimizing scheduling use at present is difficult to accurately reflect that the process of multiple charging crude oil and multiple installation optimization operator scheme is actual; Even the multi-model scheme that some document proposes also is difficult to reflect realistic model; Be difficult to describe the actual conditions of process units with limited many models; Therefore the error brought of model mismatch causes to optimize and finds the solution scheme and lose Practical significance; Do not have production run device level advanced person to control and ensure process units, thereby the scheduling model of enough limited the corresponding optimized operation mode of process units ability is described, can't be implemented for oil refining production run optimizing scheduling according to the needed Optimizing operation scheme of scheduling realization with edge optimization.

About the modeling of oil refining production run optimizing scheduling, some researchs and application result have been arranged, but all being difficult to adapt to yield, character index and the operation cost that charging crude oil changes and device is operated under the fluctuation, the optimizing scheduling model that these research institutes provide changes.Some declarations at present have the oil refining enterprise dispatcher software of Optimization Dispatching function because model problem (because multiple charging crude oil and incomplete controlled operation operating mode; Can not accurately reflect the production run characteristic with single scheduling model) in fact can't realize optimizing scheduling, deal with problems far away in research aspect the optimizing scheduling modeling at present.

Summary of the invention

The technical matters that (one) will solve

The objective of the invention is based on installing grade advanced Optimizing operation scheme that can ensure process units realization scheduling appointment with optimization of controlling; Provide a kind of towards many optimized operation mode describing framework that oil property changes and optimized operation mode is switched, based on the oil refining production run method for optimizing scheduling of many optimized operation mode model description.

(2) technical scheme

In order to address the above problem, the invention provides a kind of oil refining production run method for optimizing scheduling based on Control and Optimization, comprising: steps A: the host computer initialization; Wherein, in said host computer, set up with lower module: the off-line modeling module, at line model correcting module, expert decision-making reasoning module, optimize and find the solution module, Optimization Model generation module and database support module, wherein; The off-line modeling module; Through said database support module, acquisition need be carried out the interior device information of flow process of modeling statistics, and the apparatus for initializing model data; At the line model correcting module; Through said database support module, obtain off-line modeling module gained device optimizing scheduling model, and be initialized as online modified basis model; Step B: said off-line modeling module, be optimized operator scheme classification and statistical study through the service data of the advanced person being controlled under the enforcement of respectively installing, obtain each device statistical model under the accessible Different Optimization operator scheme under the advanced person controls; Step C: said at the line model correcting module, in the optimizing scheduling operational process, after a large amount of service datas under having obtained each optimized operation mode, plant yield model, energy consumption model and performance index model are carried out the online correction based on data.

Preferably, step B comprises: step B1: through reading database, obtain to treat the device information of modeling, and each mounted cast parameter of initialization; Step B2: through said database support module, obtain advanced control implement down respectively install service data, data are carried out pre-service, the production run data under the optimized operation mode are stablized in selection; Step B3: the stable state production run data to step B2 obtains are added up and classification analysis, obtain yield model, energy consumption model and performance index model under the accessible Different Optimization operator scheme of the advanced control of each device; Step B4: plant yield model, operation cost model and character index TRANSFER MODEL that step B3 obtains are write the scheduling model database.

Preferably; Step B3 comprises: step B3.1: for the time processing device; According to setting up based on the yield multi-model under limited the optimized operation mode of advanced person's control, i.e. YIELD based on each the side line cutting temperature under limited the optimized operation mode of advanced person's control and the incision principle that dangles _{S, u, c, m}=f _Frac(TBP _c, IBP _{S+1, u, c, m}, EP _{S-1, u, c, m}, α _{S, u, c, m}, α _{S-1, u, c, m}), in the formula, s is the device logistics, is gasoline, boat coal, light bavin, heavy bavin, residual oil for time processing device value, and m is the installation optimization operator scheme, and its value is gasoline pattern, diesel fuel mode, total light receipts pattern, TBP _cBe the true boiling point distillation data of crude oil c, its data layout is { [TI _{C, comp}TE _{C, comp}), D _{C, comp}, TI here _{C, comp}, TE _{C, comp}Be respectively initial temperature and the final temperature of the true boiling point close-boiling cut comp of crude oil c, D _{C, comp}Be the yield value of the close-boiling cut comp of crude oil c, IBP _{S+1, u, c, m}, EP _{S-1, u, c, m}Be respectively device u at the end point of distillation of the initial boiling point of charging crude oil c and m optimized operation mode bottom discharge s+1 and s-1 or do α _{S, u, c, m}, α _{S-1, u, c, m}Be respectively device u logistics s and the overlapping component of s+1 partition factor under charging crude oil c and m optimized operation mode, YIELD to the partition factor of logistics s and logistics s and the overlapping component of s-1 to logistics s-1 _{S, u, c, m}Be the yield value of device u logistics s under charging crude oil c and m optimized operation mode, for atmospheric vacuum distillation process, the performance index TRANSFER MODEL adopts following mode to represent: PRO _{S, u, p}=PRO _{S ', u, p}* ProModel _{S ', s, u, m, p}, in the formula, p is the character index, value is sulfur content, heavy metal such as iron, manganese content, octane value, cetane rating, PRO _{S, u, p}Be the p character desired value of the logistics s of u device, PRO _{S ＇, u, p}Be the value of feed stream s ' character index p of device u, ProModel _{S ', s, u, m, p}Be device u p performance index transfer coefficient from charging s ' to discharging s under optimized operation mode m, operation cost unit consumption hypothesis is fixed value, i.e. OpCost for the fixing operation cost unit consumption of crude oil and a certain optimized operation mode _{U, c, m}=const, in the formula, OpCost _{U, c, m}Be the unit operation expense of device u under crude oil c and m optimized operation mode; Step B3.2: for the secondary reaction device; According to the service data under limited a plurality of optimized operation mode of advanced person's control and integrated optimization; Investigate of the influence of Different Optimization operator scheme,, at first the yield of the product stream s under certain device u and the certain optimisation operator scheme m is taken fixed value for the yield model to model; Variation for feed composition adds compensation again, promptly ${\mathrm{YIELD}}_{s,u,c,m}={\mathrm{YIELD}}_{s,u,m}^0+{\mathrm{\Δ\; YIELD}}_{s,u,c,m},$ In the formula,

The basic yield value of discharging logistics s under the m optimized operation mode for device u is the fixed value relevant with optimized operation mode, Δ YIELD _{S, u, c, m}For installing u at the yield offset that is directed against product stream s under the feed variation under the m optimized operation mode; Character index and raw material composition, operating conditions and install closely related; In the optimizing scheduling model, to content class character index, it is relevant with following form with optimized operation mode: PRO _{S, u, m, p}=PRO _{S ', u, p}* ProModel _{S ', s, u, m, p}, for such as Attribute class character indexs such as octane number and condensation point of diesel oil, consider that optimized operation mode influences it, be characterized by following form: ${\mathrm{PRO}}_{s,u,m,p}={\mathrm{PRO}}_{s,u,p}^0+{\mathrm{\Δ\; PRO}}_{s,u,m,p},$ In the formula, The basic value of s discharging logistics p character index for device u is fixed value; Δ PRO _{S, u, m, p}For the corresponding improvement amount of discharging logistics s p character index under the m optimized operation mode of device u, be the constant relevant with optimized operation mode m.The operation cost of secondary reaction device is fixed value, i.e. OpCost for a certain optimized operation mode _{U, m}=const; Step B3.3: for modifying apparatus; The continuous adjustment of operating conditions is very big to its product influence, and logistics yield and operation cost and character index improvement amount are closely related, control service data according to the advanced person; Set up the continuous model between modifying apparatus yield and the character index index, form is following: ${\mathrm{YIELD}}_{s,u,c}={\mathrm{YIELD}}_{s,u,c}^0+f_{\mathrm{HT}}\left({\mathrm{\Δ\; PRO}}_{s,u,p}\right),$ In the formula,

Be the discharging s of modifying apparatus u basic yield value when the flow processing crude oil c, Δ PRO _{S, u, p}Be the improvement amount of discharging s character index p of device u, f _HT(Δ PRO _{S, u, p}) be discharging s yield and the Δ PRO of modifying apparatus u _{S, u, p}Between funtcional relationship, according to the production run statistics, set up the mathematical model between modifying apparatus operation cost and the character index improvement amount, form is following: ${\mathrm{OpCost}}_u={\mathrm{OpCost}}_u^0+\mathrm{Exp}\left(f_u^c\left({\mathrm{\Δ\; PRO}}_{s,u,p}\right)\right),$ In the formula,

Be the fundamental operation expense of modifying apparatus u,

Be modifying apparatus u operation cost and character index improvement amount Δ PRO _{S, u, p}Between funtcional relationship, calculate for the character index of modifying apparatus, character index improvement amount is for treating excellent variable, its product property index calculating as follows:

In the formula,

Be the basic value of the product p character index of modifying apparatus u, Δ PRO _{U, p}Improvement amount for the product p character index of modifying apparatus u.

Preferably; Step C comprises: step C1: through said database support module; Read the yield model, energy consumption model and the performance index model that respectively install in the flow process under the current state under each optimized operation mode, obtain device and control the production run data under each optimized operation mode under the enforcement the advanced person; Step C2: the device production run data to step C1 gained are carried out pre-service, reject the dynamic process data, and carry out simple data check based on material balance and balance of properties equation, obtain the stable state production data; Step C3: model correction; Step C4: the device that step C3 is obtained has been revised yield model, energy consumption model and performance index model and has been write database.

Preferably, step C3 comprises: step C3.1: the correction model of treating that step C1 is obtained is converted into the form of least square form; Step C3.2: each device production run data under each optimized operation mode according under the steady state operation of step C2 acquisition are carried out the LEAST SQUARES MODELS FITTING correction based on data.

(3) beneficial effect

Solution of the present invention; Limited optimized operation mode according to advanced person's control and installation optimization; Obtain having the scheduling model under practical operation meaning and actual attainable limited the optimized operation mode of advanced control; Guaranteeing the optimizing scheduling model simultaneously accurately, the optimizing scheduling result has real exercisable advantage, can effectively solve to be difficult to accurately to obtain that reaction raw materials changes and the application difficult problem of operation fluctuation oil refining enterprise down in Optimization of Production Dispatching model and advanced control enforcement.

Description of drawings

With reference to the accompanying drawings and combine instance to further describe the present invention.Wherein:

Fig. 1 is the optimizing scheduling total solution synoptic diagram according to the embodiment of the invention.

Fig. 2 is the optimizing scheduling modeling method module relation diagram according to the embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

Practical exercisable oil refining production run method for optimizing scheduling based on intelligent decision according to the present invention comprises that expert intelligence Decision Inference, mathematical model generate and two parts are found the solution in optimization.Expert intelligence Decision Inference part; Based on expert's priori; Switch cost with optimized operation mode and be minimised as principle, product oil changes in demand and crude oil are changed, provide to satisfy to refinery idiographic flow structure and respectively install scientific and reasonable decision information under the product oil real needs; Instruct full factory optimizing scheduling, coordinate crude oil scheduling, product oil scheduling and device optimizing scheduling; Optimize and find the solution calculating section; Plant yield model, operation cost model and character index model under the optimized operation mode that the reasoning of acquisition expert decision-making provides; Generate mathematical model and call solver and carry out optimization and find the solution; Result of calculation feedback expert decision-making reasoning part provides feedback information to the adjustment of decision-making and reasoning.

Oil refining production run optimizing scheduling modeling method based on integrated Control and Optimization according to the present invention may further comprise the steps:

Steps A: host computer initialization:

In said host computer, set up with lower module: off-line modeling module, at line model correcting module and database support module, wherein:

The off-line modeling module, through said database support module, acquisition need be carried out the interior device information of flow process of modeling statistics, and the apparatus for initializing model data;

At the line model correcting module,, obtain off-line modeling module gained device optimizing scheduling model, and be initialized as online modified basis model through said database support module.

Step B: said off-line modeling module; Be to be optimized operator scheme classification and statistical study through the service data of the advanced person being controlled under implementing of respectively installing; Obtain each device statistical model under the accessible Different Optimization operator scheme under the advanced person controls, operation below carrying out successively:

Step B1: through reading database, obtain to treat the device information of modeling, and each mounted cast parameter of initialization;

Step B2: through said database support module, obtain advanced control implement down respectively install service data, data are carried out pre-service, the production run data under the optimized operation mode are stablized in selection;

Step B3: the stable state production run data to step B2 obtains are added up and classification analysis, obtain yield model, energy consumption model and performance index model under the accessible Different Optimization operator scheme of the advanced control of each device:

Step B3.1: (only crude oil is carried out the device of lock out operation for the time processing device; Be atmospheric vacuum distillation process); According to setting up based on the yield multi-model under limited the optimized operation mode of advanced person's control, promptly based on each the side line cutting temperature under limited the optimized operation mode of advanced person's control and the incision principle that dangles

YIELD _{s，u，c，m}=TR _{s，u，c，m}+PTR _{s，u，c，m}α _s,u，c，m+PTR _{s-1,u，c，m}(1-α _{s-1,u，c，m})

${\mathrm{TR}}_{s,u,c,m}=\underset{\mathrm{comp}}{\mathrm{\Σ}}{\mathrm{TRC}}_{s,u,c,m,\mathrm{comp}}{\mathrm{PTR}}_{s,u,c,m}=\underset{\mathrm{comp}}{\mathrm{\Σ}}{\mathrm{PTRC}}_{s,u,c,m,\mathrm{comp}}$

In the formula, TR _{S, u, c, m}Be the no true boiling point distillation lap yield value of device u at charging crude oil c and m optimized operation mode bottom discharge s;

PTR _{S, u, c, m}Be the yield value of device u at charging crude oil c and m optimized operation mode bottom discharge s and s+1 true boiling point distillation lap;

PTR _{S-1, u, c, m}Be the yield value of device u at charging crude oil c and m optimized operation mode bottom discharge s-1 and s true boiling point distillation lap;

α _{S, u, c, m}Be assigned to the partition factor of discharging s part for discharging s and the overlapping component of s+1 true boiling point distillation;

TRC _{S, u, c, m, comp}For device u in charging crude oil c and m optimized operation mode bottom discharge s yield value with the corresponding true boiling point close-boiling cut of adjacent discharging zero lap part comp;

PTRC _{S, u, c, m, comp}For device u in charging crude oil c and m optimized operation mode bottom discharge s yield value with the corresponding true boiling point close-boiling cut of s+1 lap comp;

PTRC _{S-1, u, c, m, comp}For device u in charging crude oil c and m optimized operation mode bottom discharge s yield value with the corresponding true boiling point close-boiling cut of s-1 lap comp;

Wherein, TRC _{S, u, c, m, comp}, PTR _{S, u, c, m}And PTR _{S-1, u, c, m}Computing method are the same, TRC _{S, u, c, m, comp}Computing method are following:

In the formula, TI _{C, comp}, TE _{C, comp}Be respectively initial temperature and the final temperature of the true boiling point close-boiling cut comp of crude oil c;

IBP _{S+1, u, c, m}, EP _{S-1, u, c, m}Be respectively device u at the initial boiling point of charging crude oil c and m optimized operation mode bottom discharge s+1 and the end point of distillation (or doing) of s-1;

D _{C, comp}Yield value for the close-boiling cut comp of crude oil c.

For atmospheric vacuum distillation process, the transfer law of performance index is clearer and more definite, and like sulphur or content of beary metal, most of meeting gets into long residuum.Its performance index TRANSFER MODEL adopts following mode to represent:

PRO _s，u,p=PRO _s′，u,p×ProModel _{s，u，p，m}

In the formula, ProModel _{S, u, p, m}Be the transfer coefficient of device u at the performance index p of optimized operation mode m bottom discharge s;

Consider for simplifying that operation cost (containing energy consumption) unit consumption hypothesis is a fixed value for the fixing operation cost of crude oil and a certain optimized operation mode (containing energy consumption) unit consumption, promptly

OpCost _u,c,m=const

In the formula, OpCost _{U, c, m}Be the unit operation expense of device u under crude oil c and m optimized operation mode.

Step B3.2: for the secondary reaction device; Like catalytic cracking, hydrocracking, delayed coking etc.; According to the service data under limited a plurality of optimized operation mode of advanced person's control and integrated optimization; Investigate of the influence of Different Optimization operator scheme to model (yield, character index and operation cost model).

For the yield model, at first yield of the product stream s under certain device u and certain optimisation operator scheme m is taken fixed value, add compensation again for the variation of feed composition, promptly

${\mathrm{YIELD}}_{s,u,c,m}={\mathrm{YIELD}}_{s,u,m}^0+{\mathrm{\ΔYIELD}}_{s,u,c,m}$

In the formula;

is the basic yield value of discharging logistics s under the m optimized operation mode of device u, is the fixed value relevant with optimized operation mode;

Δ YIELD _{S, u, c, m}For installing u at the yield offset that is directed against product stream s under the feed variation under the m optimized operation mode.

The character index, like sulfur content, content of beary metal, the octane value of gasoline and the condensation point of diesel oil etc. and raw material composition, operating conditions and install closely relatedly, in the optimizing scheduling model, to content class character index, it is relevant with following form with optimized operation mode:

PRO _{s，u，m，p}=PRO _s′，u,p×ProModel _{s′，s，u，m，p}

For such as Attribute class character indexs such as octane number and condensation point of diesel oil, consider that optimized operation mode influences it, be characterized by following form:

${\mathrm{PRO}}_{s,u,m,p}={\mathrm{PRO}}_{s,u,p}^0+{\mathrm{\ΔPRO}}_{s,u,m,p}$

In the formula;

is the basic value of the s discharging logistics p character index of device u, is fixed value;

Δ PRO _{S, u, m, p}For the corresponding improvement amount of discharging logistics s p character index under the m optimized operation mode of device u, be the constant relevant with optimized operation mode m.

With preceding similar, the operation cost of secondary reaction device (referring to unit consumption) is a fixed value for a certain optimized operation mode, promptly

OpCost _u,m=const

Step B3.3: for modifying apparatus, like gasoline hydrodesulfurizationmethod, diesel oil hydrofining, catalytic reforming (CR) etc., the continuous adjustment of operating conditions is very big to its product influence, and logistics yield and operation cost and character index improvement amount (being operating conditions) are closely related.Because its component of producing directly gets into product oil and is in harmonious proportion, control service data according to the advanced person, set up the continuous model between modifying apparatus yield and the character index index, form is following:

${\mathrm{YIELD}}_{s,u,c}={\mathrm{YIELD}}_{s,u,c}^0+f_{\mathrm{HT}}\left({\mathrm{\ΔPRO}}_{s,u,p}\right)$

In the formula, is the discharging s of modifying apparatus u basic yield value (upgrading degree hour yield) when flow processing crude oil c;

Δ PRO _{S, u, p}The improvement amount of discharging s character index p for device u;

f _HT(Δ PRO _{S, u, p}) be discharging s yield and the Δ PRO of modifying apparatus u _{S, u, p}Between funtcional relationship, concrete form is with reference to monograph " hydroprocessing technique and engineering " (Li Dadong chief editor, Beijing: Sinopec publishing house, 2004);

According to the production run statistics, set up the mathematical model between modifying apparatus operation cost (referring to operate unit consumption) and the character index improvement amount, form is following:

${\mathrm{OpCost}}_u={\mathrm{OpCost}}_u^0+\exp \left(f_u^c\left({\mathrm{\ΔPRO}}_{s,u,p}\right)\right)$

In the formula,

is the fundamental operation expense of modifying apparatus u;

Be modifying apparatus u operation cost and character index improvement amount Δ PRO _{S, u, p}Between funtcional relationship, in reality, to adopt suitable expression-form according to the actual features of device, concrete form can be with reference to monograph " hydroprocessing technique and engineering " (Li Dadong chief editor, Beijing: Sinopec publishing house, 2004).

Character index for modifying apparatus is calculated, and character index improvement amount is for treating excellent variable, and its product property index is calculated as follows:

${\mathrm{PRO}}_{u,p}={\mathrm{PRO}}_{u,p}^0\±{\mathrm{\ΔPRO}}_{u,p}$

In the formula,

is the basic value of the product p character index of modifying apparatus u;

Δ PRO _{U, p}Improvement amount for the product p character index of modifying apparatus u.

Step B4: plant yield model, operation cost model and character index TRANSFER MODEL that step B3 obtains are write the scheduling model database.

Step C: said at the line model correcting module, be in the optimizing scheduling operational process, after a large amount of service datas under having obtained each optimized operation mode, plant yield model, energy consumption model and performance index model are carried out the online correction based on data.After the advanced control of each device operation, the device operation more tends to be steady, and makes scheduling model more accurate based on data in the line model correction.Its step is following:

Step C1: through said database support module; Read the yield model, energy consumption model and the performance index model that respectively install in the flow process under the current state under each optimized operation mode, obtain device and control the production run data under each optimized operation mode under the enforcement (energy consumption and material consumption and the extract fluidity ability index etc. that comprise unit capacity, device extraction logistics flux, device) the advanced person;

Step C2: the device production run data to step C1 gained are carried out pre-service, reject the dynamic process data, and carry out simple data check based on material balance and balance of properties equation, obtain the stable state production data;

Step C3: model correction

Step C3.1: the correction model of treating that step C1 is obtained is converted into the form of least square form;

Step C3.2: each device production run data under each optimized operation mode according under the steady state operation of step C2 acquisition are carried out the LEAST SQUARES MODELS FITTING correction based on data.

Step C4: the device that step C3 is obtained has been revised yield model, energy consumption model and performance index model and has been write database.

Method provided by the invention comprise off-line modeling with at line model correction two parts.Limited optimized operation mode according to advanced person's control and installation optimization; Obtain having the scheduling model under practical operation meaning and actual attainable limited the optimized operation mode of advanced control; Guaranteeing the optimizing scheduling model simultaneously accurately, the optimizing scheduling result has real exercisable advantage; Under advanced control action; The charging oil property is gradually steady with the device operation; After the production run data that obtain under a large amount of each optimized operation mode; Based on data can obtain optimizing scheduling model more accurately in the line model correction, and be the optimizing scheduling model under attainable each optimized operation mode of the advanced control operation of lower floor.So, just formed the total solution of a closed loop.Make advanced person's control of device also have good service condition (material composition is steady in dispatching cycle) through this comprehensive solution; To having confirmed crude oil feeding composition and optimized operation mode; The product yield of associated production device also easily steadily with reach edge and optimized; Condition is provided for the further correction of scheduling model under each optimized operation mode; So just formed benign cycle, can effectively solve the oil refining enterprise that is difficult to accurately obtain under reaction raw materials variation and the operation fluctuation and control the application difficult problem in implementing with the advanced person at the Optimization of Production Dispatching model.

Description of the invention provides for example with for the purpose of describing, and is not the disclosed form that exhaustively perhaps limit the invention to.A lot of modifications and variation are obvious for those of ordinary skill in the art.Selecting and describing embodiment is for better explanation principle of the present invention and practical application, thereby and makes those of ordinary skill in the art can understand the various embodiment that have various modifications that the present invention's design is suitable for special-purpose.

Claims (5)

1. the oil refining production run optimizing scheduling modeling method based on integrated Control and Optimization is characterized in that, comprising:

Steps A: the host computer initialization,

Wherein, in said host computer, set up with lower module: the off-line modeling module, at line model correcting module, expert decision-making reasoning module, optimize and find the solution module, Optimization Model generation module and database support module, wherein; The off-line modeling module; Through said database support module, acquisition need be carried out the interior device information of flow process of modeling statistics, and the apparatus for initializing model data; At the line model correcting module; Through said database support module, obtain off-line modeling module gained device optimizing scheduling model, and be initialized as online modified basis model;

Step B: said off-line modeling module, be optimized operator scheme classification and statistical study through the service data of the advanced person being controlled under the enforcement of respectively installing, obtain each device statistical model under the accessible Different Optimization operator scheme under the advanced person controls;

Step C: said at the line model correcting module, in the optimizing scheduling operational process, after a large amount of service datas under having obtained each optimized operation mode, plant yield model, energy consumption model and performance index model are carried out the online correction based on data.

2. the method for claim 1 is characterized in that, step B comprises:

Step B1: through reading database, obtain to treat the device information of modeling, and each mounted cast parameter of initialization;

Step B2: through said database support module, obtain advanced control implement down respectively install service data, data are carried out pre-service, the production run data under the optimized operation mode are stablized in selection;

Step B3: the stable state production run data to step B2 obtains are added up and classification analysis, obtain yield model, energy consumption model and performance index model under the accessible Different Optimization operator scheme of the advanced control of each device;

Step B4: plant yield model, operation cost model and character index TRANSFER MODEL that step B3 obtains are write the scheduling model database.

3. method as claimed in claim 2 is characterized in that step B3 comprises:

Step B3.1: for the time processing device, according to setting up based on the yield multi-model under limited the optimized operation mode of advanced person's control, promptly based on each the side line cutting temperature under limited the optimized operation mode of advanced person's control and the incision principle that dangles

YIELD _s，u，c,m=f _Frac(TBP _c,IBP _{s+1，u，c，m},EP _s-1,u,c，m,α _{s-1，u，c，m},α _{s-1，u，c，m})

In the formula, s is the device logistics, is gasoline, boat coal, light bavin, heavy bavin, residual oil for time processing device value, and m is the installation optimization operator scheme, and its value is gasoline pattern, diesel fuel mode, total light receipts pattern, TBP _cBe the true boiling point distillation data of crude oil c, its data layout is { [TI _{C, comp}TE _{C, comp}), D _{C, comp}, TI here _{C, comp}, TE _{C, comp}Be respectively initial temperature and the final temperature of the true boiling point close-boiling cut comp of crude oil c, D _{C, comp}Be the yield value of the close-boiling cut comp of crude oil c, IBP _{S+1, u, c, m}, EP _{S-1, u, c, m}Be respectively device u at the end point of distillation of the initial boiling point of charging crude oil c and m optimized operation mode bottom discharge s+1 and s-1 or do α _{S, u, c, m}, α _{S-1, u, c, m}Be respectively device u logistics s and the overlapping component of s+1 partition factor under charging crude oil c and m optimized operation mode, YIELD to the partition factor of logistics s and logistics s and the overlapping component of s-1 to logistics s-1 _{S, u, c, m}Be the yield value of device u logistics s under charging crude oil c and m optimized operation mode,

For atmospheric vacuum distillation process, the performance index TRANSFER MODEL adopts following mode to represent:

PRO _s,u，p=PRO _s′,u，p×ProModel _{s′，s，u，m，p}

In the formula, p is the character index, and value is sulfur content, heavy metal such as iron, manganese content, octane value, cetane rating etc., PRO _{S, u, p}Be the p character desired value of the logistics s of u device, PRO _{S ＇, u, p}Be the value of feed stream s ＇ character index p of device u, ProModel _{S ＇, s, u, m, p}Be device u p performance index transfer coefficient from charging s' to discharging s under optimized operation mode m,

Operation cost unit consumption hypothesis is a fixed value for fixedly crude oil and a certain optimized operation mode, promptly

OpCost _u,c,m=const

In the formula, OpCost _{U, c, m}Be the unit operation expense of device u under crude oil c and m optimized operation mode;

Step B3.2: for the secondary reaction device,, investigate of the influence of Different Optimization operator scheme to model according to the service data under limited a plurality of optimized operation mode of advanced person's control and integrated optimization,

For the yield model, at first yield of the product stream s under certain device u and certain optimisation operator scheme m is taken fixed value, add compensation again for the variation of feed composition, promptly

${\mathrm{YIELD}}_{s,u,c,m}={\mathrm{YIELD}}_{s,u,m}^0+{\mathrm{\ΔYIELD}}_{s,u,c,m}$

In the formula,

The basic yield value of discharging logistics s under the m optimized operation mode for device u is the fixed value relevant with optimized operation mode, Δ YIELD _{S, u, c, m}For device u under the m optimized operation mode to the yield offset of product stream s under the feed variation,

Character index and raw material composition, operating conditions and install closely relatedly, in the optimizing scheduling model, to content class character index, it is relevant with following form with optimized operation mode:

PRO _s,u,m,p=PRO _s′,u，p×ProModel _s′,s,u,m,p

For such as Attribute class character indexs such as octane number and condensation point of diesel oil, consider that optimized operation mode influences it, be characterized by following form:

${\mathrm{PRO}}_{s,u,m,p}={\mathrm{PRO}}_{s,u,p}^0+{\mathrm{\ΔPRO}}_{s,u,m,p}$

In the formula,

The basic value of s discharging logistics p character index for device u is fixed value; Δ PRO _{S, u, m, p}For the corresponding improvement amount of discharging logistics s p character index under the m optimized operation mode of device u, be the constant relevant with optimized operation mode m,

The operation cost of secondary reaction device is a fixed value for a certain optimized operation mode, promptly

OpCost _u,m=const；

Step B3.3: for modifying apparatus; The continuous adjustment of operating conditions is very big to its product influence, and logistics yield and operation cost and character index improvement amount are closely related, control service data according to the advanced person; Set up the continuous model between modifying apparatus yield and the character index index, form is following:

${\mathrm{YIELD}}_{s,u,c}={\mathrm{YIELD}}_{s,u,c}^0+f_{\mathrm{HT}}\left({\mathrm{\ΔPRO}}_{s,u,p}\right)$

In the formula,

Be the discharging s of modifying apparatus u basic yield value when the flow processing crude oil c, Δ PRO _{S, u, p}Be the improvement amount of discharging s character index p of device u,

f _HT(Δ PRO _{S, u, p}) be discharging s yield and the Δ PRO of modifying apparatus u _{S, u, p}Between funtcional relationship,

According to the production run statistics, set up the mathematical model between modifying apparatus operation cost and the character index improvement amount, form is following:

${\mathrm{OpCost}}_u={\mathrm{OpCost}}_u^0+\exp \left(f_u^c\left({\mathrm{\ΔPRO}}_{s,u,p}\right)\right)$

In the formula,

Be the fundamental operation expense of modifying apparatus u,

Be modifying apparatus u operation cost and character index improvement amount Δ PRO _{S, u, p}Between funtcional relationship,

Character index for modifying apparatus is calculated, and character index improvement amount is for treating excellent variable, and its product property index is calculated as follows:

${\mathrm{PRO}}_{u,p}={\mathrm{PRO}}_{u,p}^0\±{\mathrm{\ΔPRO}}_{u,p}$

In the formula,

Be the basic value of the product p character index of modifying apparatus u, Δ PRO _{U, p}Improvement amount for the product p character index of modifying apparatus u.

4. the method for claim 1 is characterized in that, step C comprises:

Step C1: through said database support module; Read the yield model, energy consumption model and the performance index model that respectively install in the flow process under the current state under each optimized operation mode, obtain device and control the production run data under each optimized operation mode under the enforcement the advanced person;

Step C2: the device production run data to step C1 gained are carried out pre-service, reject the dynamic process data, and carry out simple data check based on material balance and balance of properties equation, obtain the stable state production data;

Step C3: model correction;

Step C4: the device that step C3 is obtained has been revised yield model, energy consumption model and performance index model and has been write database.

5. method as claimed in claim 4 is characterized in that step C3 comprises:

Step C3.1: the correction model of treating that step C1 is obtained is converted into the form of least square form;

Step C3.2: each device production run data under each optimized operation mode according under the steady state operation of step C2 acquisition are carried out the LEAST SQUARES MODELS FITTING correction based on data.

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