CN1554898A - Intelligent optimizing method for optimal synthesis of heat exchange network - Google Patents
Intelligent optimizing method for optimal synthesis of heat exchange network Download PDFInfo
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Abstract
The comprehensive optimizing method of heat exchange network belongs to the field of intelligent information processing technology. Based on system optimizing concept, the optimal synthesis problem of heat exchange network is equivalized into two parts of system structure and heat exchange unit, and the two parts are synergically optimized to obtain the optimal compromise of system structure and heat exchange unit ultimately. The proper optimizing scheme, including three steps of decomposing issue, synergic optimizing of network and selecting optimal optimizing, is adopted according to the practical conditions. The present invention solves the problem of optimal synthesis for large scale heat exchange network, and may be used in chemical, oil refining and other industrial process.
Description
Technical field
What the present invention relates to is a kind of optimization method of heat-exchange network optimal synthesis, particularly a kind of intelligent optimization method of heat-exchange network optimal synthesis.Belong to the intelligent information processing technology field.
Background technology
Heat-exchange network is the chief component that process industrial energy such as chemical industry and oil refining reclaim, heat-exchange network optimal synthesis problem is under the situation of various conditions permits, reclaims the effective energy of all hot and cold process-streams as far as possible economically, reduce the public work consumption, to reach purpose of energy saving.Its optimization aim is based on first law of thermodynamics analysis, consider all investment cost factors (heat exchange area, heat exchange unit number and structural material etc.) and all operating cost factors (public work consumption etc.) and mutual restriction relation thereof simultaneously, the form of expression is for mixing the Integer Non-Linear Programming problem.
The optimization method of heat-exchange network optimal synthesis carries out along 3 directions basically, i.e. thermodynamics rule, mathematical method and expert system application formed two big class methods: hold point design method and mathematical programming approach under the arm basically over nearly 10 years.Holding point design method under the arm is to determine holding under the arm a little of network according to thermodynamics method, weighs to determine heat-exchange network again between heat exchange unit number of devices and public work; Mathematical programming approach is by setting up the superstructure model of network, adopting mathematical method to find the solution.Find by literature search, Niniek F P is at " Journal ofChemical Engineering of Japan " (" Japanese Chemical Engineering ") (Vol.32 (3): 330-339,1998) write articles " Synthesis of Heat Exchanger Networks Considering Locationof Process Stream " (" considering the heat exchanger network synthesis problem of process-stream position ") on, this article has been studied heat-exchange network optimal synthesis problem, studies show that, hold investment cost and operating cost that point design method can not be considered network simultaneously under the arm; Though mathematical programming approach can be considered investment cost and operating cost simultaneously in model,, can only solve the problem that only limits to tens process flow thigh scales if adopt Deterministic Methods; If adopt randomization method, genetic algorithm for example, though but have characteristics such as robustness parallel processing and high efficiency, also can only solve problem less than tens stream burst scales, and easily sink into local optimum, convergence rate is slower.Because for having N
HIndividual hot-fluid thigh and N
CIndividual cold flow thigh, progression are N
KHeat-exchange network, each level has R=N at most
H* N
CIndividual heat exchanger is if the corresponding a kind of heat-exchange network structure of each matching order is then total
Individual heat-exchange network structure, along with hot and cold stream number of share of stock order increases, topology of networks will sharply increase, and obviously be a multiple shot array, belong to the NP-Hard problem.
Summary of the invention
The objective of the invention is to overcome deficiency of the prior art, a kind of intelligent optimization method of heat-exchange network optimal synthesis is provided, solving fairly large heat exchanger network synthesis problem, and can accelerate to find the solution speed, avoid local optimum effectively.
The present invention is achieved by the following technical solutions, the present invention is based on the thought of system optimization, heat-exchange network optimal synthesis problem equivalent is decomposed into system architecture optimization and heat exchange unit optimization two parts, utilize the team work evolvement method, make this two parts cooperative optimization, finally obtain the optimal compromise between system architecture and the heat exchange unit,, further select optimal prioritization scheme then according to on-site actual situations.Method specifically comprises team work optimization and three basic steps of the suitableeest prioritization scheme selection of PROBLEM DECOMPOSITION, network.
System optimization is exactly an optimization problem of considering whole network from the angle of system optimization, comprises system architecture optimization and parameter optimization two parts content.In general, system architecture determined whole network the performance that can reach; And the quality of a system architecture need be carried out parameter optimization to the heat exchange unit in the structure and judge according to object function usually.Based on this thought, can utilize the team work evolvement method to solve heat-exchange network optimal synthesis problem.During evolution, have the evolution of two kinds of species: a kind of system architecture of spore network is referred to as " leading species "; Another kind of spore heat exchange unit parameter is referred to as " species of appraising and choosing excellent ".Purpose is to utilize leading spore to guide the evolution direction of the species of appraising and choosing excellent; The utilization spore of appraising and choosing excellent is estimated the quality of the system architecture of having evolved, is the team work relation between them, finally obtains the optimal compromise between system architecture and the heat exchange unit.Because it is a plurality of that resulting optimal compromise has, so also can in conjunction with the long-term work experience, therefrom select optimal prioritization scheme according to the on-site actual situations of comprehensively not advancing object function.
Below the inventive method is further described, particular content is as follows:
● resolution problem
From the angle of system optimization, typical heat exchanger network synthesis problem equivalent is decomposed into system architecture and heat exchange unit optimization two parts.Wherein, system architecture comprises the coupling and the heat exchange unit number of required public work consumption, stream thigh; Heat exchange unit comprises thermic load, operating temperature and the heat exchange area of every heat exchanger.Compare traditional heat exchanger network synthesis problem optimization method, this method can be handled extensive heat exchange network optimization problem preferably, and can accelerate to find the solution speed.
● the team work optimization of network
The team work optimization of network comprises the system architecture and the two-part optimization of heat exchange unit of heat-exchange network, adopts the team work evolvement method, and cooperative optimization system architecture and heat exchange unit parameter are specific as follows:
(1) coding method
The individuality of leading species has been represented the system architecture of network, adopts incidence matrix to represent here, and its encoded content is a positive integer; The individuality of species of appraising and choosing excellent has been represented all heat exchange unit parameters of the heat-exchange network of a given system architecture, and its encoded content is a real number, and its individual lengths is relevant with corresponding system architecture, and this system architecture is corresponding to body one by one in the leading species.The body one by one of leading species can make up a complete heat-exchange network with the body one by one of the species of appraising and choosing excellent.
(2) adaptive value function
Only after system architecture was determined, it is just meaningful that the heat exchanging unit carries out evolutionary optimization, and the evolutionary optimization result of heat exchange unit certainly will reflect the quality of network architecture, and the simultaneity factor structure influences the evolutionary optimization of heat exchange unit again conversely.Therefore, at first the species of appraising and choosing excellent are evolved, promptly the heat exchange unit parameter of the heat-exchange network of given system architecture is evolved, its ideal adaptation value function equals the integrated objective function of heat-exchange network; For leading species, be that system architecture is evolved, its ideal adaptation value function is to be provided by best individuality in the species of appraising and choosing excellent, and promptly utilizes the species of appraising and choosing excellent to estimate the quality of the system architecture of having evolved.
(3) evolutionary optimization process
At first, determine the population scale of the leading species and the species of appraising and choosing excellent, and the random initializtion population, each population of the species of wherein appraising and choosing excellent is corresponding to the body one by one of leading species, and the individuality in each population has been represented the parameter of whole heat exchange units of the heat-exchange network of a given system architecture.
Secondly, utilize local evolution algorithm (PTA) population of species of appraising and choosing excellent of evolving, and select of the individuality representative of best individuality as this population; Utilize general evolution algorithm to evolve and dominate the population of species, wherein a complete heat-exchange network is formed in the individuality representative in the species of appraising and choosing excellent that each individuality is corresponding with this individuality, and selects best heat-exchange network as the optimization of current best heat-exchange network optimal synthesis.
When the pairing network structure of the population of the species of appraising and choosing excellent changed, utilizing increased and deletion of node method (EAN) and local evolution algorithm (PTA) population of species of appraising and choosing excellent of evolving.
A), if increased heat exchange unit, increase and the deletion of node method under, only the evolve parameter of heat exchange unit of the heat exchange unit that increases newly and place level of local evolution algorithm, and other heat exchange unit parameters remain unchanged; B) if deleted heat exchange unit, increase and the deletion of node method under, if the place level once increased heat exchange unit, delete heat exchange unit with the backward of original increase unit order so, otherwise delete cells at random.Utilize the heat exchange unit parameter of local evolution algorithm evolution and place level then; C), utilize the associated heat exchange unit parameter of local evolution algorithm evolution if the matching order of heat exchange stream thigh changes.Said method can greatly reduce the search volume, and because already present part heat exchange unit parameter remains unchanged, prevents to destroy heat-exchange network and acquired behavior.
● the suitableeest prioritization scheme of heat-exchange network is selected
Because it is a plurality of that resulting optimum prioritization scheme has, so can in conjunction with the long-term work experience, therefrom select optimal prioritization scheme according to the on-site actual situations of comprehensively not advancing object function.
The present invention has substantive distinguishing features and marked improvement.The present invention is based on system optimization thought, heat-exchange network optimal synthesis problem equivalent is decomposed into system architecture and heat exchange unit optimization two parts, utilize novel these two parts of team work evolvement method cooperative optimization, can solve fairly large heat-exchange network optimal synthesis problem preferably, and the convergence rate that can avoid existing in the optimization of traditional heat-exchange network optimal synthesis more slowly, is easily sunk into local minimum, object function and practical problem such as must be able to be led.The energy that the present invention can be applicable in the process industrials such as chemical industry, oil refining reclaims the network optimization, and has been used for the overall process using energy synthtic price index of certain factory's ethylene unit, obtains the production unit approval.
Description of drawings
The building-block of logic of Fig. 1 the inventive method.
The specific embodiment
In order to understand technical scheme of the present invention better, be further described below in conjunction with accompanying drawing and specific embodiment.
As shown in Figure 1, the building-block of logic of the inventive method.Mainly be divided into two parts among the figure: the leading spore and the spore of appraising and choosing excellent, the evolution of leading species and the evolution of the species of appraising and choosing excellent hocket, till algorithm stops.Its objective is that the evolution that utilizes the species of appraising and choosing excellent estimates the quality of the network architecture of having evolved; Conversely, utilize the evolution of leading species to guide the evolution direction of the species of appraising and choosing excellent, make in its zone that may have the optimal network system architecture in the search volume and search for, they are co-operating relations.Wherein, evolve when appraising and choosing excellent species, utilized increase and deletion of node method and local evolution algorithm, safeguard that parent in the species of appraising and choosing excellent is connected with behavior between offspring individual, has improved search efficiency.
Embodiment:
In order to verify the present invention's effect in the heat-exchange network optimal synthesis is optimized, compare with foregoing domestic and international existing method.With typical 10sp1 problem is example, and it comprises 5 strands of hot-fluids and 5 strands of cold flows, and the initial temperature of hot-fluid is respectively 416,526,544,501 and 472 degree, and the initial temperature of cold flow is respectively 355,366,311,333 and 389 degree.The optimal solution that adopts conventional method to address this problem is 49,658 dollars of annual costs, and the expense calculating formula of its all heat exchangers is 145.63S
0.6(S is the required heat exchange area of exchange specific heat).Utilize the present invention can obtain one group of prioritization scheme,, select the suitableeest a kind of prioritization scheme according to on-site actual situations, its annual cost is 42,704 dollars, adopts 7 heat exchangers, a stream burst coupling is respectively (H3, C5), (H2, C3), (H5, C1), (H3, C4), (H1, C3), (H4, C2), (H2, C1); 3 public heat exchangers of cold flow are respectively at hot-fluid thigh H3, H4 and H5.Other is used for the overall process using energy synthtic price index of the existing ethylene unit of certain factory, and its hot-fluid number of share of stock is 50, and the cold flow number of share of stock is 60, and annual cost that the present invention tries to achieve is 893762.71 dollars, and this result obtains the production unit approval.
Because the present invention does not need to consider restricted problem carrying out network architecture when optimizing, as long as found suitable system architecture expression formula, just can whether feasible task be given heat exchange unit parameter optimization algorithm with this structure of differentiation.Characteristics such as the result shows, this algorithm has intuitively, realizes easily and algorithm performance is better.
Claims (3)
1, a kind of intelligent optimization method of heat-exchange network optimal synthesis, it is characterized in that, thought based on system optimization, heat-exchange network optimal synthesis problem equivalent is decomposed into system architecture optimization and heat exchange unit optimization two parts, utilizes the team work evolvement method, make this two parts cooperative optimization, finally obtain the optimal compromise between system architecture and the heat exchange unit, according to on-site actual situations, further select optimal prioritization scheme then, method is specific as follows:
Resolution problem at first: from the angle of system optimization, the heat exchanger network synthesis problem equivalent is decomposed into system architecture and heat exchange unit optimization two parts, wherein, system architecture comprises the coupling and the heat exchange unit number of required public work consumption, stream thigh, and heat exchange unit comprises thermic load, operating temperature and the heat exchange area of every heat exchanger;
The team work optimization of network: comprise network architecture and the optimization of heat exchange unit two parts, adopt the team work evolvement method, cooperative optimization system architecture and heat exchange unit parameter;
The suitableeest prioritization scheme is selected: according to the on-site actual situations of object function, select optimal prioritization scheme from a plurality of optimum prioritization schemes.
2, the intelligent optimization method of heat-exchange network optimal synthesis according to claim 1 is characterized in that, the team work optimization of described heat-exchange network comprises following steps:
(1) coding method
The individuality of leading species has been represented the system architecture of network, adopts incidence matrix to represent here, and its encoded content is a positive integer; The individuality of species of appraising and choosing excellent has been represented all heat exchange unit parameters of the heat-exchange network of a given system architecture, and its encoded content is a real number, and its individual lengths is relevant with corresponding system architecture, and this system architecture is corresponding to body one by one in the leading species.The body one by one of leading species can make up a complete heat-exchange network with the body one by one of the species of appraising and choosing excellent;
(2) adaptive value function
At first the species of appraising and choosing excellent are evolved, promptly the heat exchange unit parameter of the heat-exchange network of given system architecture is evolved, its ideal adaptation value function equals the integrated objective function of heat-exchange network; For leading species, be that system architecture is evolved, its ideal adaptation value function is to be provided by best individuality in the species of appraising and choosing excellent, and promptly utilizes the species of appraising and choosing excellent to estimate the quality of the system architecture of having evolved;
(3) evolutionary optimization process
At first, determine the population scale of the leading species and the species of appraising and choosing excellent, and the random initializtion population, each population of the species of wherein appraising and choosing excellent is corresponding to the body one by one of leading species, and the individuality in each population has been represented the parameter of whole heat exchange units of the heat-exchange network of a given system architecture;
Secondly, utilize local evolution algorithm to evolve to appraise and choose excellent the population of species, and select best individuality to represent as the individuality of this population, utilize general evolution algorithm to evolve and dominate the population of species, wherein a complete heat-exchange network is formed in the individuality representative in the species of appraising and choosing excellent that each individuality is corresponding with this individuality, and selects best heat-exchange network as the optimization of current best heat-exchange network optimal synthesis.
3, neural network modeling approach according to claim 1 and 2, it is characterized in that, when the pairing network architecture of the population of the species of appraising and choosing excellent changes, utilize to increase and deletion of node method and the local evolution algorithm population of species of appraising and choosing excellent of evolving: as if having increased heat exchange unit, increase and the deletion of node method under, only the evolve parameter of heat exchange unit of the heat exchange unit that increases newly and place level of local evolution algorithm; If deleted heat exchange unit, under increase and deletion of node method,, then delete heat exchange unit with the backward of original increase unit order if the place level once increased heat exchange unit, otherwise delete cells at random utilizes evolve heat exchange unit parameter with the place level of local evolution algorithm then; If the matching order of heat exchange stream thigh changes, utilize the associated heat exchange unit parameter of local evolution algorithm evolution.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155860A (en) * | 2010-12-28 | 2011-08-17 | 浙江工业大学 | Method for constructing heat exchange network based on exergy consumption cost |
| CN102446299A (en) * | 2011-07-19 | 2012-05-09 | 北京三博中自科技有限公司 | Heat exchanger network analysis method for process industry |
| CN103914605A (en) * | 2012-12-31 | 2014-07-09 | 北京宜能高科科技有限公司 | Heat exchanger network optimum design method for considering stream heat capacity change |
| CN111007718A (en) * | 2019-12-12 | 2020-04-14 | 西安交通大学 | Method for determining optimal circulation ratio of heat exchange network provided with circulating reactor |
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2003
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155860A (en) * | 2010-12-28 | 2011-08-17 | 浙江工业大学 | Method for constructing heat exchange network based on exergy consumption cost |
| CN102155860B (en) * | 2010-12-28 | 2012-11-14 | 浙江工业大学 | Method for constructing heat exchange network based on exergy consumption cost |
| CN102446299A (en) * | 2011-07-19 | 2012-05-09 | 北京三博中自科技有限公司 | Heat exchanger network analysis method for process industry |
| CN102446299B (en) * | 2011-07-19 | 2014-06-25 | 北京三博中自科技有限公司 | Heat exchanger network analysis method for process industry |
| CN103914605A (en) * | 2012-12-31 | 2014-07-09 | 北京宜能高科科技有限公司 | Heat exchanger network optimum design method for considering stream heat capacity change |
| CN103914605B (en) * | 2012-12-31 | 2017-05-17 | 北京宜能高科科技有限公司 | Heat exchanger network optimum design method for considering stream heat capacity change |
| CN111007718A (en) * | 2019-12-12 | 2020-04-14 | 西安交通大学 | Method for determining optimal circulation ratio of heat exchange network provided with circulating reactor |
| CN111007718B (en) * | 2019-12-12 | 2021-04-13 | 西安交通大学 | Method for determining optimal circulation ratio of heat exchange network provided with circulating reactor |
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