CN110728031B - Multi-objective optimization method for balancing complex petrochemical process production energy based on ANN modeling - Google Patents
Multi-objective optimization method for balancing complex petrochemical process production energy based on ANN modeling Download PDFInfo
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Abstract
The invention discloses a multi-objective optimization method for balancing complex petrochemical process productivity based on ANN modeling, which is characterized in that an MINLP model is established on the basis of ANNs models, the MINLP model comprises a logical variable compressor position and a heat exchanger position, an optimal position is determined through a GA (genetic algorithm), and then multi-objective integrated optimization is carried out at the optimal position through an NSGA-II (non-subsampled genetic algorithm-II) algorithm to obtain a pareto front edge and a key decision variable front edge. The invention can greatly reduce the operation complexity of the petrochemical industry and has important guiding significance for energy conservation and emission reduction of factories.
Description
Technical Field
The invention belongs to the technical field of process simulation, and particularly relates to a multi-objective optimization method for balancing complex petrochemical process production energy based on ANN modeling.
Technical Field
The ethylene industry is the dominant position in the petrochemical industry, about three quarters of the petrochemicals being related to ethylene products. In 2018, the yield of the Chinese ethylene is about 2330 ten thousand tons/year, and compared with the last year, the yield of the Chinese ethylene is increased by 9.6 percent and accounts for 13.8 percent of the global ethylene yield. However, the energy consumption is as high as more than 50% of the total cost, which is increased by 3.5% compared with the last year. Therefore, balancing energy consumption and production efficiency in the petrochemical industry plays a crucial role in improving energy efficiency.
At present, with the high-speed development of the computer level, modeling simulation for the petrochemical industry is mature. A number of rigorous models have been proposed. However, given the complexity of industrial processes, some assumptions are inevitable in these models. Therefore, the current optimization model can not accurately and truly reflect the actual industrial situation. On the other hand, since a strict model has a large number of differential operations, the calculation amount is large and convergence is difficult.
Disclosure of Invention
The invention provides a complex petrochemical process statistical modeling optimization method based on Artificial Neural Networks (ANNs), aiming at the problems that a complex petrochemical industry model, particularly an ethylene production industry model, is too complex, and important attributes of the process cannot be completely understood, multi-system multi-objective optimization is difficult to perform, and the like.
The technical scheme is as follows:
a multi-objective optimization method for balancing complex petrochemical process productivity based on ANN modeling is characterized in that an MINLP model is established on the basis of an ANNs model, the MINLP model comprises a logical variable compressor position and a heat exchanger position, an optimal position is determined through a GA algorithm, then multi-objective integrated optimization is carried out at the optimal position through an NSGA-II algorithm, and a pareto front edge and a key decision variable front edge are obtained.
Preferably, the method for establishing the ANNs model of the petrochemical process in the method comprises the following steps:
1): establishing a strict model;
2): obtaining uniformly distributed random input samples by utilizing a Latin hypercube sampling method:
xm,i=xm,l+(xm,u-xm,l)·PLHS
wherein xm,u,xm,lRespectively, the upper and lower limits of the variable, PLHSIs the probability distribution of Latin hypercube sampling;
3): compiling an automatic step, taking the samples obtained in the second step as the input of the strict model in sequence, automatically executing the strict model and collecting corresponding output;
4): classifying ANNs models into two categories according to whether the output is converged or not, wherein the feasibility model ANN1 is used for judging whether the input data is feasible or not; the rectification model ANN2 was used to calculate all the outputs related to the basic optimization;
5): and (5) training and verifying the ANN model.
Preferably, in the method, the method for establishing the heat capacity model considering the influence of temperature and phase change comprises the following steps:
I) heat capacity of the stream, which is not affected by temperature and whose value is only related to the composition of the stream, during the heat exchange, the following function is established to calculate the enthalpy of the stream:
wherein subscript i represents the number of streams; a is0,a1,a2… are coefficients of linear terms; b0,b1,b2… are coefficients of the mixture term; c is a constant term; m is mass fraction;
II) a stream with partial phase change, the heat capacity of the stream will change with temperature change in the heat exchange temperature range, in order to reflect the effect of the phase change on enthalpy, on the basis of group I), a gas phase fraction is introduced, the degree of phase change at different temperatures will be different, and therefore the value of the gas phase fraction will also be different, a model is established to predict the gas fraction at different temperatures:
VFRAC=A0T5+A1T4+A2T3+A3T2+A4T+A5
wherein the parameters a, b, c, d and e are coefficients of a linear term, a mixed term, a quadratic term, a cubic term and a constant term, respectively; vFRACThe gas phase fraction is shown, A is a regression coefficient of temperature and gas phase fraction, and the proved fifth-order polynomial regression effect is the best;
III) a stream with a complete phase change, exchanging only latent heat, with a temperature difference of almost zero, considering that the heat capacity does not vary with temperature during the heat exchange, and the enthalpy can be assumed as a linear relationship with temperature, calculated by the following formula:
wherein H3,OUT、H3,IN、T3,OUTAnd T3,INRespectively representing the inlet and outlet enthalpy values and the inlet and outlet temperatures of the heat exchanger under the condition III; the coefficients of the above equations can all be obtained by using least squares regression, with regression data from a validated distillation model ANN2 or rigorous model.
Preferably, in the method, the optimization framework based on the hybrid algorithm is solved:
the method comprises the following steps of using an ANN2 distillation model for calculating yields of ethylene and propylene and integrally optimizing other variables, calculating the minimum energy consumption by using a heat recovery model, firstly initializing, generating samples containing 1000 groups of operating conditions by using a feasible neural network, and calculating the optimal combination of the 1000 groups of samples by using an NSGA-II algorithm, wherein the sample with the pareto front value of 1; profit P is then calculated from diene production and energy consumption and maximized using GA algorithm.
The technical effects are as follows:
the invention provides a Multi-objective optimization method for balancing complex petrochemical process production energy based on ANN modeling, wherein a Multi-objective mixed integer nonlinear programming model (MOMINLP) is obtained on the basis of the modeling, and the model is solved through a hybrid algorithm. Pareto Frontier (PF) and corresponding Key Decision Variables (KDV) are derived that simultaneously maximize diene production and minimize energy consumption. The method can greatly reduce the operation complexity of the petrochemical industry and has important guiding significance for energy conservation and emission reduction of factories.
Drawings
Fig. 1 is a diagram of an artificial neural network.
Fig. 2 is a graph of the training results of ANN 1.
FIG. 3 is a graph of the results of ANN2 training
FIG. 4 is an optimization framework.
FIG. 5 is a flow diagram of the cryogenic separation of ethylene.
FIG. 6 is a full process modeling and optimization sketch.
In FIG. 7, (a) is a multi-objective optimization optimal leading edge; (b-k) are leading edges of manipulated variables.
In FIG. 8, (a) is a multi-objective optimization optimal leading edge; (b, c) is the leading edge of the key operating variable.
Detailed Description
The data-driven ANNs method is applied to modeling optimization of the large petrochemical industry, so that the problems can be well solved. Robustness and accuracy are essential for an integrated model for multiple system multiple targets in a petrochemical process. And the model built needs to provide enough information to represent the synergy between the entire separation process and other systems. When selected regression data and model parameters are appropriate, ANNs models can predict results that are the same as or even more accurate than rigorous distillation models. In addition, because the ANNs model is built based on engineering data, strict mechanisms and complex process limitations do not need to be considered, and therefore the ANNs model is simpler and more robust. Based on these advantages, the distillation model of the artificial neural network is more suitable as an optimization model to simulate complex petrochemical processes than a strictly simplified model.
The ANNs principle of operation stems from biological neurons that correlate input data with output data through a series of nodes. Each layer in the ans is composed of one or more neurons, each neuron having a corresponding transfer function. By adjusting the number of neural layers and meridians, the ANN can represent any linear or non-linear function with any desired precision.
FIG. 1 shows a block diagram of a typical single hidden layer feedforward neural network. The artificial neural network modeling process can be divided into the following steps:
1. data acquisition and processing, 2, model structure determination, and 3, model training and verification.
Equations (1) and (2) are algebraic expressions of the network graph, respectively.
a=f(W1x+b1) (1)
y=g(W2x+b2) (2)
Wherein: x represents the input, a represents the hidden layer output, and y represents the prediction output; w represents a weight between layers, b represents a threshold; superscripts 1 and 2 represent the hidden layer and the output layer, respectively; the transfer function f is a Sigmoid function and g is a linear function.
In order to make those skilled in the art better understand the technical solution of the present invention, the following describes in detail a multi-objective optimization method for balancing the production capacity of a complex petrochemical process based on ANN modeling, which is provided by the present invention, with reference to the following embodiments. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention.
In order to better balance the energy consumption and yield of the ethylene industry in the petrochemical process, the invention provides a modeling, analyzing and optimizing method aiming at the low-temperature separation process of complex ethylene based on an artificial neural network. On the basis of ANNs models, an MINLP model is established, a Genetic Algorithm (GA) and an NSGA-2 algorithm are combined to optimize the model, and the obtained pareto front edge and the key decision variable front edge can greatly reduce the operation complexity of the petrochemical industry and have important guiding significance for energy conservation and emission reduction of factories.
1. Petrochemical process ANNs model establishing method
The first step is as follows: and (5) establishing a strict model.
The second step is that: and (2) obtaining uniformly distributed random input samples by utilizing a Latin Hypercube Sampling (LHS) method. The principle of the method is given in equation (3).
xm,i=xm,l+(xm,u-xm,l)·PLHS (3)
Wherein xm,u,xm,lRespectively, the upper and lower limits of the variable, PLHSIs the probability distribution of latin hypercube sampling.
The third step: and compiling an automatic step, taking the samples obtained in the second step as the input of the strict model in sequence, automatically executing the strict model and collecting corresponding output.
The fourth step: ANNs models are classified into two categories depending on whether the output converges or not. The feasibility model ANN1 is used for judging whether the input data is feasible or not; the rectification model ANN2 was used to calculate all the outputs related to the basic optimization.
The fifth step: and (5) training and verifying the ANN model.
The training results for ANN1 and ANN2 are given in fig. 2 and 3, respectively. The accuracy of the two models is high, and the models can be used for later optimization.
2. Heat capacity model establishing method considering temperature and phase change influence
Thermal integration design methods typically implement simplifying assumptions, such as assuming that the heat capacity of all process streams is constant. These assumptions are only valid when there is no phase change, the temperature difference between the stream inlet and outlet is small, or the temperature has little effect on the heat capacity. However, in the complex petrochemical industry, the temperature range is large, the heat capacity of most streams is greatly affected by temperature, and other properties of the stream (e.g., density, viscosity, etc.) also vary with temperature. Therefore, an accurate model is required to calculate the heat capacity of the flow.
Process streams participating in thermal integration can generally be divided into three cases: I) no phase-changed stream during heat exchange, II) a stream with partial phase change, III) a stream with complete phase change.
For group (I), the heat capacity is not affected by the temperature when there is no phase change during the heat exchange, and its value is only related to the composition of the stream. To calculate the heat capacity of the flow, the following function is established to calculate the enthalpy of the flow:
where the subscript i represents the stream number. a is0,a1,a2… are coefficients of linear terms. b0,b1,b2… are coefficients of the mixture term. c is a constant term. M is a mass fraction.
For group (II), when a partial phase change occurs in the stream, the heat capacity of the stream will vary with temperature over the heat exchange temperature range. To reflect the effect of phase change on enthalpy, a gas phase fraction was introduced based on group (I). Since the degree of phase change will be different at different temperatures, the value of the gas phase fraction will also be different. Therefore, a model must also be built to predict the gas fraction at different temperatures.
VFRAC=A0T5+A1T4+A2T3+A3T2+A4T+A5 (7)
The parameters a, b, c, d and e in the formula (6) are coefficients of a linear term, a mixed term, a quadratic term, a cubic term and a constant term, respectively. VFRACIs the gas phase fraction, the value of which can be calculated by equation (7), a is the regression coefficient of temperature and gas phase fraction, and the best regression effect of the fifth order polynomial is verified.
For group (III), only latent heat is exchanged in this case, so the temperature difference is almost zero. Therefore, it is considered that the heat capacity does not change with temperature during the heat exchange. And the enthalpy can be assumed to be a linear relationship of temperature, which can be calculated by equation (9):
wherein H3,OUT、H3,IN、T3,OUTAnd T3,INRespectively representing the inlet and outlet enthalpy values and the inlet and outlet temperatures of the heat exchanger under the condition III. The coefficients of equations (4), (6), and (7) can all be obtained by regression using the least square method. These regression data were from a validated distillation model ANN2 or rigorous model.
3. Optimization framework based on hybrid algorithm
Among the established models for ANNs, the ANN2 distillation model was used to calculate yields of ethylene and propylene and to integrate optimization of other variables. The heat recovery model calculates the minimum energy consumption. Since the model built contains the logical variables compressor position and heat exchanger position, the model is a typical MINLP model, which is very difficult to solve. The invention combines the GA algorithm and the NSGA-2 algorithm to carry out optimization solution on the model. Firstly, determining an optimal position through GA, and then performing multi-objective integrated optimization at the optimal position through NSGA-II algorithm. FIG. 4 shows the detailed optimization steps, j and g represent the maximum number of iterations for GA and NSGA-II, respectively. i represents the position of the compressor and the heat exchanger. For each iteration in the genetic algorithm, i is updated once. The Flag is used to determine whether the genetic algorithm is finished. And when the position i of the compressor reaches the optimal position or the position j reaches the maximum iteration number, ending the genetic algorithm, wherein the Flag is 1, otherwise, continuously optimizing the position of the compressor, and the Flag is 0. When Flag is 1, then start with the optimal position ioptAnd performing multi-objective optimization. The optimization process is divided into four parts, first, initialization, and the feasible neural network is used to generate a sample containing 1000 sets of operating conditions. The second part uses a multi-objective algorithm (NSGA-II) to find the optimal combination of 1000 sets of samples (samples with a pareto front value of 1). Followed by utilization of diene yieldAnd calculating profit P by energy consumption, maximizing profit P by using GA, and returning to the optimal structure i.
Example 1
The ANNs model was established for the ethylene separation process for the ethylene plant and all modeling data was derived from the rigorous model Aspen Plus (V8.4). Figure 5 shows a flow diagram of an ethylene cryogenic separation system. Based on the ANNs model, the ethylene yield and the energy consumption are optimized in a multi-objective mode through a mixing algorithm. Equations (10), (11) are the optimized objective functions:
wherein MIN J1Denotes minimum energy consumption, MAX J2Indicating maximum ethylene production. CuAnd CWRespectively the unit price of the shaft work of the utility and the compressor. Specific data are given in table 1.
TABLE 1 Utility data and unit price
There are 16 operating variables that need to be optimized, with compressor and heat exchanger positions being logical variables and the other 15 being continuous variables. Table 2 shows the operating ranges for 15 continuous variables. Figure 6 gives a structural sketch of the entire modeling and optimization process.
TABLE 2 operating variables and their optimization Range
The optimization is carried out according to the optimization method provided by the invention, and a good optimization result is obtained. Table 3 gives the optimum operating conditions for each flash tank. FIG. 7 shows the optimal leading edge for multi-objective optimization.
TABLE 3 optimal operating conditions for the flash tank
It can be readily seen from fig. 7 that only the bottom draw of the DE column and the reflux ratio of the DE column were varied with ethylene production, while the other operating variables were constant throughout the optimization process. Thus, it is reasonable to believe that the ethylene production is only affected by the bottom draw of the DE column and the reflux ratio of the DE column, which are key operating variables. To verify this conclusion, a control experiment can be performed on the basis of fig. 7. In the control group, only the reflux ratio of the deethanizer and the bottom yield are taken as operation variables, and other variables are taken as fixed values. Figure 8 shows the results of the optimization of the control group. As can be seen from fig. 8, the optimization result when only two key operation variables are considered is almost completely consistent with the optimization result of fig. 7, but the operation difficulty is greatly reduced. By adjusting the values of the two key decision variables, the plant's ethylene production can be increased by 73164 kilograms per year, corresponding to a revenue increase of $ 95.1 ten thousand per year.
The present invention is not limited to the above-described examples, and various changes can be made without departing from the spirit and scope of the present invention within the knowledge of those skilled in the art.
Claims (1)
1. A multi-objective optimization method for balancing complex petrochemical process capacity based on ANN modeling is characterized in that an MINLP model is established on the basis of an ANNs model, the MINLP model comprises a logical variable compressor position and a heat exchanger position, an optimal position is determined through a GA algorithm, then multi-objective integrated optimization is carried out at the optimal position through an NSGA-II algorithm, and a pareto front edge and a key decision variable front edge are obtained;
the method for establishing the ANNs model in the petrochemical process comprises the following steps:
1): establishing a strict model;
2): obtaining uniformly distributed random input samples by utilizing a Latin hypercube sampling method:
xm,i=xm,l+(xm,u-xm,l)·PLHS
wherein xm,u,xm,lRespectively, the upper and lower limits of the variable, PLHSIs the probability distribution of Latin hypercube sampling;
3): compiling an automatic step, taking the samples obtained in the second step as the input of the strict model in sequence, automatically executing the strict model and collecting corresponding output;
4): classifying ANNs models into two categories according to whether the output is converged or not, wherein the feasibility model ANN1 is used for judging whether the input data is feasible or not; the rectification model ANN2 was used to calculate all the outputs related to the basic optimization;
5): training and verifying an ANN model;
the method for establishing the heat capacity model considering the temperature and the phase change influence comprises the following steps:
I) heat capacity of the stream, which is not affected by temperature and whose value is only related to the composition of the stream, during the heat exchange, the following function is established to calculate the enthalpy of the stream:
wherein subscript i represents the number of streams; a is0,a1,a2… are coefficients of linear terms; b0,b1,b2… are coefficients of the mixture term; c. CIs a constant term; m is mass fraction; h1Is not subjected to phase change at temperature T1Lower corresponding enthalpy value, CP1The specific pressure heat capacity is the specific pressure heat capacity when the phase change does not occur, and is a constant when the phase change does not occur;
II) a stream with partial phase change, the heat capacity of the stream will change with temperature change in the heat exchange temperature range, in order to reflect the effect of the phase change on enthalpy, on the basis of group I), a gas phase fraction is introduced, the degree of phase change at different temperatures will be different, and therefore the value of the gas phase fraction will also be different, a model is established to predict the gas fraction at different temperatures:
VFRAC=A0T5+A1T4+A2T3+A3T2+A4T+A5
wherein the parameters a, b, c, d and e are coefficients of a linear term, a mixed term, a quadratic term, a cubic term and a constant term respectively; vFRACThe method is characterized in that the gas phase fraction is under the temperature T when partial phase change occurs, A is a regression coefficient of the temperature and the gas phase fraction, and the fifth-order polynomial regression effect is proved to be the best; h2Is the temperature T at which the partial phase change occurs2Corresponding enthalpy value, which is no longer a linear function of temperature, CP2Is the temperature T at which the partial phase change occurs2Specific pressure heat capacity at a value of H2For T2Partial differentiation is obtained;
III) a stream with a complete phase change, exchanging only latent heat, with a temperature difference of almost zero, considering that the heat capacity does not vary with temperature during the heat exchange, and the enthalpy can be assumed as a linear relationship with temperature, calculated by the following formula:
wherein H3,OUT、H3,IN、T3,OUTAnd T3,INRespectively representing the inlet and outlet enthalpy values and inlet and outlet temperatures, CP, of the heat exchanger under the condition III3 *The specific pressure heat capacity is assumed on the basis of the previous formula, coefficients of the formula are obtained by using least square regression, and regression data are from a verified distillation model ANN2 or a strict model;
solving an optimization framework based on a hybrid algorithm: the method comprises the following steps of using an ANN2 distillation model for calculating yields of ethylene and propylene and integrally optimizing other variables, calculating the minimum energy consumption by using a heat recovery model, firstly initializing, generating samples containing 1000 groups of operating conditions by using a feasible neural network, and calculating the optimal combination of the 1000 groups of samples by using an NSGA-II algorithm, wherein the sample with the pareto front value of 1; profit P is then calculated from diene production and energy consumption and maximized using GA algorithm.
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