CN103473597A - Method for optimizing natural gas liquefaction process technological parameters based on genetic algorithm - Google Patents
Method for optimizing natural gas liquefaction process technological parameters based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses a method for optimizing the natural gas liquefaction process technological parameters based on the genetic algorithm. According to the method for optimizing the natural gas liquefaction process technological parameters based on the genetic algorithm, on the basis of an original technological process, the genetic algorithm is introduced, a corresponding original genus, a fitness judgment formula, the cross-over rate, the aberration rate and the end condition are established for each set of parameters based on the genetic algorithm in the process of optimization of the process technological parameters, genetic algorithm optimization operation is conducted, and finally the optimal genes are obtained and used as the optimal technological parameters of the natural gas liquefaction process. Under the condition that the inherent characteristics of all devices of a system and other design regulations are met, the energy consumption generated in the natural gas liquefaction process is reduced. Meanwhile, the method for optimizing the natural gas liquefaction process technological parameters based on the genetic algorithm has the advantages of being supermatic in intelligent optimization, capable of improving the optimization efficiency, and high in global superiority and universality. According to the method for optimizing the natural gas liquefaction process technological parameters based on the genetic algorithm, the optimization range covers all the technological parameters of the process, and global optimization is really achieved through interrelation of the parameters. The method for optimizing the natural gas liquefaction process technological parameters based on the genetic algorithm can also be applied to various optimization objects, and therefore the universality of natural gas liquefaction technological parameter optimization is improved.
Description
Technical field
The present invention relates to area of natural gas liquefaction, in particular to a kind of natural gas liquefaction flow process parameter optimization method based on genetic algorithm.
Background technology
Natural gas liquefaction drops to natural gas temperature below-160 ℃ by refrigeration modes, by condensation of gas, is liquid, and 1/600 when the rock gas volume after liquefaction is original gaseous state is easy to transportation and uses.But need to consume a large amount of energy in gas deliquescence process, how to optimize the technological parameter of liquefaction flow path, the energy consumption reduced in gas deliquescence process is the emphasis of liquefaction Technology of Natural Gas research.
Natural gas liquefaction flow process parameter optimization is on original liquefaction flow path technique basis, Performance Characteristics according to unstripped gas condition and liquefaction device, the operational factor of adjustment and optimization liquefaction system is (as temperature of charge pressure, compressor pressure ratio, throttling valve pressure drop etc.), make whole liquefaction flow path Energy Intensity Reduction.
The technological parameter of natural gas liquefaction flow process is numerous, connects each other closely, so be a very complicated optimization problem to the optimization of liquefaction flow path.Often nearly ten or twenty is individual for optimized variable, and interdependence is strong, also have very various restrictive condition, and objective function is non-linear.
Present stage comprises simplicial method, gradient method, dynamic programming, branch and bound method etc. to the optimization method of natural gas liquefaction flow process.These optimization methods mostly are the gradient information based on function, and application often is subject to the restriction of solved problem.At first may select where an initial point in optimum solution, utilize the trend of function and gradient thereof, produce a series of point and converge to optimum solution.Because the initial point of selecting only has one, the search volume distributed for multimodal usually can sink into certain local unimodal extreme point, so the just locally optimal solution probably finally found.And the initial point of genetic algorithm for solving is very many, solution procedure does not rely on the gradient information of function yet, and does not require the continuity of objective function.Different from the mode of traditional algorithm single point search, genetic algorithm is processed a plurality of individualities in search volume simultaneously, and a plurality of solutions of search volume are assessed, this makes genetic algorithm have ability of searching optimum preferably, also makes genetic algorithm itself be easy to parallelization. simultaneouslyIn addition, known by the characteristic of evaluating objects function, traditional gradient method mainly produces new point by a upper point; Genetic algorithm, by genetic manipulation, produces population of future generation through intersection, variation and selection in current population.Therefore genetic algorithm can solve the optimization problem of the complexity that traditional algorithm cann't be solved.
After natural gas liquefaction flow process parameter is determined, the flow process energy consumption obtained, as objective function, has the discrete characteristics of nonlinearity height, and not too easily obtains the gradient information of objective function solution, so use common optimization method to bother very much.The natural gas liquefaction flow process also has very many process technology limit conditions; the technological parameter of single equipment can have influence on the operation operating mode of next equipment usually; traditional optimization method can't be made response to these design codes, thereby can not meet all constraint condition.
Genetic algorithm provides a kind of general framework of solving system optimization problem, by renewal and the iteration of population, searches for globally optimal solution, with other optimized algorithm, compares, and for complicated engineering problem, solves and has stronger robustness; Only utilize objective function value information, without such as gradient and other supplementary, be suitable for extensive, nonlinearity and without the objective function optimization problem of analytical expression; Search capability is strong, theoretically proof can Complete Convergence in globally optimal solution; Adopt colony's search, there is good concurrency; Adopt randomization operator rather than strict determinacy computing, can directly approach the target that solves of non-linear, multiple constraint, multi-objective optimization question; In search procedure, can constantly observe the value of technological parameter and influence each other, all genes of individuals that do not meet design code can be detected, thereby eliminate as early as possible by the way of giving penalties the individuality that does not meet design code.When these characteristics make genetic algorithm be applied to the rock gas flow process to optimize this class complex engineering problems, can show outstanding computing power.
Summary of the invention
The invention provides a kind of natural gas liquefaction flow process parameter optimization method based on genetic algorithm, under the condition in order to the inherent characteristic of all devices in guaranteeing to meet system and other design codes, reduced the energy consumption in the gas deliquescence process.
For achieving the above object, the invention provides a kind of natural gas liquefaction flow process parameter optimization method based on genetic algorithm, comprise the following steps:
Step a: determine optimization object according to parameter relevant to energy consumption in gas deliquescence process, the objective function using optimization object as genetic algorithm, and determine the parameter that will optimize according to optimization object;
Step b: according to the technical standard of natural gas liquefaction and the device characteristics of each parameter representative, set the initialization factor of genetic algorithm and the upper and lower limit of the parameter that each will be optimized, the wherein initialization factor comprises group size, evolutionary generation, ideal adaptation degree evaluation function, Crossover Operator, mutation operation operator, crossover probability and variation probability;
Step c: according to characteristic and the flow process requirement of equipment that natural gas liquefaction process is used, for the special parameter in the parameter that will optimize or a plurality of relevant parameter arrange constraint condition;
Steps d: according to group size, in the upper and lower limit scope of the parameter that will optimize at each, the uniform design value forms gene, and by the assortment of genes corresponding to parameter that will optimize to some extent form individuality together, obtain initial population;
Step e: whether the relevant parameter of differentiating one by one the individuality formed meets constraint condition, if do not meet give penalties for this individuality, if meet calculate this individual fitness function value by ideal adaptation degree evaluation function, penalties should, far above the average fitness functional value of population, not be eliminated so that do not meet the individuality of constraint condition as early as possible;
Step f: after all individualities of former generation all obtain fitness function value or penalties, adopt linear ordering operator and elitist selection strategy to select to set the individuality of number, according to crossover probability and variation probability, adopt crossover operator and mutation operator form new gene and are combined to form new individuality, form population of future generation;
Step g: whether the individuality in the new population formed of judgement has met end condition, each parameter of the individuality correspondence with optimum fitness function value that end condition will finally obtain if met is as the parameter combinations of natural gas liquefaction process flow process, otherwise repeated execution of steps e and step f, wherein end condition reaches optimum fitness function value for the individuality in this population or the genetic algebra of this population reaches the termination genetic algebra.
Optionally, in the above-described embodiments, using the value of feedback of optimization object as the fitness of population, by the decoding formula, each individuality in the population of choosing decode and formed a set of technological parameter, bring the value of the corresponding optimization object of calculating in liquefaction flow path figure PFD into.
Optionally, in the above-described embodiments, penalties is set to 1.5-3 times of current population average fitness.
Optionally, in the above-described embodiments, the individuality in this population reaches optimum fitness function value and refers to that the variation of the fitness function value of the optimum of individuality in this population is in setting range.
In the above-described embodiments, on original technological process basis, introduce genetic algorithm, the flow process technological parameter is being optimized, utilizing genetic algorithm is that each group parameter is set up corresponding initial population, fitness is passed judgment on formula, crossing-over rate, aberration rate and end condition, carry out the genetic algorithm optimization operation, finally obtain optimal base because of the technological parameter as the natural gas liquefaction flow process, in guaranteeing to meet system, under the condition of the inherent characteristic of all devices and other design codes, reduced the energy consumption in the gas deliquescence process; There are supermatic intelligent optimization characteristics simultaneously, alleviate slip-stick artist's Optimization Work amount, improved optimization efficiency; The method also has the of overall importance and versatility of height, optimization range has covered all technological parameters of flow process, utilize connecting each other between parameter, really realized global optimization, and can be multiplexing by various optimization objects institute, increased the versatility of natural gas liquefaction process parameter optimization of the present invention.
The accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, below will the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The natural gas liquefaction flow process parameter optimization method process flow diagram based on genetic algorithm that Fig. 1 is one embodiment of the invention;
Fig. 2 is the single-stage mix refrigerant liquefaction flow path schematic diagram of an embodiment of invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not paying under the creative work prerequisite the every other embodiment obtained, belong to the scope of protection of the invention.
Natural gas liquefaction flow process parameter optimization method of the present invention is based upon on the basis of genetic algorithm, genetic algorithm is the computation model of the biological evolution process of the natural selection of simulation Darwin theory of biological evolution and science of heredity mechanism, it is a kind of optimization method be based upon on random device, it utilizes random algorithm to generate the primitive solution of some, iterative operation by every generation population, by these primitive solutions intersected, breeding and mutation operation, then eliminate ropy individuality, make last solution slowly close to optimum solution.Genetic algorithm is applicable to the discrete problem optimization of this non-linear height of natural gas liquefaction process optimization very much.In optimizing process, give penalties to the individuality that does not meet process stipulation, guarantee that all populations all meet designing requirement, for the individuality that meets process stipulation calculates fitness, and according to ranking fitness selected, crossover and mutation operation, so circulation is until obtain the final optimization pass result.This optimization method only utilizes objective function value information, without such as gradient and other supplementarys, is applicable to very much the objective function optimization problem of this multiparameter of natural gas liquefaction process optimization, non-linear, high dispersion.And the method do not rely on any subjective factor, there is the objectivity of height, the slip-stick artist only need provide the natural air-air source condition, and device attribute and design code just can carry out the Optimized Matching of selected parameter automatically by the method.Simultaneously this method has very high efficiency, can be in iteration repeatedly of shorter time, thus obtain fast optimum solution.
The natural gas liquefaction flow process parameter optimization method process flow diagram based on genetic algorithm that Fig. 1 is one embodiment of the invention; As shown in the figure, this process parameter optimizing method specifically comprises the following steps:
Step a: determine optimization object according to parameter relevant to energy consumption in gas deliquescence process, the objective function using optimization object as genetic algorithm, and determine the parameter that will optimize according to optimization object;
Wherein, optimization object can be constructed according to parameter relevant to energy consumption in gas deliquescence process, for example, optimization object can be the ratio power consumption in the natural gas liquefaction flow process, be the power consumption of compressor and the ratio of the flow of rock gas, like this, according to than power consumption, can determine that compressor pressure ratio, throttling valve pressure drop and cryogen component in the natural gas liquefaction process parameter is as the Optimal Parameters variable.
Step b: according to the technical standard of natural gas liquefaction and the device characteristics of each parameter representative, set the initialization factor of genetic algorithm and the upper and lower limit (UB, LB) of the parameter that each will be optimized, the wherein initialization factor comprises group size M, evolutionary generation G, ideal adaptation degree evaluation function F, Crossover Operator c, mutation operation operators m, crossover probability C and variation probability M;
Step c: according to characteristic and the flow process requirement of equipment that natural gas liquefaction process is used, for the special parameter in the parameter that will optimize or a plurality of relevant parameter arrange constraint condition Ct;
Wherein, by constraint condition Ct, can make all individualities meet the technological design regulation, this regulation can be that equipment and technology requirement, gas source condition requirement, design code requirement and all can be at PFD(Process Flow Diagram, process stream state diagrams) in figure by the constraint condition of parameter expression.Comprise 3-20 optimizable technological parameter in natural gas liquefaction flow process PFD figure, for determining the main technique operating point under this operating mode.
Steps d: according to group size M, in the upper and lower limit of the parameter that will optimize at each (UB, LB) scope, the uniform design value forms gene, and by the assortment of genes corresponding to parameter that will optimize to some extent form individuality together, obtain initial population Pini;
Step e: whether the relevant parameter of the individuality differentiate formed one by one meets constraint condition Ct, if do not meet give penalties for this individuality, if satisfied calculate this individual fitness function value by ideal adaptation degree evaluation function F;
Wherein, when giving penalties, can penalties be set to the 1.5-3 of current population average fitness doubly, be eliminated faster in order to meet the technological parameter of constraint condition.
Step f: after all individualities of former generation all obtain fitness function value or penalties, adopt linear ordering operator and elitist selection strategy to select to set the individuality of number, according to crossover probability C and variation probability M, adopt crossover operator c and mutation operator m form new gene and are combined to form new individuality, form population of future generation;
Step g: whether the individuality in the new population formed of judgement has met end condition, each parameter of the individuality correspondence with optimum fitness function value that end condition will finally obtain if met is as the parameter combinations of natural gas liquefaction process flow process, otherwise repeated execution of steps e and step f, wherein end condition reaches optimum fitness function value for the individuality in this population or the genetic algebra of this population reaches the termination genetic algebra.
Wherein, the individuality in this population reaches the variation of fitness function value that optimum fitness function value refers in this population individual optimum in setting range, and for example rate of change is no more than 2%, or changing value is no more than certain setting value.
Again for example, in the above-described embodiments, can also using the value of feedback of optimization object as the fitness of population, by the decoding formula, each individuality in the population of choosing decode and formed a set of technological parameter, bring the value of the corresponding optimization object of calculating in liquefaction flow path figure PFD into.
Below to take single-stage mix refrigerant liquefaction flow path (SMR) be example, carry out the explanation of specific implementation method.
Fig. 2 is the single-stage mix refrigerant liquefaction flow path schematic diagram of an embodiment of invention.In the embodiment of Fig. 2, according to scaling scheme, adopt the SMR flow process of the simplest mono heat exchanger, single throttling valve, single compressor to carry out analogue simulation.In this embodiment, adopt four kinds of cryogens to form azeotrope MR, enter ice chest L cooling after compressor K compression, after cooling box L, by after throttling valve V step-down cooling, again enter ice chest L cold is provided.The raw natural gas NG of gaseous state has become the liquefied natural gas (LNG) LNG of-148 ℃ after ice chest.
The natural gas liquefaction flow process parameter optimization method based on genetic algorithm of Fig. 2 embodiment comprises the following steps:
Step 201: the ratio power consumption of usining in the natural gas liquefaction flow process is as optimization object OT, i.e. the ratio of the power consumption of compressor and the flow of rock gas; According to than power consumption, using the compressor pressure ratio in technological parameter, throttling valve pressure drop and four kinds of cryogen components as optimized variable.
Step 202: the variable individuality based on step 201 obtains initial population by random device.Population quantity is made as 50, and genetic algebra is made as 100, and crossing-over rate is made as 0.5, and aberration rate is made as 0.01.Fitness function obtains by the ratio power consumption calculation of flow process.
Step 203: the corresponding device characteristics according to technological parameter are each variable set up bound scope and constraint condition.
Step 204: to 50 individual fitness that calculate of initial population, to the individuality that does not meet constraint condition, give penalties successively, then sorted by numerical values recited.Adopt linear ordering operator and elitist selection strategy to select to retain outstanding individual inheritance to of future generation.
Step 205: the population to new generation carries out the crossover and mutation genetic manipulation according to crossing-over rate and aberration rate, and produces population of new generation.
Step 206: repeat the operation of 204-205, until reach, stop evolutionary generation 100, obtain the individuality of high fitness, using this, individual gene is as the Optimizing Process Parameters of natural gas liquefaction flow process.
In addition, due to the Genetic algorithm searching result, with certain randomness, can utilize above-mentioned steps to carry out repeatedly (as 5 times) experiment for each objective optimization object, and then the technological parameter using the optimal value of many experiments result as the natural gas liquefaction flow process.
In the above-described embodiments, on original technological process basis, introduce genetic algorithm, the flow process technological parameter is being optimized, utilizing genetic algorithm is that each group parameter is set up corresponding initial population, fitness is passed judgment on formula, crossing-over rate, aberration rate and end condition, carry out the genetic algorithm optimization operation, finally obtain optimal base because of the technological parameter as the natural gas liquefaction flow process, in guaranteeing to meet system, under the condition of the inherent characteristic of all devices and other design codes, reduced the energy consumption in the gas deliquescence process; There are supermatic intelligent optimization characteristics simultaneously, alleviate slip-stick artist's Optimization Work amount, improved optimization efficiency; The method also has the of overall importance and versatility of height, optimization range has covered all technological parameters of flow process, utilize connecting each other between parameter, really realized global optimization, and can be multiplexing by various optimization objects institute, increased the versatility of natural gas liquefaction process parameter optimization of the present invention.
One of ordinary skill in the art will appreciate that: accompanying drawing is the schematic diagram of an embodiment, and the module in accompanying drawing or flow process might not be that enforcement the present invention is necessary.
One of ordinary skill in the art will appreciate that: the module in the device in embodiment can be described and be distributed in the device of embodiment according to embodiment, also can carry out respective change and be arranged in the one or more devices that are different from the present embodiment.The module of above-described embodiment can be merged into a module, also can further split into a plurality of submodules.
Finally it should be noted that: above embodiment only, in order to technical scheme of the present invention to be described, is not intended to limit; Although with reference to previous embodiment, the present invention is had been described in detail, those of ordinary skill in the art is to be understood that: its technical scheme that still can put down in writing previous embodiment is modified, or part technical characterictic wherein is equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution break away from the spirit and scope of embodiment of the present invention technical scheme.
Claims (4)
1. the natural gas liquefaction flow process parameter optimization method based on genetic algorithm, is characterized in that, comprises the following steps:
Step a: determine optimization object according to parameter relevant to energy consumption in gas deliquescence process, the objective function using described optimization object as genetic algorithm, and determine the technological parameter that will optimize according to described optimization object;
Step b: according to the technical standard of natural gas liquefaction and the device characteristics of each parameter representative, set the initialization factor of genetic algorithm and the upper and lower limit of the parameter that each will be optimized, the wherein said initialization factor comprises group size, evolutionary generation, ideal adaptation degree evaluation function, Crossover Operator, mutation operation operator, crossover probability and variation probability;
Step c: according to characteristic and the flow process requirement of equipment that natural gas liquefaction process is used, for the special parameter in the parameter that will optimize or a plurality of relevant parameter arrange constraint condition;
Steps d: according to described group size, in the upper and lower limit scope of the parameter that will optimize at each, the uniform design value forms gene, and by the assortment of genes corresponding to parameter that will optimize to some extent form individuality together, obtain initial population;
Step e: whether the relevant parameter of the individuality differentiate formed one by one meets described constraint condition, if do not meet give penalties for this individuality, if satisfied calculate this individual fitness function value by described ideal adaptation degree evaluation function;
Step f: after all individualities of former generation all obtain fitness function value or penalties, adopt linear ordering operator and elitist selection strategy to select to set the individuality of number, according to described crossover probability and described variation probability, adopt described crossover operator and described mutation operator form new gene and are combined to form new individuality, form population of future generation;
Step g: whether the individuality in the new population formed of judgement has met end condition, each parameter of the individuality correspondence with optimal-adaptive degree functional value that described end condition will finally obtain if met is as the Optimal Parameters combination of natural gas liquefaction process flow process, otherwise repeated execution of steps e and step f, wherein said end condition reaches optimum fitness function value for the individuality in this population or the genetic algebra of this population reaches the termination genetic algebra.
2. natural gas liquefaction flow process parameter optimization method according to claim 1, it is characterized in that, using the value of feedback of described optimization object as the fitness of population, by the decoding formula, each individuality in the population of choosing is decoded and formed a set of technological parameter, bring the value of calculating corresponding optimization object in liquefaction flow path figure PFD into.
3. natural gas liquefaction flow process parameter optimization method according to claim 1, is characterized in that, described penalties is set to 1.5-3 times of current population average fitness.
4. natural gas liquefaction flow process parameter optimization method according to claim 1, is characterized in that, the individuality in described this population reaches optimum fitness function value and refers to that the variation of the fitness function value of the optimum of individuality in this population is in setting range.
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| CN110704956A (en) * | 2019-08-09 | 2020-01-17 | 太原科技大学 | Cold rolling mill data-driven technological parameter optimization method |
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Application publication date: 20131225 |