CN102360181A - Low-temperature heat real-time optimization system based on general algorithm sequential quadratic programming (GA-SQP) mixed optimization strategy - Google Patents
Low-temperature heat real-time optimization system based on general algorithm sequential quadratic programming (GA-SQP) mixed optimization strategy Download PDFInfo
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Abstract
The invention relates to the field of advanced manufacture and automation, in particular to a low-temperature heat real-time optimization system based on the general algorithm sequential quadratic programming (GA-SQP) mixed optimization strategy, which comprises a real-time data base, a low-temperature heat system soft measurement module, an energy consumption optimization module, a user interface and a real-time information issuing module based on Flash. Compared with the prior art, the low-temperature heat real-time optimization system has the advantages that the flow process simulation technology and the exergy economics are used as the basis for building a low-temperature heat system simulation and optimization model, a mixed solving strategy of combining the classical mathematical programming sequence secondary planning SQP and the novel group intelligent optimization algorithm GA is adopted for solving the nonlinear optimization model, and the real-time data base technology is used for collecting real-time information in the production process, so the real-time monitoring, the off-line emulation, the on-line simulation and the real-time optimization are realized.
Description
Technical field
The present invention relates to advanced manufacturing and automatic field, specifically a kind of low-temperature heat real-time optimization system based on GA-SQP hybrid optimization strategy.
Background technology
In recent years, petroleum chemical enterprise is explored for the low-temperature heat system of full factory under the framework of " big system capacity comprehensive utilization ", has formed the hot utilization system of new type low temperature that includes many cover process units.The hot utilization system of these new type low temperatures greatly reduces enterprise energy consumption, and has obtained huge economic benefit.The following problem of ubiquity but the hot utilization system of new type low temperature is in operation:
1. system's bulky complex, it is many to relate to device, and thermal source and hot trap disperse, and are difficult to unified management and scheduling;
2. measurement point disperses, and the part key node lacks measurement instrument, and managerial personnel are difficult to grasp system's real time execution situation comprehensively;
3. management rests on the experiential operating stage; Lack rational mathematical optimization model auxiliary dispatching personnel decision-making, receive processing scheme, machining load, climatic environment, and the restriction of managerial personnel's professional skill; System's operation often departs from optimum point, and energy-saving effect does not reach design load.
Therefore, a kind of low-temperature heat real-time optimization system that can optimize the low-temperature heat integrated artistic of design is vital.
Summary of the invention
The objective of the invention is to overcome the deficiency of prior art, a kind of low-temperature heat real-time optimization system based on GA-SQP hybrid optimization strategy that can optimize the low-temperature heat integrated artistic is provided.
In order to achieve the above object; The present invention includes real-time data base, the soft measurement module of low-temperature heat system, energy optimization module, user interface and based on Flash real-time information release module, it is characterized in that: Process Control System is sent to real-time data base with process datas such as the temperature that collects, pressure, flows; Real time data is sent to soft measurement module, and data are carried out treatment for correcting, exports soft measurement result of calculation and deposits real-time data base in; The user passes through application operating interface configurations real-time model parameter: the energy consumption conversion factor, and deposit real-time data base in; The energy optimization module reads real time data through the real time data bank interface: process data and model parameter; Generate the real-time optimization model; Find the solution through optimized Algorithm then; And with real time modelling result of calculation: real time modelling result of calculation and real-time optimization scheduling scheme deposit real-time data base in through the real time data bank interface; The user is through application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module receives emulator command, the beginning simulation calculation, after calculating was accomplished, the result was sent to user interface with simulation calculation; Read real time data based on Flash real-time information release module through the real time data interface, be presented in real time in the Web process flow diagram through Flash then; User interface reads real-time optimization scheduling scheme data, real time modelling calculation result data and simulation calculation result data.
Described energy optimization module is accomplished following steps in regular turn: step a1. process data and energy consumption conversion factor are sent to the energy optimization module through the real time data interface module; Step b1. data processing is rejected error; Step c1. judges whether to start computation optimization, if not carrying out steps d 1, if carry out step e1; Steps d 1. is carried out analog computation according to energy consumption calculation model, and calculating passes to optimal module with power consumption values after accomplishing, and exports the analog computation result simultaneously; Step e1: adjust optimization variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in the energy consumption calculation model, calculate power consumption values and turn back to optimal module by energy consumption calculation model, after computation optimization is accomplished, the output The optimization results.
The analog computation flow process of described energy consumption calculation model is following: step a2. reads Apsen Plus heat exchanger network model through Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB through MATLAB ActiveX Automation interface, and generates energy consumption calculation model; Step c2. is with the energy optimization model parameter: process data, energy consumption conversion factor, optimization variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and output analog computation result goes out to deliver to optimal module with power consumption values simultaneously after accomplishing.
The optimizing flow process of described energy optimization module is following: after process data that step a3. energy consumption calculation model basis collects and energy consumption conversion factor are carried out analog computation; Power consumption values is sent to genetic algorithm GA and SQP SQP hybrid optimization strategy; And judge whether to start computation optimization; If then proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to genetic algorithm GA, seeks global optimum's scheduling scheme; Step c3. output global optimum scheduling scheme; Steps d 3. is carried out the local strengthening optimizing according to SQP SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. confirms and output optimal scheduling scheme.
Described soft measurement module idiographic flow is following: numerical value such as step a4. collecting temperature, pressure, flow; Step b4. data processing is rejected error; Step c4. flowsheeting software Aspen Plus sets up heat exchanger network mechanism; Steps d 4. analog computation results; Step e4. judges whether the analog computation result has corresponding measurement numerical value, if corresponding measurement numerical value is arranged then proceed step f4, if do not have corresponding measurement numerical value then export simulation value and deposit real-time data base in; Step f4. will measure numerical value in real time and simulation value compares; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range then early warning instrument maintenance, if do not exceed preset range then after the simulation value replacement measured numerical value in real time, the output simulation value also deposited real-time data base in.
Described real time data interface module is packaged into higher level lanquage C# version with IP.21 bottom api interface.
Described specific as follows based on Flash real-time information release module issue real-time information flow process: real-time data base sends to Web server with real time data through the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in the browser shows dynamic flow diagram and dynamic trend figure.
The present invention compares with prior art, and low-temperature heat real-time optimization system uses economics to be the basis with flowsheeting technology and fire, sets up low-temperature heat system emulation and Optimization Model; The mixing solution strategies that adopts classical mathematics planning SQP SQP and novel colony intelligence optimized Algorithm genetic algorithm GA to combine is found the solution the nonlinear optimization model; Utilize the real-time data base technology to gather the production run real-time information, thereby realize real-time monitoring, off-line simulation, online simulation and real-time optimization.
Description of drawings
Fig. 1 is a software flow pattern of the present invention.
Fig. 2 is an energy optimization module process flow diagram of the present invention.
Fig. 3 is an optimized Algorithm process flow diagram of the present invention.
Fig. 4 is a soft measurement module process flow diagram of the present invention.
Fig. 5 is the real-time information release module process flow diagram based on Flash of the present invention.
Embodiment
Combine accompanying drawing that the present invention is done further describes at present.
Referring to Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5; The present invention includes real-time data base, the soft measurement module of low-temperature heat system, energy optimization module, user interface and based on Flash real-time information release module, it is characterized in that: Process Control System is sent to real-time data base with process datas such as the temperature that collects, pressure, flows; Real time data is sent to soft measurement module, and data are carried out treatment for correcting, exports soft measurement result of calculation and deposits real-time data base in; The user passes through application operating interface configurations real-time model parameter: the energy consumption conversion factor, and deposit real-time data base in; The energy optimization module reads real time data through the real time data bank interface: process data and model parameter; Generate the real-time optimization model; Find the solution through optimized Algorithm then; And with real time modelling result of calculation: real time modelling result of calculation and real-time optimization scheduling scheme deposit real-time data base in through the real time data bank interface; The user is through application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module receives emulator command, the beginning simulation calculation, after calculating was accomplished, the result was sent to user interface with simulation calculation; Read real time data based on Flash real-time information release module through the real time data interface, be presented in real time in the Web process flow diagram through Flash then; User interface reads real-time optimization scheduling scheme data, real time modelling calculation result data and simulation calculation result data.The real time data interface module is packaged into higher level lanquage C# version with IP.21 bottom api interface.
The energy optimization module is accomplished following steps in regular turn: step a1. process data and energy consumption conversion factor are sent to the energy optimization module through the real time data interface module; Step b1. data processing is rejected error; Step c1. judges whether to start computation optimization, if not carrying out steps d 1, if carry out step e1; Steps d 1. is carried out analog computation according to energy consumption calculation model, and calculating passes to optimal module with power consumption values after accomplishing, and exports the analog computation result simultaneously; Step e1: adjust optimization variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in the energy consumption calculation model, calculate power consumption values and turn back to optimal module by energy consumption calculation model, after computation optimization is accomplished, the output The optimization results.
The analog computation flow process of energy consumption calculation model is following: step a2. reads Apsen Plus heat exchanger network model through Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB through MATLAB ActiveX Automation interface, and generates energy consumption calculation model; Step c2. is with the energy optimization model parameter: process data, energy consumption conversion factor, optimization variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and output analog computation result goes out to deliver to optimal module with power consumption values simultaneously after accomplishing.
The optimizing flow process of energy optimization module is following: after process data that step a3. energy consumption calculation model basis collects and energy consumption conversion factor are carried out analog computation; Power consumption values is sent to genetic algorithm GA and SQP SQP hybrid optimization strategy; And judge whether to start computation optimization; If then proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to genetic algorithm GA, seeks global optimum's scheduling scheme; Step c3. output global optimum scheduling scheme; Steps d 3. is carried out the local strengthening optimizing according to SQP SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. confirms and output optimal scheduling scheme.
Described soft measurement module idiographic flow is following: numerical value such as step a4. collecting temperature, pressure, flow; Step b4. data processing is rejected error; Step c4. flowsheeting software Aspen Plus sets up heat exchanger network mechanism; Steps d 4. analog computation results; Step e4. judges whether the analog computation result has corresponding measurement numerical value, if corresponding measurement numerical value is arranged then proceed step f4, if do not have corresponding measurement numerical value then export simulation value and deposit real-time data base in; Step f4. will measure numerical value in real time and simulation value compares; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range then early warning instrument maintenance, if do not exceed preset range then after the simulation value replacement measured numerical value in real time, the output simulation value also deposited real-time data base in.
Specific as follows based on Flash real-time information release module issue real-time information flow process: real-time data base sends to Web server with real time data through the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in the browser shows dynamic flow diagram and dynamic trend figure.
Claims (7)
1. low-temperature heat real-time optimization system based on GA-SQP hybrid optimization strategy; Comprise real-time data base, the soft measurement module of low-temperature heat system, energy optimization module, user interface and based on Flash real-time information release module, it is characterized in that: Process Control System is sent to real-time data base with process datas such as the temperature that collects, pressure, flows; Real time data is sent to soft measurement module, and data are carried out treatment for correcting, exports soft measurement result of calculation and deposits real-time data base in; The user passes through application operating interface configurations real-time model parameter: the energy consumption conversion factor, and deposit real-time data base in; The energy optimization module reads real time data through the real time data bank interface: process data and model parameter; Generate the real-time optimization model; Find the solution through optimized Algorithm then; And with real time modelling result of calculation: real time modelling result of calculation and real-time optimization scheduling scheme deposit real-time data base in through the real time data bank interface; The user is through application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module receives emulator command, the beginning simulation calculation, after calculating was accomplished, the result was sent to user interface with simulation calculation; Read real time data based on Flash real-time information release module through the real time data interface, be presented in real time in the Web process flow diagram through Flash then; User interface reads real-time optimization scheduling scheme data, real time modelling calculation result data and simulation calculation result data.
2. a kind of low-temperature heat real-time optimization system according to claim 1 based on GA-SQP hybrid optimization strategy, it is characterized in that: described energy optimization module is accomplished following steps in regular turn: step a1. process data and energy consumption conversion factor are sent to the energy optimization module through the real time data interface module; Step b1. data processing is rejected error; Step c1. judges whether to start computation optimization, if not carrying out steps d 1, if carry out step e1; Steps d 1. is carried out analog computation according to energy consumption calculation model, and calculating passes to optimal module with power consumption values after accomplishing, and exports the analog computation result simultaneously; Step e1: adjust optimization variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in the energy consumption calculation model, calculate power consumption values and turn back to optimal module by energy consumption calculation model, after computation optimization is accomplished, the output The optimization results.
3. a kind of energy optimization module flow process according to claim 2, it is characterized in that: the analog computation flow process of described energy consumption calculation model is following: step a2. reads Apsen Plus heat exchanger network model through Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB through MATLAB ActiveX Automation interface, and generates energy consumption calculation model; Step c2. is with the energy optimization model parameter: process data, energy consumption conversion factor, optimization variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and output analog computation result goes out to deliver to optimal module with power consumption values simultaneously after accomplishing.
4. a kind of low-temperature heat real-time optimization system according to claim 1 based on GA-SQP hybrid optimization strategy; It is characterized in that: the optimizing flow process of described energy optimization module is following: after process data that step a3. energy consumption calculation model basis collects and energy consumption conversion factor are carried out analog computation; Power consumption values is sent to genetic algorithm GA and SQP SQP hybrid optimization strategy; And judge whether to start computation optimization; If then proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to genetic algorithm GA, seeks global optimum's scheduling scheme; Step c3. output global optimum scheduling scheme; Steps d 3. is carried out the local strengthening optimizing according to SQP SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. confirms and output optimal scheduling scheme.
5. a kind of low-temperature heat real-time optimization system according to claim 1 based on GA-SQP hybrid optimization strategy, it is characterized in that: described soft measurement module idiographic flow is following: step a4. measures numerical value such as temperature, pressure, flow in real time; Step b4. data processing is rejected error; Step c4. flowsheeting software Aspen Plus sets up heat exchanger network mechanism; Steps d 4. analog computation results; Step e4. judges whether the analog computation result has corresponding measurement numerical value, if corresponding measurement numerical value is arranged then proceed step f4, if do not have corresponding measurement numerical value then export simulation value and deposit real-time data base in; Step f4. will measure numerical value in real time and simulation value compares; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range then early warning instrument maintenance, if do not exceed preset range then after the simulation value replacement measured numerical value in real time, the output simulation value also deposited real-time data base in.
6. a kind of low-temperature heat real-time optimization system based on GA-SQP hybrid optimization strategy according to claim 1, it is characterized in that: described real time data interface module is packaged into higher level lanquage C# version with IP.21 bottom api interface.
7. a kind of low-temperature heat real-time optimization system based on GA-SQP hybrid optimization strategy according to claim 1 is characterized in that: described specific as follows based on Flash real-time information release module issue real-time information flow process: real-time data base sends to Web server with real time data through the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in the browser shows dynamic flow diagram and dynamic trend figure.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1877589A (en) * | 2006-07-06 | 2006-12-13 | 上海交通大学 | Container ship on-line real-time pre-stowage planning method |
CN1916791A (en) * | 2006-09-12 | 2007-02-21 | 浙江大学 | Method of soft measuring fusion index of producing propylene through polymerization in industrialization |
US20070059838A1 (en) * | 2005-09-13 | 2007-03-15 | Pavilion Technologies, Inc. | Dynamic constrained optimization of chemical manufacturing |
CN101619850A (en) * | 2009-08-06 | 2010-01-06 | 杭州盘古自动化系统有限公司 | Dispatching method and dispatching system based on load online forecasting of thermoelectric power system |
CN101852681A (en) * | 2010-03-31 | 2010-10-06 | 桂林电子科技大学 | Crack identification method of main shaft of boring machine |
CN102129242A (en) * | 2011-04-12 | 2011-07-20 | 上海大学 | Product quality control method during batch processing production process based on two-layer hybrid intelligent optimization |
-
2011
- 2011-09-07 CN CN 201110263252 patent/CN102360181B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070059838A1 (en) * | 2005-09-13 | 2007-03-15 | Pavilion Technologies, Inc. | Dynamic constrained optimization of chemical manufacturing |
CN1877589A (en) * | 2006-07-06 | 2006-12-13 | 上海交通大学 | Container ship on-line real-time pre-stowage planning method |
CN1916791A (en) * | 2006-09-12 | 2007-02-21 | 浙江大学 | Method of soft measuring fusion index of producing propylene through polymerization in industrialization |
CN101619850A (en) * | 2009-08-06 | 2010-01-06 | 杭州盘古自动化系统有限公司 | Dispatching method and dispatching system based on load online forecasting of thermoelectric power system |
CN101852681A (en) * | 2010-03-31 | 2010-10-06 | 桂林电子科技大学 | Crack identification method of main shaft of boring machine |
CN102129242A (en) * | 2011-04-12 | 2011-07-20 | 上海大学 | Product quality control method during batch processing production process based on two-layer hybrid intelligent optimization |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
CN103106333A (en) * | 2012-12-14 | 2013-05-15 | 上海优华系统集成技术有限公司 | Online theory energy consumption calculation system which is used for catalytic cracking unit and based on process simulation software |
CN103077263A (en) * | 2012-12-14 | 2013-05-01 | 上海优华系统集成技术有限公司 | Catalytic cracking transparent fractionating tower simulation calculation system based on process simulation software |
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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 |
CN103366066A (en) * | 2013-07-21 | 2013-10-23 | 浙江工业大学 | Method for realizing automatic tuning of Aspen Plus built-in solver |
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CN103699754B (en) * | 2014-01-02 | 2016-10-05 | 上海优华系统集成技术有限公司 | Catalytic cracking Vapor recovery unit unit optimization processing method based on process simulation software |
CN106021733A (en) * | 2016-05-23 | 2016-10-12 | 广东工业大学 | Rapid customization design service platform for production lines |
CN106021733B (en) * | 2016-05-23 | 2017-07-28 | 广东工业大学 | A kind of fast custom design service system of production line |
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WO2022233101A1 (en) * | 2021-05-06 | 2022-11-10 | 上海优华系统集成技术股份有限公司 | Intelligent optimization control device for low-temperature thermal system |
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