CN102360181B - 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 the manufacturing and automatic field, specifically a kind of Low Temperature Thermal 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 " large 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, relate to device many, 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-aided dispatcher decision-making, be subjected to 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, design a kind of Low Temperature Thermal real-time optimization system that can optimize the Low Temperature Thermal integrated artistic is vital.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, a kind of Low Temperature Thermal real-time optimization system based on GA-SQP hybrid optimization strategy that can optimize the Low Temperature Thermal 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 proofreaied and correct processing, 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 by the real time data bank interface: process data and model parameter, generate the real-time optimization model, then find the solution by optimized algorithm, and with real time modelling result of calculation: real time modelling result of calculation and Real time optimal dispatch scheme deposit real-time data base in by the real time data bank interface; The user is by application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module received emulator command, the beginning simulation calculation after calculating is completed, was sent to user interface with simulation result; Read real time data based on Flash real-time information release module by the real time data interface, then be presented in real time in the Web process flow diagram by Flash; User interface reads Real time optimal dispatch scheme data, real time modelling calculation result data and simulation result data.
Described energy optimization module is sequentially completed following steps: step a1. process data and energy consumption conversion factor are sent to the energy optimization module by the real time data interface module; Step b1. data are processed, and reject error; Step c1. judges whether to start to optimize and calculates, 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, after calculating is completed, power consumption values is passed to the optimization module, exports simultaneously the analog computation result; Step e1: adjust optimized variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in energy consumption calculation model, calculating power consumption values and turn back to the optimization module by energy consumption calculation model, after optimization was calculated and completed, result of calculation was optimized in output.
The analog computation flow process of described energy consumption calculation model is as follows: step a2. reads Apsen Plus heat exchanger network model by Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB by 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, optimized variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and after completing, output analog computation result, go out to deliver to the optimization module with power consumption values simultaneously.
The optimizing flow process of described energy optimization module is as follows: after the 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 Algorithms and seqential quadratic programming SQP hybrid optimization strategy, and judge whether start to optimize to calculate, if it is proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to Genetic Algorithms, 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 seqential quadratic programming SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. determines and exports the optimal scheduling scheme.
Described soft measurement module idiographic flow is as follows: the numerical value such as step a4. collecting temperature, pressure, flow; Step b4. data are processed, and reject 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 proceed step f4, if there is no corresponding measurement numerical value export simulation value and deposit real-time data base in; Step f4. compares Real-time Measuring numerical quantity and simulation value; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range the maintenance of early warning instrument, if do not exceed preset range after simulation value is replaced the Real-time Measuring numerical quantity, export simulation value and also deposit real-time data base in.
Described real time data interface module becomes higher level lanquage C# version with IP.21 Basic API interface encapsulation.
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 by the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in browser shows dynamic flow diagram and dynamic trend figure.
The present invention compares with prior art, Low Temperature Thermal real-time optimization system take flowsheeting technology and fire with economics as basic, set up low temperature heat system emulation and Optimized model; The hybrid solving strategy that adopts Classical mathematical programming seqential quadratic programming SQP and novel colony intelligence optimized algorithm Genetic Algorithms to combine is found the solution Non-linear Optimal Model; Utilize Real-Time Databases System Technique 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 software flow pattern of the present invention.
Fig. 2 is energy optimization module process flow diagram of the present invention.
Fig. 3 is optimized algorithm process flow diagram of the present invention.
Fig. 4 is 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
Now by reference to the accompanying drawings the present invention is described further.
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 proofreaied and correct processing, 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 by the real time data bank interface: process data and model parameter, generate the real-time optimization model, then find the solution by optimized algorithm, and with real time modelling result of calculation: real time modelling result of calculation and Real time optimal dispatch scheme deposit real-time data base in by the real time data bank interface; The user is by application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module received emulator command, the beginning simulation calculation after calculating is completed, was sent to user interface with simulation result; Read real time data based on Flash real-time information release module by the real time data interface, then be presented in real time in the Web process flow diagram by Flash; User interface reads Real time optimal dispatch scheme data, real time modelling calculation result data and simulation result data.The real time data interface module becomes higher level lanquage C# version with IP.21 Basic API interface encapsulation.
The energy optimization module is sequentially completed following steps: step a1. process data and energy consumption conversion factor are sent to the energy optimization module by the real time data interface module; Step b1. data are processed, and reject error; Step c1. judges whether to start to optimize and calculates, 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, after calculating is completed, power consumption values is passed to the optimization module, exports simultaneously the analog computation result; Step e1: adjust optimized variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in energy consumption calculation model, calculating power consumption values and turn back to the optimization module by energy consumption calculation model, after optimization was calculated and completed, result of calculation was optimized in output.
The analog computation flow process of energy consumption calculation model is as follows: step a2. reads Apsen Plus heat exchanger network model by Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB by 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, optimized variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and after completing, output analog computation result, go out to deliver to the optimization module with power consumption values simultaneously.
The optimizing flow process of energy optimization module is as follows: after the 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 Algorithms and seqential quadratic programming SQP hybrid optimization strategy, and judge whether start to optimize to calculate, if it is proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to Genetic Algorithms, 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 seqential quadratic programming SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. determines and exports the optimal scheduling scheme.
Described soft measurement module idiographic flow is as follows: the numerical value such as step a4. collecting temperature, pressure, flow; Step b4. data are processed, and reject 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 proceed step f4, if there is no corresponding measurement numerical value export simulation value and deposit real-time data base in; Step f4. compares Real-time Measuring numerical quantity and simulation value; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range the maintenance of early warning instrument, if do not exceed preset range after simulation value is replaced the Real-time Measuring numerical quantity, export simulation value and also deposit 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 by the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in browser shows dynamic flow diagram and dynamic trend figure.
Claims (5)
1. Low Temperature Thermal 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 temperature, pressure, the discharge process data that collect; Real time data is sent to the soft measurement module of low temperature heat system, and data are proofreaied and correct processing, 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 and energy consumption conversion factor, and deposits real-time data base in; The energy optimization module reads real time data by the real time data bank interface, described real time data comprises process data and model parameter, generate the real-time optimization model, then find the solution by optimized algorithm, and with real time modelling result of calculation and Real time optimal dispatch scheme, deposit real-time data base in by the real time data bank interface; The user is by application operating interface configurations simulation calculation parameter, and the generation emulator command; After the energy optimization module received emulator command, the beginning simulation calculation after calculating is completed, was sent to user interface with simulation result; Read real time data based on Flash real-time information release module by the real time data interface module, then be presented in real time in the Web process flow diagram by Flash; User interface reads Real time optimal dispatch scheme data, real time modelling calculation result data and simulation result data; Described energy optimization module is sequentially completed following steps: step a1. process data and energy consumption conversion factor are sent to the energy optimization module by the real time data interface module; Step b1. data are processed, and reject error; Step c1. judges whether to start to optimize and calculates, 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, after calculating is completed, power consumption values is passed to the optimization module, exports simultaneously the analog computation result; Step e1: adjust optimized variable according to the GA-SQP optimisation strategy: the water yield and water temperature, and import in energy consumption calculation model, calculating power consumption values and turn back to the optimization module by energy consumption calculation model, after optimization was calculated and completed, result of calculation was optimized in output; The optimizing flow process of described energy optimization module is as follows: after the 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 Algorithms and seqential quadratic programming SQP hybrid optimization strategy, and judge whether start to optimize to calculate, if it is proceed step b3, if otherwise proceed step f3; Step b3. carries out global optimizing according to Genetic Algorithms, 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 seqential quadratic programming SQP, seeks the local optimum scheduling scheme; Step e3. output local optimum scheduling scheme; Step f3. determines and exports the optimal scheduling scheme.
2. a kind of Low Temperature Thermal real-time optimization system based on GA-SQP hybrid optimization strategy according to claim 1, it is characterized in that: the flow process of the analog computation of described energy consumption calculation model is as follows: step a2. reads Apsen Plus heat exchanger network model by Aspen Plus ActiveX interface; Step b2. sends Aspen Plus heat exchanger network model into MATLAB by 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, optimized variable: the water yield and water temperature are imported energy consumption calculation model into; Steps d 2. starts calculation procedure, and after completing, output analog computation result, go out to deliver to the optimization module with power consumption values simultaneously.
3. a kind of Low Temperature Thermal real-time optimization system based on GA-SQP hybrid optimization strategy according to claim 1, it is characterized in that: the soft measurement module idiographic flow of described low temperature heat system is as follows: step a4. Real-time Measuring amount temperature, pressure, flow number; Step b4. data are processed, and reject 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 proceed step f4, if there is no corresponding measurement numerical value export simulation value and deposit real-time data base in; Step f4. compares Real-time Measuring numerical quantity and simulation value; Step g 4. judges whether simulation value exceeds preset range, if exceed preset range the maintenance of early warning instrument, if do not exceed preset range after simulation value is replaced the Real-time Measuring numerical quantity, export simulation value and also deposit real-time data base in.
4. a kind of Low Temperature Thermal 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 becomes higher level lanquage C# version with IP.21 Basic API interface encapsulation.
5. a kind of Low Temperature Thermal 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 by the real time data interface module; Web server sends to each browser with the real time data of XML form; Flash player in browser shows dynamic flow diagram and dynamic trend figure.
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Citations (5)
| 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 |
| 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 |
Family Cites Families (1)
| 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 |
-
2011
- 2011-09-07 CN CN 201110263252 patent/CN102360181B/en active Active
Patent Citations (5)
| 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 |
| 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 |
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Address after: 200127 B, 9 floor, 2 building, 428 Yanggao South Road, Pudong New Area, Shanghai. Patentee after: SHANGHAI YOUHUA SYSTEM INTEGRATION TECHNOLOGY Co.,Ltd. Patentee after: GUANGZHOU YOUHUA PROCESS TECHNOLOGY Co.,Ltd. Address before: 200127 B, 9 floor, 2 building, 428 Yanggao South Road, Pudong New Area, Shanghai. Patentee before: SHANGHAI YOUHUA SYSTEM INTEGRATION TECHNOLOGY Co.,Ltd. Patentee before: Guangzhou Youhua Process Technology Co.,Ltd. |
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