CN102520705B - Refining production process optimal analysis method and system - Google Patents
Refining production process optimal analysis method and system Download PDFInfo
- Publication number
- CN102520705B CN102520705B CN201110460828.1A CN201110460828A CN102520705B CN 102520705 B CN102520705 B CN 102520705B CN 201110460828 A CN201110460828 A CN 201110460828A CN 102520705 B CN102520705 B CN 102520705B
- Authority
- CN
- China
- Prior art keywords
- parameter
- refinery
- oil
- key parameter
- flowsheeting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention provides refining production process optimal analysis method and system. The method comprises the following steps that: receiving refining initial parameters input by users, such as parameters of all refining production devices, parameters between devices, refining process parameters and technical operation parameters; invoking fundamental data corresponding to the refining initial parameters; starting the corresponding model in a process simulation model library according to the refining initial parameters and the fundamental data, and generating the key parameter of the refining production process; comparing the key parameter with the optimal parameter and best practical value in an expert knowledge base, and generating a comparison result; and outputting the comparison result to users. Through the invention, the multi-variable optimal problem caused by the complex energy-material relation in the refining production process can be effectively solved; the energy-utilization reasonableness of the refining production process, the production devices, and main energy-utilization equipment can be judged; the potential point of improving the energy efficiency can be found; and a quantificational optimization scheme is finally obtained.
Description
Technical field
The invention relates to oil refining technology, is about a kind of refining production process optimal analysis method and system concretely.
Background technology
The energy is the motive power of economic development, is the important substance basis of human survival and development.Along with the fast development of Chinese national economy, the problem such as energy shortage and utilization factor are on the low side is seriously restricting the raising of the national economic development and living standards of the people.2010,2.39 hundred million tons of China's net importation crude oil, the external interdependency of crude oil exceedes 55%.According to the statistics of State Statistics Bureau, within 2010, China's total energy consumption is 32.5 hundred million tons of mark coals, and ten thousand yuan, whole nation gross domestic product (GDP) energy consumption declines 4.01% compared with 2009 year-on-years, but still has larger gap with world average level.After the United Nations Climate Change Conferences in 2010 of particularly holding in Copenhagen, the carbon dioxide discharge-reduction pressure that China faces is increasing.Therefore, no matter be the needs from meeting economic growth and social progress, or the needs of energy industry self-growth, all must vigorously implement energy-saving and emission-reduction, make great efforts to build low input, high production, low consumption, few discharge, continuable national economic system and conservation-minded society.
Petrochemical complex is the mainstay industry of the national economic development, and according to statistics, the energy consumption of China's oil chemical industry in 2010 accounts for 22.6% of national industrial total energy consumption.For being the Petrochemical Enterprises that production capacity rich and influential family uses again energy rich and influential family, carry out energy-saving and cost-reducing work meaning particularly great.
Refinery production run has the features such as production link is many, technological process long, technology-intensive, energy consumption system is complicated, belongs to the process industry of typical complex.Energy resource consumption is the important component part that forms Petrochemical Enterprises operation cost, direct relation profitability and the market competitiveness of enterprise, and cause the reason that Petrochemical Enterprises energy consumption is higher conventionally to have the following aspects: the one, refinery production run is technology-intensive, along with the technical progress of refinery process, production process and flow process are adjusted in time.The 2nd, many refineries form through reorganization and expansion repeatedly on original small-scale basis; owing to being subject to the restriction of technology and fund, often could not take into full account reasonable energy exchange and system optimization utilization between new device and original device, process units and public engineer system.The 3rd, in process of production, the change of feedstock property, operating conditions, product solution and the variation of external environmental condition all can produce the crisscross energy interweaving and material and change between each production link.Therefore, must often and timely the energy system of whole refinery production run be optimized, find that in time energy efficiency chance puts and form prioritization scheme adjustment, make energy system keep the running status of efficiency the best.
Summary of the invention
The invention provides a kind of refining production process optimal analysis method and system, the multivariate optimization problem forming to solve fast and effectively the complicated energy of refinery production run and material relation.
To achieve these goals, the invention provides a kind of refining production process optimal analysis method, the method comprises: the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of reception user input; Call the basic data corresponding with described refinery initial parameter; Start model corresponding in flowsheeting model bank according to described refinery initial parameter and basic data, generate refinery production run key parameter; Optimal Parameters in described key parameter and expert knowledge library and best practices value are compared, generate comparison result; Described comparison result is exported to described user.
To achieve these goals, the invention provides a kind of refinery production process optimization analytic system, this system comprises: parameter receiving element, the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of reception user input; Data call unit, for calling the basic data corresponding with described refinery initial parameter; Key parameter generation unit, for starting model corresponding to flowsheeting model bank according to described refinery initial parameter and basic data, generates refinery production run key parameter; Parameter comparing unit, for the Optimal Parameters of described key parameter and expert knowledge library and best practices value are compared, generates comparison result; Comparison result output unit, for exporting to described user by described comparison result.
The beneficial effect of the embodiment of the present invention is, the present invention can solve the multivariate optimization problem of the complicated energy of refinery production run and material relation formation fast and effectively, can judge between refinery production procedure, process units, device and emphasis with can equipment with can rationality, find out the potentiality point that improves energy efficiency, and finally provide the prioritization scheme of quantification, directly instruct fast the transformation of industry energy conservation Synergistic technique and production operation optimization.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.In the accompanying drawings:
Fig. 1 is embodiment of the present invention refining production process optimal analysis method process flow diagram;
Fig. 2 is the graph of a relation of embodiment of the present invention refinery production run energy efficiency intelligent optimization analytic function unit;
Fig. 3 is embodiment of the present invention refinery production process optimization analytic system structured flowchart;
Fig. 4 is that the present invention refines oil production run path analysis figure.
Embodiment
For making object, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with accompanying drawing, the embodiment of the present invention is described in further details.At this, schematic description and description of the present invention is used for explaining the present invention, but not as a limitation of the invention.
As shown in Figure 1, the embodiment of the present invention provides a kind of refining production process optimal analysis method, for the intelligent optimization analysis of refinery process energy conservation synergy, the method is optimized knowledge by refinery production run energy saving and efficiency increasing system and is combined with artificial intelligence technology, thought process, inference mode and the strict simulation to production run of dealing with problems by imitating the mankind, the problem in analyzing and diagnosing reality also forms prioritization scheme.Described method comprises:
S101: the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of reception user input.
This refining production process optimal analysis method can be realized by man-machine interaction workbench, flowsheeting model bank, expert knowledge library and path analysis and four functional units of inference system as shown in Figure 2.
Man-machine interaction workbench provides the window of user and computer exchange, for pointing out user to input necessary information and showing energy efficiency diagnostic result and prioritization scheme.The man-machine interworking platform of the present invention comprises icon, button, prompting frame, input frame, scroll bar etc.
In the present embodiment, the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between parameter, device that what user can input by man-machine interaction workbench comprise each refinery process units itself.
The present invention's " atmospheric tower feedboard overflash rate " key parameter taking calculating atmospheric and vacuum distillation unit is as example, and other key parameter is not described one by one.The related data that user inputs by man-machine interaction workbench is as shown in table 1:
The correlation parameter of table 1 user input
In the present embodiment, by the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of path analysis and inference system reception user input.In the example of " atmospheric tower feedboard overflash rate " key parameter that calculates atmospheric and vacuum distillation unit, path analysis and inference system receive the temperature and pressure of the correlation parameter of user's input.
S102: call the basic data corresponding with described refinery initial parameter.
Receive after the temperature and pressure of correlation parameter of table 1 in path analysis and inference system, need to call the relevant rudimentary data (i.e. the basic data corresponding with described refinery initial parameter) of storing in auxiliary data base, correlation technique data are as shown in table 2.
The relevant rudimentary data of storing in table 2 auxiliary data base
S103: start model corresponding in flowsheeting model bank according to described refinery initial parameter and basic data, generate refinery production run key parameter.
Further, start the corresponding model in flowsheeting model bank according to described refinery initial parameter and basic data, calculate refinery production run key parameter, specifically can comprise:
Determine the priority that starts model corresponding in described flowsheeting model bank according to described refinery initial parameter, start successively model corresponding in described flowsheeting model bank according to described priority, generate refinery production run key parameter.
In flowsheeting model bank, store multiple models of being set up by General-Purpose Simulation Softwares such as Pro/II, Petro-SIM, SPYRO, Aspen Plus, Aspen Utility, Prosteam that comprise, can calculate key parameter in conjunction with the primary data of user input, and the basic data of storing in Automatically invoked auxiliary data base as required.
Path analysis and inference system are according to the correlation (in table 1) of user's input and call the relevant rudimentary data (in table 2) in auxiliary data base, the atmospheric tower model that the Petro-SIM storing in startup flowsheeting model bank builds, the value that can obtain " atmospheric tower feedboard overflash rate " key parameter through model inside heat Balance Calculation is repeatedly 6.6%.
S104: the Optimal Parameters in described key parameter and expert knowledge library and best practices value are compared, generate comparison result, specifically comprise: determine the parameter call priority in described expert knowledge library according to described refinery initial parameter, according to described parameter call priority, the Optimal Parameters in described key parameter and expert knowledge library is compared, and generate comparison result.
S105: described comparison result is exported to described user.Further, described comparison result comprises: energy efficiency diagnostic result and Optimized Measures.
Expert knowledge library for store to the relationship between refinery process units, relevant apparatus, between production procedure and the related energy efficiency Optimal Parameters of scrap build and best practices value (KPI) thereof, parameter, qualitatively judge according to and the Optimized Measures of energy efficiency etc.
Illustrate energy efficiency Optimal Parameters and the best practices value thereof of in book expert knowledge library, storing below.
Taking the energy consumption of each process units as example, energy efficiency Optimal Parameters and best practices value thereof are as shown in table 3:
Table 3 part energy efficiency Optimal Parameters and best practices value thereof
Taking " atmospheric tower feedboard overflash rate " Optimal Parameters of atmospheric and vacuum distillation unit as row, its best practices value is 3% (" petroleum refining engineering (third edition) " of writing referring to Lin Shixiong, petroleum industry publishing house, 2008).The Optimized Measures of energy efficiency is: reduce feeding temperature.Quantitative data is: the every reduction by 1% of overflash rate, heater outlet temperature approximately can reduce by 3.3 DEG C, if heater efficiency is 90%, fuel value is 41.87MJ/kg, heating oil product amount is 2.918 × 105kg/h, can fuel saving oil 94.4kg/h, and the whole year can fuel saving oil 815.6t, be RMB$460 by price per ton, be worth and be about RMB$37.5 × 104.
Taking the heating furnace of atmospheric and vacuum distillation unit as example, its energy efficiency Optimal Parameters mainly contains " heater efficiency ", " excess air coefficient ", " exhaust gas temperature ", " body of heater thermal loss ", " CO content in smoke ".The best practices value of " heater efficiency " is as shown in table 4, and thermal load one hurdle in table is for qualitatively judging foundation, and the thermal efficiency one hurdle is best practices value.
The best practices value of table 4 heater efficiency
The best practices value of " excess air coefficient " is: when fuel burning oil, excess air coefficient is answered < 1.20; When fuel burning gas, excess air coefficient is answered < 1.15.Wherein, " fuel burning oil " and " fuel burning gas " is for qualitatively judging foundation.The Optimized Measures of energy efficiency is: " coefficient of excess is bigger than normal, should reduce excess air coefficient, suggestion and measure: select high-performance combustor 1.; 2. carry out burner daily servicing, adjusting and management; 3. reduce the each position of body of heater and leak out, three plates of controlling well operate.", " excess air coefficient is good ", analysis result corresponding to these measures (referring to Du Denghua, the impact of excess air coefficient on tubular heater, chemical plant and management, 2004).
The best practices value of " exhaust gas temperature " is: < dewpoint temperature+25, this Knowledge Source is in industry empirical value.Wherein the computing formula of dewpoint temperature is t=132+13.3lgS (S is the massfraction of elementary sulfur in fuel oil) or t=141+11.1lgH
2s (H
2s is H in fuel gas
2the percent by volume of S).The Optimized Measures of energy efficiency is: " high fume temperature, should reduce exhaust gas temperature, suggestion and measure: (1) improves radiation chamber rate of heat transfer, reduces wall with flues temperature; (2) improve convection cell rate of heat transfer, and rationally determine end heat transfer temperature difference; (3) set up flue gas waste heat recovery system; (4) set up reliable ash blowing and cleaning measure.", " exhaust gas temperature is reasonable ", the analysis result that these measures are corresponding different.
The best practices value of " body of heater thermal loss " is: furnace body outer wall temperature is answered 80 DEG C of < (referring to industry standard: SY/T0538-2004 " tubular heater specification ").
The best practices value of " CO content in smoke " is: < 150ppm (" tubular heater (second edition) " of writing referring to treatise: Qian Jialin, Sinopec publishing house, 2010).
Relationship between three Optimal Parameters of " heater efficiency " (representing with η), " excess air coefficient " (representing with α), " exhaust gas temperature " (representing with t) is as follows:
η＝(100-(8.3×10
-3+0.031×α)×(t+1.35×10
-4×t
2)-3)％。
The process units relating in the embodiment of the present invention generally includes atmospheric and vacuum distillation unit, catalytic cracking unit, catalytic reforming unit, hydrogenation plant, delayed coking unit etc., and battery limit (BL) scope comprises the related raw material of device, device internal process flow process and equipment, the relevant upstream and downstream logistics of product (containing intermediate product).Knowledge in expert knowledge library is mainly derived from the data such as refinery process design national standard, specification, list of references, and the expertise of design specialist, execute-in-place expert and energy-optimised expert summary.Expert knowledge library can carry out Dynamic Maintenance and renewal by knowledge engineer, thereby effectively adapts to run-time environment and dynamic processes extensive, that constantly change.
As shown in Figure 3, the embodiment of the present invention provides a kind of refinery production process optimization analytic system, and described system comprises: parameter receiving element 301, data call unit 302, key parameter generation unit 303, parameter comparing unit 304 and comparison result output unit 305.
Parameter receiving element 301 receives the refinery initial parameter of the parameter that comprises each refinery process units itself of user input, parameter, refinery flow process parameter and process operation parameter between installing.
This refining production process optimal analysis method can be realized by man-machine interaction workbench, flowsheeting model bank, expert knowledge library and path analysis and four functional units of inference system as shown in Figure 2.
In the present embodiment, the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between parameter, device that what user can input by man-machine interaction workbench comprise each refinery process units itself.
The present invention's " atmospheric tower feedboard overflash rate " key parameter taking calculating atmospheric and vacuum distillation unit is as example, and other key parameter is not described one by one.The related data that user inputs by man-machine interaction workbench as shown in Table 1.
In the present embodiment, by the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of path analysis and inference system reception user input.In the example of " atmospheric tower feedboard overflash rate " key parameter that calculates atmospheric and vacuum distillation unit, path analysis and inference system receive the temperature and pressure of the correlation parameter of user's input.
Data call unit 302 is for calling the basic data corresponding with described refinery initial parameter.
Receive at parameter receiving element 301 after the temperature and pressure of correlation parameter of table 1, need to call the relevant rudimentary data (i.e. the basic data corresponding with described refinery initial parameter) of storing in auxiliary data base by data call unit 302, correlation technique data are as above shown in table 2.
Key parameter generation unit 303, for starting model corresponding to flowsheeting model bank according to described refinery initial parameter and basic data, generates refinery production run key parameter.Further, described key parameter generation unit is specifically for determining the priority that starts model corresponding in described flowsheeting model bank according to described refinery initial parameter, start successively model corresponding in described flowsheeting model bank according to described priority, generate refinery production run key parameter.
In flowsheeting model bank, store multiple models of being set up by General-Purpose Simulation Softwares such as Pro/II, Petro-SIM, SPYRO, Aspen Plus, Aspen Utility, Prosteam that comprise, can calculate key parameter in conjunction with the primary data of user input, and the basic data of storing in Automatically invoked auxiliary data base as required.
Key parameter generation unit 303 is according to the correlation (in table 1) of user's input and call the relevant rudimentary data (in table 2) in auxiliary data base, the atmospheric tower model that the Petro-SIM storing in startup flowsheeting model bank builds, the value that can obtain " atmospheric tower feedboard overflash rate " key parameter through model inside heat Balance Calculation is repeatedly 6.6%.
Parameter comparing unit 304, for the Optimal Parameters of described key parameter and expert knowledge library is compared, generates comparison result.Further, described parameter comparing unit is specifically for determining the parameter call priority in described expert knowledge library according to described refinery initial parameter, according to described parameter call priority, the Optimal Parameters in described key parameter and expert knowledge library and best practices value are compared, and generate comparison result.
Comparison result output unit 305 is for exporting to described user by described comparison result, and described comparison result comprises: energy efficiency diagnostic result and Optimized Measures.
Illustrate energy efficiency Optimal Parameters and the best practices value thereof of in book expert knowledge library, storing below.
Taking the energy consumption of each process units as example, energy efficiency Optimal Parameters and best practices value thereof are as shown in table 3.
Taking " atmospheric tower feedboard overflash rate " Optimal Parameters of atmospheric and vacuum distillation unit as row, its best practices value is 3% (" petroleum refining engineering (third edition) " of writing referring to Lin Shixiong, petroleum industry publishing house, 2008).The Optimized Measures of energy efficiency is: reduce feeding temperature.Quantitative data is: the every reduction by 1% of overflash rate, heater outlet temperature approximately can reduce by 3.3 DEG C, if heater efficiency is 90%, fuel value is 41.87MJ/kg, heating oil product amount is 2.918 × 105kg/h, can fuel saving oil 94.4kg/h, and the whole year can fuel saving oil 815.6t, be RMB$460 by price per ton, be worth and be about RMB$37.5 × 104.
Taking the heating furnace of atmospheric and vacuum distillation unit as example, its energy efficiency Optimal Parameters mainly contains " heater efficiency ", " excess air coefficient ", " exhaust gas temperature ", " body of heater thermal loss ", " CO content in smoke ".The best practices value of " heater efficiency " is as shown in table 4, and thermal load one hurdle in table is for qualitatively judging foundation, and the thermal efficiency one hurdle is best practices value.
The best practices value of " excess air coefficient " is: when fuel burning oil, excess air coefficient is answered < 1.20; When fuel burning gas, excess air coefficient is answered < 1.15.Wherein, " fuel burning oil " and " fuel burning gas " is for qualitatively judging foundation.The Optimized Measures of energy efficiency is: " coefficient of excess is bigger than normal, should reduce excess air coefficient, suggestion and measure: select high-performance combustor 1.; 2. carry out burner daily servicing, adjusting and management; 3. reduce the each position of body of heater and leak out, three plates of controlling well operate.", " excess air coefficient is good ", analysis result corresponding to these measures (referring to Du Denghua, the impact of excess air coefficient on tubular heater, chemical plant and management, 2004).
The best practices value of " exhaust gas temperature " is: < dewpoint temperature+25, this Knowledge Source is in industry empirical value.Wherein the computing formula of dewpoint temperature is t=132+13.3lgS (S is the massfraction of elementary sulfur in fuel oil) or t=141+11.1lgH
2s (H
2s is H in fuel gas
2the percent by volume of S).The Optimized Measures of energy efficiency is: " high fume temperature, should reduce exhaust gas temperature, suggestion and measure: (1) improves radiation chamber rate of heat transfer, reduces wall with flues temperature; (2) improve convection cell rate of heat transfer, and rationally determine end heat transfer temperature difference; (3) set up flue gas waste heat recovery system; (4) set up reliable ash blowing and cleaning measure.", " exhaust gas temperature is reasonable ", the analysis result that these measures are corresponding different.
The best practices value of " body of heater thermal loss " is: furnace body outer wall temperature is answered 80 DEG C of < (referring to industry standard: SY/T0538-2004 " tubular heater specification ").
The best practices value of " CO content in smoke " is: < 150ppm (" tubular heater (second edition) " of writing referring to treatise: Qian Jialin, Sinopec publishing house, 2010).
Relationship between three Optimal Parameters of " heater efficiency " (representing with η), " excess air coefficient " (representing with α), " exhaust gas temperature " (representing with t) is as follows:
η＝(100-(8.3×10
-3+0.031×α)×(t+1.35×10
-4×t
2)-3)％。
Path analysis and inference system are the reasoning from logic program of Computerized intelligent, can man-machine interaction workbench, flowsheeting model bank be connected with expert knowledge library by machine language, and the information of inputting according to user is carried out data call and reasoning.
Path analysis can determine the priority level to rule invocation in analogy model storehouse and expert knowledge library according to the user profile of man-machine interactive platform, and reasoning effect and efficiency are had to important impact.The agent structure of path analysis is tree structure, wherein comprises some levels, and each level comprises again next level, by that analogy.Taking the path analysis of oil refining production run as example, as shown in Figure 4, enter behind oil refining production run path, first enter " energy-optimised between device " this level, after the respective rule optimization in calling analogy model storehouse expert knowledge library is analyzed, enter " single device is energy-optimised " this level, select to want the device (as atmospheric and vacuum distillation unit) of analyzing and diagnosing, continue to call respective rule and judge and enter next level (as the heating furnace in atmospheric and vacuum distillation unit, electro-desalting, fractionator, heat exchanger network etc.), by that analogy.
Inference machine can be regarded an interpreter in itself as, in reasoning process, and according to path analysis structure, the series of rules in explanation and executive expert's knowledge base.Reasoning process is an Expert-oriented knowledge base, based on hypothesis generation-test of hypothesis recursive process mechanism, by high level to bottom Stepwise Refinement, it is for reference that the energy efficiency diagnostic result producing and Optimized Measures feed back to man-machine interaction workbench.
When use, first by user according to the interface prompt of man-machine interaction workbench, the input quasi-solution required initial parameter value of optimization problem of determining.Path analysis and inference system are according to the KPI of set optimization key parameter, relationship and the necessary parameter that needs model to calculate, calculate, analyze, process, and point out relevant issues, according to the path optimizing in system, successively carry out step by step reasoning.Judge that user, by the problem that may exist in energy process, finds energy efficiency chance point, and the prioritization scheme of energy efficiency diagnostic result and quantification is presented on man-machine interactive platform interface.
Will analyze all devices of full factory as example taking user, user, first by the interface prompt of man-machine interaction workbench, inputs following parameter and analog value, in table 5, table 6:
The each plant energy consumption data entry form of table 5
Between table 6 device, qualitatively judge data entry form
Path analysis and inference system, according to the correlation parameter of input, call the energy efficiency Optimal Parameters stored in knowledge base and best practices value, qualitatively judge foundation, and by preliminary result feedback, to interface, result is as follows:
" 1. the energy consumption of atmospheric and vacuum distillation unit is higher, and suggestion is analysed in depth; The energy consumption of catalytic cracking unit, catalytic reforming unit, hydrogenation plant, delayed coking unit is all in advanced level and so on.
2. should not carry out heat integration or hot feed between device, suggestion is goed deep into device and is optimized.”
User then analyses in depth atmospheric and vacuum distillation unit, inputs following parameter by interface prompt, in table 7, table 8:
Table 7 atmospheric and vacuum distillation unit atmospheric tower part correlation parameter input table
Table 8 atmospheric and vacuum distillation unit heating furnace part correlation parameter input table
Path analysis and inference system, call Optimal Parameters and best practices value, relationship and the necessary parameter that needs model to calculate in knowledge base, and successively carries out reasoning, and net result is as follows:
1. calculating atmospheric tower feedboard overflash rate is 6.6%, exceeds 3.3% than best practices value, and suggestion is optimized.Concrete measure is: reduce feeding temperature, reduce 10 DEG C, be 41.87MJ/kg by fuel value, heating oil product amount is that 2.918 × 105kg/h calculates, can fuel saving oil 311.52kg/h, the whole year can fuel saving oil 2691.5t, be RMB$460 by price per ton, be worth and be about RMB$123.75 × 104.
2. heating furnace aspect:
(1) heater efficiency is low, and suggestion improves heater efficiency.
(2) fuel combustion situation is bad, suggestion and measure: select high-performance combustor 1.; 2. carry out burner daily servicing, adjusting and management; 3. reduce the each position of body of heater and leak out, three plates of controlling well operate.
(3) in fuel, sulfur content is high, needs desulfurizing and purifying; Convection cell is selected the material (as cast iron, ND steel, enamel pipe, glass tube etc.) of resistance to dew point corrosion;
(4) furnace body outer wall temperature is high, and loss heat is large, and novel adiabatic coating or repairing burner hearth adiabatic coating are selected in suggestion 1.; 2. furnace lining is answered periodic maintenance and maintenance.
The beneficial effect of the embodiment of the present invention is, the present invention can solve the multivariate optimization problem of the complicated energy of refinery production run and material relation formation fast and effectively, can judge between refinery production procedure, process units, device and emphasis with can equipment with can rationality, find out the potentiality point that improves energy efficiency, and finally provide the prioritization scheme of quantification, directly instruct fast the transformation of industry energy conservation Synergistic technique and production operation optimization.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the foregoing is only specific embodiments of the invention; the protection domain being not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment of making, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (6)
1. a refining production process optimal analysis method, is characterized in that, described method comprises:
Path analysis and inference system receive the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of user's input, and described refinery initial parameter comprises: stripped vapor at the bottom of heavy oil, tower at the bottom of the atmospheric tower charging of atmospheric and vacuum distillation unit, tower top gasoline, a normal line kerosene, normal two wires diesel oil, atmosphere 3rd side cut diesel oil, tower, often two wires stripped vapor and atmosphere 3rd side cut stripped vapor;
Described path analysis and inference system call basic data corresponding with described refinery initial parameter in auxiliary data base;
Start model corresponding in flowsheeting model bank according to described refinery initial parameter and basic data, generate refinery production run key parameter, in described flowsheeting model bank, store multiple models of being set up by Pro/II, Petro-SIM, SPYRO, Aspen Plus, Aspen Utility, Prosteam that comprise, primary data in conjunction with user's input calculates key parameter, the basic data of storing in Automatically invoked auxiliary data base as required;
Optimal Parameters in described key parameter and expert knowledge library and best practices value are compared, generate energy efficiency diagnostic result and Optimized Measures, described Optimal Parameters comprises: atmospheric and vacuum distillation unit energy consumption, Energy Consumption in Fcc Unit, catalytic reforming unit energy consumption, hydrogenation plant energy consumption and delayed coking unit energy consumption, and corresponding best practices value is respectively 7.2～9.6kg mark oil/t crude oil, 40.0kg mark oil/t raw material, 61.0kg mark oil/t raw material, 7.0kg mark oil/t raw material and 18.0kg mark oil/t raw material;
Described energy efficiency diagnostic result and Optimized Measures are exported to described user;
Wherein, start the corresponding model in flowsheeting model bank according to described refinery initial parameter and basic data, generate refinery production run key parameter, comprise: determine the priority that starts model corresponding in described flowsheeting model bank according to described refinery initial parameter, start successively model corresponding in described flowsheeting model bank according to described priority, generate refinery production run key parameter.
2. the method for claim 1, it is characterized in that, in described knowledge base, store between each refinery process units, device, relationship between refinery flow process and the related energy efficiency Optimal Parameters of scrap build, best practices value KPI, parameter, qualitatively judge according to and energy efficiency Optimized Measures.
3. method as claimed in claim 2, is characterized in that, the Optimal Parameters in described key parameter and expert knowledge library is compared, and generates comparison result, comprising:
Determine the parameter call priority in described expert knowledge library according to described refinery initial parameter, according to described parameter call priority, the Optimal Parameters in described key parameter and expert knowledge library is compared, and generate comparison result.
4. a refinery production process optimization analytic system, is characterized in that, described system comprises:
Parameter receiving element, path analysis and inference system receive the refinery initial parameter of parameter, refinery flow process parameter and process operation parameter between the parameter that comprises each refinery process units itself, the device of user's input, and described refinery initial parameter comprises: stripped vapor at the bottom of heavy oil, tower at the bottom of the atmospheric tower charging of atmospheric and vacuum distillation unit, tower top gasoline, a normal line kerosene, normal two wires diesel oil, atmosphere 3rd side cut diesel oil, tower, often two wires stripped vapor and atmosphere 3rd side cut stripped vapor;
Data call unit, calls for described path analysis and inference system the basic data that auxiliary data base is corresponding with described refinery initial parameter;
Key parameter generation unit, for starting model corresponding to flowsheeting model bank according to described refinery initial parameter and basic data, generate refinery production run key parameter, in described flowsheeting model bank, store multiple models of being set up by Pro/II, Petro-SIM, SPYRO, Aspen Plus, Aspen Utility, Prosteam that comprise, primary data in conjunction with user's input calculates key parameter, the basic data of storing in Automatically invoked auxiliary data base as required;
Parameter comparing unit, for the Optimal Parameters of described key parameter and expert knowledge library and best practices value are compared, generate energy efficiency diagnostic result and Optimized Measures, described Optimal Parameters comprises: atmospheric and vacuum distillation unit energy consumption, Energy Consumption in Fcc Unit, catalytic reforming (CR) energy consumption, hydrogenation plant energy consumption and delayed coking unit energy consumption, and corresponding best practices value is respectively 7.2～9.6kg mark oil/t crude oil, 40.0kg mark oil/t raw material, 61.0kg mark oil/t raw material, 7.0kg mark oil/t raw material and 18.0kg mark oil/t raw material;
Comparison result output unit, for exporting to described user by described energy efficiency diagnostic result and Optimized Measures;
Wherein, described key parameter generation unit is specifically for determining the priority that starts model corresponding in described flowsheeting model bank according to described refinery initial parameter, start successively model corresponding in described flowsheeting model bank according to described priority, generate refinery production run key parameter.
5. system as claimed in claim 4, it is characterized in that, in described knowledge base, store between each refinery process units, device, relationship between refinery flow process and the related energy efficiency Optimal Parameters of scrap build, best practices value KPI, parameter, qualitatively judge according to and energy efficiency Optimized Measures.
6. system as claimed in claim 4, it is characterized in that, described parameter comparing unit is specifically for determining the parameter call priority in described expert knowledge library according to described refinery initial parameter, according to described parameter call priority, the Optimal Parameters in described key parameter and expert knowledge library and best practices value are compared, and generate comparison result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110460828.1A CN102520705B (en) | 2011-12-31 | 2011-12-31 | Refining production process optimal analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110460828.1A CN102520705B (en) | 2011-12-31 | 2011-12-31 | Refining production process optimal analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102520705A CN102520705A (en) | 2012-06-27 |
| CN102520705B true CN102520705B (en) | 2014-11-26 |
Family
ID=46291658
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110460828.1A Active CN102520705B (en) | 2011-12-31 | 2011-12-31 | Refining production process optimal analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102520705B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI637251B (en) * | 2016-10-26 | 2018-10-01 | Mitsubishi Chemical Engineering Corporation | Analysis method of production process |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10429858B2 (en) | 2011-07-21 | 2019-10-01 | Bl Technologies, Inc. | Advisory controls of desalter system |
| CN103106333B (en) * | 2012-12-14 | 2015-08-19 | 上海优华系统集成技术有限公司 | The online theoretical energy consumption computing system of catalytic cracking unit based on process simulation software |
| CN103150452B (en) * | 2013-03-26 | 2016-02-24 | 上海优华系统集成技术有限公司 | A kind of Atmospheric vacuum reference energy consumption computing method based on process simulation software |
| CN104123584A (en) * | 2013-04-28 | 2014-10-29 | 成都勤智数码科技股份有限公司 | Organization optimization method based on information system |
| CN104123586A (en) * | 2013-04-28 | 2014-10-29 | 成都勤智数码科技股份有限公司 | Core information system based organization optimization platform |
| CN106444428B (en) * | 2016-10-25 | 2019-09-10 | 中国石油化工股份有限公司 | Atmospheric and vacuum distillation unit optimization operating system and method based on process simulation software |
| CN109299483B (en) * | 2017-07-24 | 2022-06-07 | 中国石油化工股份有限公司 | Energy analysis method and device for atmospheric and vacuum distillation |
| CN109299836B (en) * | 2017-07-24 | 2022-02-08 | 中国石油化工股份有限公司 | Diesel oil hydrogenation energy analysis method and device |
| CN107450503A (en) * | 2017-09-13 | 2017-12-08 | 佛山市南方数据科学研究院 | A kind of production process technology optimization system based on Internet of Things |
| CN108226093B (en) * | 2018-01-11 | 2021-05-28 | 南京富岛信息工程有限公司 | Automatic model parameter selection and correction method for atmospheric and vacuum device |
| CN110515359A (en) * | 2019-08-30 | 2019-11-29 | 嘉兴锦鸿包装有限公司 | The semi-automatic production method of packing case |
| CN112542216A (en) * | 2019-09-20 | 2021-03-23 | 中国石油化工股份有限公司 | FCC-SIM-based FCC catalyst database development method and equipment |
| CN112662423B (en) * | 2019-10-16 | 2022-11-04 | 中国石油天然气股份有限公司 | Process simulation method for catalytic gasoline hydro-upgrading process |
| CN112488530B (en) * | 2020-12-02 | 2024-02-09 | 中海油信息科技有限公司 | Intelligent level monitoring method, system, equipment and medium for refining enterprises |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2382523A1 (en) * | 1999-09-03 | 2001-03-15 | Quantis Formulation Inc. | Method of optimizing parameter values in a process of producing a product |
| CN101377678A (en) * | 2008-09-12 | 2009-03-04 | 华南理工大学 | Synthesis optimizing and scheduling system of energy system |
| CN101520656A (en) * | 2008-11-21 | 2009-09-02 | 东北大学 | On-line multi-mode time optimization scheduling method for steelmaking and continuous casting production |
| CN101760980A (en) * | 2010-01-22 | 2010-06-30 | 华南理工大学 | Monitoring control system of paper-making machine drying part energy system and working method thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19508476A1 (en) * | 1995-03-09 | 1996-09-12 | Siemens Ag | Control system for a plant in the basic material or processing industry or similar |
-
2011
- 2011-12-31 CN CN201110460828.1A patent/CN102520705B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2382523A1 (en) * | 1999-09-03 | 2001-03-15 | Quantis Formulation Inc. | Method of optimizing parameter values in a process of producing a product |
| CN101377678A (en) * | 2008-09-12 | 2009-03-04 | 华南理工大学 | Synthesis optimizing and scheduling system of energy system |
| CN101520656A (en) * | 2008-11-21 | 2009-09-02 | 东北大学 | On-line multi-mode time optimization scheduling method for steelmaking and continuous casting production |
| CN101760980A (en) * | 2010-01-22 | 2010-06-30 | 华南理工大学 | Monitoring control system of paper-making machine drying part energy system and working method thereof |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI637251B (en) * | 2016-10-26 | 2018-10-01 | Mitsubishi Chemical Engineering Corporation | Analysis method of production process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102520705A (en) | 2012-06-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102520705B (en) | Refining production process optimal analysis method and system | |
| Gong et al. | Global optimization for sustainable design and synthesis of algae processing network for CO2 mitigation and biofuel production using life cycle optimization | |
| Xi et al. | Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality | |
| Tan et al. | Stochastic optimization framework of the energy-water-emissions nexus for regional power system planning considering multiple uncertainty | |
| Gharaie et al. | Simultaneous optimization of CO2 emissions reduction strategies for effective carbon control in the process industries | |
| Karimkashi et al. | Total site energy improvement using R-curve concept | |
| Zhang et al. | Coupling process plants and utility systems for site scale steam integration | |
| CN103605821A (en) | Ethylene cracking furnace group load distribution optimization method | |
| CN104268712A (en) | Energy balancing and scheduling method based on improved mixed multi-population evolutionary algorithm | |
| Wang et al. | Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework | |
| CN114282736A (en) | Monitoring and management method for carbon emission of oil refining enterprise | |
| Wang et al. | The heterogeneous impact of environmental regulations on low‐carbon economic transformation in China: Empirical research based on the mediation effect model | |
| Xie et al. | China's optimal development pathway of intermittent renewable power towards carbon neutrality | |
| Wang et al. | Pathways of carbon emissions reduction under the water-energy constraint: A case study of Beijing in China | |
| Wang et al. | Research on CCUS business model and policy incentives for coal-fired power plants in China | |
| Xu et al. | A heuristic approach to design a cost-effective and low-CO2 emission synthesis in a heat exchanger network with crude oil distillation units | |
| Gong et al. | Multi-level and multi-granularity energy efficiency diagnosis scheme for ethylene production process | |
| Wu et al. | Environmental impacts of hydrotreating processes for the production of clean fuels based on life cycle assessment | |
| Maddaloni et al. | A quadratic programming model for the optimization of off-gas networks in integrated steelworks | |
| CN112950409A (en) | Production scheduling optimization method of gas and steam energy comprehensive utilization system | |
| Li et al. | The assessment of energy-related greenhouse gas emissions in China's chemical industry | |
| CN114958395B (en) | Waste heat utilization method and system for biomass carbonization furnace | |
| CN109299836B (en) | Diesel oil hydrogenation energy analysis method and device | |
| CN107273619B (en) | Steel enterprise static energy flow network optimization design method | |
| Zhao et al. | Uncovering the lifecycle carbon emissions and its reduction pathways: A case study of petroleum refining enterprise |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |