Energy consumption prediction method for lyocell fiber glue making process and application

Abstract

The invention relates to the technical field of energy consumption prediction, in particular to an energy consumption prediction method for a lyocell fiber glue making process and application. Historical original data are collected and processed, and historical data are obtained; calculating process similarity according to the process sheet to obtain historical sorting data; selecting reference group sequences, calculating the process similarity of each reference group sequence and the threshold interval of the process similarity, and then calculating to obtain an energy consumption predicted value sequence of the reference group sequences; comparing the energy consumption predicted value sequence with historical energy consumption data, and recording an absolute error sum; increasing the threshold interval percentage of the process similarity to obtain the threshold interval percentage of the corresponding process similarity when the absolute error sum is minimum; taking the historical screening and sorting data as a training set of a neural network for training to obtain an energy consumption prediction model; inputting the process parameter data and the intermediate parameter data into an energy consumption prediction model to obtain an energy consumption prediction value; the scale of the training set is controlled, and the credibility and accuracy of model prediction are improved.

Description

Technical Field

[0001] The invention relates to the technical field of energy consumption prediction, and in particular to an energy consumption prediction method and application of a lyocell fiber glue making process. Background Art

[0002] As a new type of cellulose fiber, lyocell fiber uses wood and bamboo in nature as raw materials. Its manufacturing process is green and environmentally friendly. It started late in China but has developed rapidly. Its glue making process is a key link in the production process of lyocell fiber, which directly affects the quality and performance of the fiber. Steam and electricity energy consumption account for a large proportion of the cost of the lyocell glue making process. Accurate prediction of them can help enterprises optimize production processes, reduce costs and improve market competitiveness. At present, there are few studies on energy consumption prediction in the production process of lyocell fiber, and no complete technical methods have been formed. As for the direction of energy consumption prediction, it is mostly concentrated in the fields of construction and equipment, and the prediction object is mainly electricity. At the same time, these energy consumption prediction methods often regard energy consumption data as time series data, combine environmental parameters, and use time prediction algorithms to make real-time dynamic predictions, but lack attention to the static prediction of energy consumption under a given process order. The lack of energy consumption prediction under a given process order makes it impossible for the power supply and gas supply related departments of the enterprise to provide future power and gas consumption plan reports to cope with sudden power outages and gas outages during peak power and gas consumption in the future, which requires more energy consumption.

[0003] The energy consumption prediction related technologies in the existing technology all input a large amount of historical data as a training set to train the model, and then use the model to obtain energy consumption prediction results; however, in the technical field of lyocell fiber glue making process, if the energy consumption prediction of the lyocell fiber glue making process is to train all the collected historical parameters as the model training set, it will result in the input of too many historical parameters with low similarity to the current process unit of the lyocell fiber glue making process, resulting in low model prediction accuracy and inability to perform accurate energy consumption prediction.

[0004] For example, a Chinese patent, whose publication number is CN115062847A, and whose publication date is September 16, 2022, is entitled "A method and system for predicting carbon fiber heating energy consumption based on logistic regression algorithm". The specific technical scheme is as follows: Real-time collection of energy consumption and environmental data of carbon fiber electric heating equipment in the target area to obtain factor data, and the factor data is used to characterize the data that affects the electricity consumption of carbon fiber electric heating equipment; the factor data is loaded into a pre-established and trained energy consumption prediction model to obtain a heating energy consumption prediction result; wherein, the establishment and training process of the energy consumption prediction model specifically includes: obtaining factor data for training; inputting the factor data for training into the prediction function, and updating the parameters in the prediction function according to the gradient descent method until the training is completed; using the trained prediction function as an energy consumption prediction model to perform energy consumption prediction on the factor data collected in real time.

[0005] The above-mentioned patent completes the training of the energy consumption prediction model by collecting and acquiring the factor data used for training, and inputs the factor data into the energy consumption prediction model to obtain the final prediction result. However, the above-mentioned patent does not process the factor data used for training, which will lead to a decrease in the accuracy of the energy consumption prediction model obtained by training, resulting in low accuracy of the obtained energy consumption prediction result. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the present invention provides an energy consumption prediction method and application of a lyocell fiber glue making process, which can ensure the accuracy of energy consumption prediction of the lyocell fiber glue making process, control the training set size and reduce the input parameters of the neural training process, thereby preventing overfitting.

[0007] In order to achieve the above technical effects, the technical solution of this application is as follows: In a first aspect, a method for predicting energy consumption in a lyocell fiber glue making process comprises the following specific steps: Step 1: Collect the historical raw data of evaporation system parameter data, glue and sealing system parameter data, insulation water and vacuum system parameter data, slurry weight, glue weight, glue refractive index, consumption time and energy consumption data during the Lyocell fiber glue making process, and perform data preprocessing to obtain historical data; Step 2: Calculate the process similarity of the corresponding historical data according to the current process order, and rearrange the historical data according to the process similarity to obtain historical sorted data; Step 3: Select a reference group sequence from the historical sorting data, calculate the process similarity of each reference group sequence, obtain the historical sorting data sequence, and find the threshold interval of the process similarity. Then use the historical sorting data sequence within the threshold interval to calculate the energy consumption prediction value sequence; Step 4: Compare the energy consumption forecast value sequence with the historical energy consumption data of the corresponding reference group sequence, and record the absolute error and X; Step 5: Increase the threshold interval percentage P of process similarity to obtain the threshold interval of the current process similarity; repeat steps 3 to 4 until the threshold interval of the current process similarity reaches the set maximum value, and obtain the threshold interval percentage P of process similarity corresponding to the minimum absolute error and X, and mark P as P s ; Step 6: Pass the historical sorting data through P s The historical screening and sorting data are obtained by screening, and the porridge weight, glue liquid weight, glue liquid refractive index and consumption time are used as the training set of the neural network, and the energy consumption prediction model is obtained after training; Step seven: Extract the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 from the process list, and input them into the constructed calculation model to obtain the porridge weight, glue weight, glue refractive index and consumption time, and input them into the energy consumption prediction model to obtain the energy consumption prediction value.

[0008] Furthermore, in step seven, the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 are extracted from the process list and input into the constructed calculation model to obtain the porridge weight, glue liquid weight, glue liquid refractive index and consumption time, and input into the energy consumption prediction model. The specific method to obtain the energy consumption prediction value is as follows: extract the porridge weight from the process list, compare the porridge weight with the process parameter group 1 extracted from the process list, and obtain the glue liquid weight through the glue liquid weight calculation model constructed based on the BP neural network algorithm, compare the porridge weight with the process parameter group 3 extracted from the process list, and obtain the consumption time through the glue making consumption time calculation model constructed based on the least squares fitting nonlinear parameter algorithm, obtain the glue liquid refractive index through the glue liquid refractive index calculation model constructed based on the BP neural network algorithm from the process parameter group 2 extracted from the process list, and then input the porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model, and finally obtain the energy consumption prediction value.

[0009] Furthermore, historical original data include historical original process parameter data, historical original energy consumption data and historical original intermediate parameter data; historical data include historical process parameter data, historical energy consumption data and historical intermediate parameter data; historical sorted data include historical sorted process parameter data, historical sorted intermediate parameter data and historical sorted energy consumption data; historical filtered and sorted data include historical filtered and sorted process parameter data, historical filtered and sorted intermediate parameter data and historical filtered and sorted energy consumption data.

[0010] Furthermore, the process sheet includes process parameter data.

[0011] Furthermore, the process parameter data include evaporation system parameter data, glue liquid and sealing system parameter data, insulation water and vacuum system parameter data and porridge weight; the evaporation system parameter data include evaporation heating system parameter data, evaporation dissolving machine parameter data and evaporation condensation water system parameter data; the glue liquid and sealing system parameter data include sealing liquid system parameter data and glue liquid transportation parameter data; the insulation water and vacuum system parameter data include insulation water system parameter data and vacuum system parameter data.

[0012] Furthermore, the evaporation heating system parameter data includes the heating system temperature, the heating system liquid level and the heating system pressure; the evaporation dissolving machine parameter data includes the evaporation dissolving machine four-zone temperature, the evaporation dissolving machine motor speed, the evaporation dissolving machine bearing lubricating oil circulation temperature, the reducer bearing speed, the cooling fan speed, the evaporation dissolving machine liquid level, the evaporation dissolving machine outlet pressure and the evaporation dissolving machine bottom outlet temperature; the evaporation dissolving machine four-zone temperature includes the four evaporation zone jacket temperature and the four evaporation zone glue temperature; the glue delivery parameter data includes the temperature of the insulation water heat exchanger in the pump, the flow rate of the insulation water heat exchanger in the pump, the insulation water heat exchanger outside the pump, the flow rate of the insulation water heat exchanger inside the pump, the flow rate of the insulation water heat exchanger outside ... The data include the temperature of the water heat exchanger, the flow rate of the insulation water heat exchanger outside the pump, the speed of the glue delivery pump, the outlet pressure of the glue delivery pump and the outlet temperature of the glue delivery pump; the parameter data of the sealing liquid system include the temperature of the sealing liquid tank, the sealing liquid flow rate and the liquid level of the sealing liquid tank; the parameter data of the insulation water system include the outlet temperature of the insulation water circulation pump and the hot water pressure of the insulation system; the parameter data of the evaporative condensing water system include the temperature of the evaporative condenser, the liquid level of the evaporative condenser, the pressure of the evaporative condenser, the flow rate of cooling water to the evaporative condenser and the outlet flow rate of the evaporative condensing liquid pump; the parameter data of the vacuum system include the liquid level of the vacuum system, the pressure of the vacuum system and the speed of the vacuum pump.

[0013] Furthermore, the specific steps of step seven are as follows: Step a1: Based on the BP neural network algorithm, a glue liquid weight calculation model is constructed; the specific construction method of the glue liquid weight calculation model is: the porridge weight, the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed in the historical screening and sorting process parameter data are used as the input features of the glue liquid weight calculation model training set, and the glue liquid weight in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid weight calculation model training set; Step b1: Based on the BP neural network algorithm, a glue liquid refractive index calculation model is constructed; the specific construction method of the glue liquid refractive index calculation model is: the motor speed of the evaporation dissolution machine, the temperature of the four zones of the evaporation dissolution machine, the speed of the vacuum pump and the outlet temperature of the insulation water circulation pump in the historical screening and sorting process parameter data are used as the input features of the glue liquid refractive index calculation model training set, and the glue liquid refractive index in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid refractive index calculation model training set; Step c1: constructing a glue-making time calculation model based on a least squares fitting nonlinear parameter algorithm; the specific construction method of the glue-making time calculation model is as follows: using the porridge weight, the evaporation dissolution machine motor speed and the vacuum pump speed in the historically screened and sorted process parameter data as input features of the glue-making time calculation model, and using the consumption time in the historically screened and sorted intermediate parameter data as the target output of the glue-making time calculation model; Step d1: extract the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 from the process list; input the porridge weight and process parameter group 1 into the glue liquid weight calculation model to obtain the glue liquid weight, input the process parameter group 2 into the glue liquid refractive index calculation model to obtain the glue liquid refractive index, and input the porridge weight and process parameter group 3 into the glue making consumption time calculation model to obtain the consumption time, and finally input the porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to obtain the energy consumption prediction value; the process parameter group 1 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed; the process parameter group 2 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum pump speed and the insulation water circulation pump outlet temperature; the process parameter group 3 includes the evaporation dissolution machine motor speed and the vacuum pump speed.

[0014] Furthermore, in the step a1, the number of input layer neurons of the glue weight calculation model is set to 8, and the number of output layer neurons is 1; there are two hidden layers, the number of neurons in the first layer is 5, and the number of neurons in the second layer is 4; the activation function is the tanh activation function; the loss function is the MSE; the initial weight is randomly generated using a Gaussian distribution with a mean of 0 and a standard deviation of 0.01; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to three decimal places.

[0015] Furthermore, in the step b1, the number of input layer neurons of the glue refractive index calculation model is set to 8, and the number of output layer neurons is set to 1; there are two hidden layers, the number of neurons in the first layer is 6, and the number of neurons in the second layer is 4; the activation function is the tanh activation function; the loss function is the MSE; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to four decimal places.

[0016] Furthermore, the expression of the calculation model of the glue consumption time is as follows: ; In the formula, t is the time consumed by the glue making process; a, b and e are parameters to be fitted; is the motor speed of the evaporation dissolution machine; is the vacuum pump speed; It is the weight of porridge.

[0017] Furthermore, the intermediate parameter data include glue weight, glue refractive index and consumption time.

[0018] Furthermore, the specific steps of data preprocessing in step 1 are as follows: Step a: Eliminate outliers based on the 3σ principle; Step b: After outliers are removed, the missing values ​​are filled by taking the weighted average of the K neighboring data values ​​near the missing value.

[0019] Furthermore, in step a, outliers are eliminated based on the 3σ principle, and the expression is as follows: ; In the formula, is the mean of each column of historical original data, is the standard deviation of each column of historical raw data. The specific calculation formula is as follows: ; ; In the formula, is the number of historical raw data in each column, It is the numerical value of each column of historical original data.

[0020] Furthermore, the process similarity in step 2 The cosine similarity is used for calculation, and the specific calculation formula is: ; In the formula, It is A vector consisting of the process parameter data of the row history; It is a vector consisting of the current process order data.

[0021] Furthermore, the specific method of selecting the reference group from the historical sorting data in step 3 is: the first m rows of the historical sorting data form a reference group sequence ; By reference group sequence For each sequence in the reference group, we obtain the historical sorting data corresponding to each sequence in the reference group. , thus forming the historical sorting data sequence of the reference group sequence , and record the maximum process similarity value in the process ; The threshold interval of process similarity in step 3 , calculate the minimum process similarity value of the threshold interval The specific calculation formula is as follows: ; In the formula, Indicates the initial value of the threshold interval percentage of the set process similarity; Furthermore, in step three, the historical sorted data sequence within the threshold interval is used to calculate the energy consumption prediction value sequence in the following specific manner: the porridge weight of the historical sorted intermediate parameter data and the historical sorted process parameter data in the historical sorted data sequence of the reference group sequence are used as the training set of the neural network model, thereby obtaining the energy consumption prediction value sequence of the reference group sequence; the process similarity corresponding to the historical sorted intermediate parameter data belongs to the threshold interval of the process similarity.

[0022] Furthermore, before using the porridge weight of the historically sorted intermediate parameter data and the historically sorted process parameter data as the training set of the neural network model for training, it is necessary to eliminate the difference between the input data dimensions through homogenization processing. The specific calculation formula is: ; In the formula, It is the result of the uniform processing of the i-th row and j-th column of the training set; is the original data of row i and column j of the training set; is the minimum value of the process parameters in the jth column of the training set; is the maximum value of the process parameters in the jth column in the training set.

[0023] Furthermore, the neural network model specifically uses a three-layer BP neural network, with 4 neurons in the input layer; 4 neurons in the output layer; 1 hidden layer with 4 neurons; the Tanh activation function is used as the activation function; the MSE is used as the loss function; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to five decimal places.

[0024] Furthermore, the specific calculation formula for increasing the threshold interval percentage P of process similarity in step 5 is: ; In the formula, P represents the threshold interval percentage of the current process similarity; represents the initial value of the threshold interval percentage of the set process similarity; S represents the step value of the threshold interval percentage of the set process similarity; n represents the current step number; The threshold interval of the current process similarity is : ; In the formula, Indicates the current minimum process similarity value; Indicates the maximum process similarity value.

[0025] Furthermore, in step six, the training set is homogenized to eliminate the differences between the dimensions of the input data.

[0026] Furthermore, in the step six, the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network. The specific method of obtaining the energy consumption prediction model after training is: the glue weight, glue refractive index and consumption time in the intermediate parameter data of historical screening and sorting and the porridge weight in the process parameters of historical screening and sorting are used as input features of the training set of the energy consumption prediction model, and the energy consumption data of historical screening and sorting are used as the target output of the energy consumption prediction model; the energy consumption prediction model uses a three-layer BP neural network, the number of neurons in the input layer is 4, the number of neurons in the output layer is 1; the hidden layer is 1, and the number of neurons in this layer is 4.

[0027] Furthermore, the energy consumption data is the sum of electricity energy consumption and steam energy consumption; the specific calculation formula for converting steam energy consumption into electricity energy consumption is as follows: ; In the formula, The electricity consumption for steam; is the price of steam, in yuan / m 3 ; is the price of electric energy, in Yuan / KW·h; is the amount of steam used, in m 3 .

[0028] Furthermore, the specific method in step 4 is: compare the energy consumption prediction value sequence of the reference group sequence with the historical energy consumption data of the corresponding reference group sequence, record their absolute error values, traverse the m rows of historical sorted data sequence, and obtain the energy consumption prediction absolute error value sequence , accumulate the energy consumption prediction absolute error value sequence to obtain the absolute error and X and record it.

[0029] In a second aspect, an application of an energy consumption prediction method for a lyocell fiber glue making process is applicable to energy consumption prediction for a lyocell fiber glue making process.

[0030] According to the above technical solution, the beneficial effects of this application are as follows: 1. Since steam energy consumption and electricity energy consumption account for a large proportion of the cost of the Lyocell fiber glue making process, the method of the present invention is used to accurately predict the energy consumption data, which helps enterprises to assist in optimizing the production process of Lyocell fibers and reduce the technical effect of the cost of the Lyocell fiber glue making process.

[0031] 2. The method adopted by the present invention controls the size of the training set by calculating the most suitable threshold interval percentage of process similarity, thereby improving the credibility and accuracy of the model prediction.

[0032] 3. The method adopted by the present invention screens out historical screening and sorting data, and uses the historical screening and sorting intermediate parameter data and the historical screening and sorting energy consumption parameter data in the historical screening and sorting data as training sets of the neural network. An energy consumption prediction model is obtained through neural network training. Compared with the prior art that uses a large amount of data as the training set of the neural network model, the present method only uses the screened historical screening and sorting data, which effectively reduces the complexity of the model and improves the fitting ability and generalization ability of the model.

[0033] 4. The method adopted in the present invention constructs a glue liquid weight calculation model, a glue liquid refractive index calculation model and a glue making consumption time calculation model to extract and process the glue liquid weight, glue liquid refractive index and consumption time from the current process sheet as the input result of the energy consumption prediction model, which facilitates the energy consumption prediction model to achieve the technical effect of energy consumption prediction.

[0034] 5. The method adopted by the present invention adjusts each model parameter by solving the process similarity, thereby reducing the risk of model failure due to process changes and enhancing the reliability of the model.

[0035] 6. The method adopted by the present invention selects the training feature set by analyzing the mechanism that affects the output of each model. On the one hand, the model has strong interpretability, and on the other hand, it reduces the input features, which facilitates the implementation of the model.

[0036] 7. The method adopted by the present invention constructs a glue liquid weight calculation model and a glue liquid refractive index calculation model through a BP neural network algorithm, and constructs a glue making consumption time calculation model based on a least squares fitting nonlinear parameter algorithm, thereby ensuring the accuracy of the final glue liquid weight, glue liquid refractive index and consumption time, making the final energy consumption prediction result more accurate.

[0037] 8. The method adopted in the present invention obtains the energy consumption forecast value, which helps the power supply and gas supply related departments of the enterprise to provide future electricity and gas consumption plan reports to cope with sudden power and gas outages during peak electricity and gas consumption times in the future, which require more energy consumption.

[0038] 9. The method adopted in the present invention obtains the energy consumption prediction value. The relevant staff can judge whether there is instability in the process and process equipment based on the obtained energy consumption prediction value. If instability occurs, the relevant staff can check and judge the specific problem based on the instability, thereby alerting the relevant staff to pay attention to the technical effects of the process and process equipment, and ensuring the normal operation of the process and process equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the structural block diagram of energy consumption prediction.

[0040] Figure 2 It is a schematic flow chart of steps one to three of the method of the present invention.

[0041] Figure 3 It is a schematic flow chart of steps 4 to 7 of the method of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0043] Example 1 A method for predicting energy consumption in a lyocell fiber glue making process comprises the following specific steps: Step 1: Collect the evaporation system parameter data, glue and sealing system parameter data, insulation water and vacuum system parameter data, slurry weight, glue weight, glue refractive index, consumption time and energy consumption data of the historical original data in the process of Lyocell fiber glue making, and perform data preprocessing to obtain historical data; data preprocessing technology is a mature technical method in this field in the prior art, which can eliminate abnormal values ​​in the historical original data and fill in the missing values ​​in the historical original data; Step 2: Calculate the process similarity of the historical process parameter data in the corresponding historical data according to the current process order, and rearrange the historical data in descending order of process similarity to obtain historical sorted data; Step 3: Select a reference group sequence from the historical sorting data, calculate the process similarity of each reference group sequence, thereby obtaining the historical sorting data sequence of the reference group sequence, and in the process of obtaining the historical sorting data sequence of the reference group sequence, obtain the threshold interval of the process similarity, and then use the historical sorting data sequence of the reference group sequence within the threshold interval of the process similarity to calculate the energy consumption prediction value sequence of the reference group sequence; the method for solving the process similarity in step 3 is the same as that in step 2; Step 4: Compare the energy consumption prediction value sequence of the reference group sequence with the historical energy consumption data of the corresponding reference group sequence, and record their absolute errors and X; Step 5: Step by step increase the threshold interval percentage P of process similarity to obtain the threshold interval of current process similarity; repeat steps 3 to 4 until the threshold interval of current process similarity reaches the set maximum value, and obtain the threshold interval percentage P of process similarity corresponding to the minimum absolute error and X in the process of repeating steps 3 to 4, and mark P as P s ; Among them, the maximum value of the threshold interval setting is artificially set and should not be too large. If the maximum value of the threshold interval setting is too large, it will lead to too much training data, causing overfitting and extending the calculation time. In practice, the maximum value of the threshold interval setting is half of the maximum similarity.

[0044] Step 6: Pass the historical sorting data through the threshold interval percentage P of process similarity s The set range is filtered to obtain historical screening and sorting data, and the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network, and the energy consumption prediction model is obtained after the neural network training; like Figure 1 As shown, step seven: extract the weight of the pulp porridge from the process list, and obtain the weight of the glue liquid by using the glue liquid weight calculation model constructed based on the BP neural network algorithm to compare the weight of the pulp porridge with the process parameter group 1 extracted from the process list, and obtain the consumption time by using the glue consumption time calculation model constructed based on the least squares fitting nonlinear parameter algorithm to compare the weight of the pulp porridge with the process parameter group 3 extracted from the process list, and obtain the consumption time by using the glue liquid refractive index calculation model constructed based on the BP neural network algorithm to obtain the glue liquid refractive index by using the process parameter group 2 extracted from the process list, and then input the pulp porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to finally obtain the energy consumption prediction value.

[0045] Example 2 A method for predicting energy consumption in a lyocell fiber glue making process comprises the following specific steps: Step 1: Collect the historical original data of the evaporation system parameter data, glue and sealing system parameter data, insulation water and vacuum system parameter data, slurry weight, glue weight, glue refractive index, consumption time and energy consumption data during the Lyocell fiber glue making process, and perform data preprocessing to obtain historical data; Step 2: Calculate the process similarity of the historical process parameter data in the corresponding historical data according to the current process order, and rearrange the historical data in descending order of process similarity to obtain historical sorted data; Step 3: Select a reference group sequence from the historical sorting data, calculate the process similarity of each reference group sequence, thereby obtaining the historical sorting data sequence of the reference group sequence, and in the process of obtaining the historical sorting data sequence of the reference group sequence, obtain the threshold interval of the process similarity, and then use the historical sorting data sequence of the reference group sequence within the threshold interval of the process similarity to calculate the energy consumption prediction value sequence of the reference group sequence; the method for solving the process similarity in step 3 is the same as that in step 2; Step 4: Compare the energy consumption prediction value sequence of the reference group sequence with the historical energy consumption data of the corresponding reference group sequence, and record their absolute errors and X; Step 5: Step by step increase the threshold interval percentage P of process similarity to obtain the threshold interval of current process similarity; repeat steps 3 to 4 until the threshold interval of current process similarity reaches the set maximum value, and obtain the threshold interval percentage P of process similarity corresponding to the minimum absolute error and X in the process of repeating steps 3 to 4, and mark P as P s ; Among them, the maximum value of the threshold interval setting is artificially set and should not be too large. If the maximum value of the threshold interval setting is too large, it will lead to too much training data, resulting in overfitting and prolonged calculation time. In practice, the maximum value of the threshold interval setting is half of the maximum similarity; Step 6: Pass the historical sorting data through the threshold interval percentage P of process similarity s The set range is filtered to obtain historical screening and sorting data, and the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network, and the energy consumption prediction model is obtained after the neural network training; Step seven: extract the weight of pulp porridge from the process list, and obtain the weight of glue liquid by using the glue liquid weight calculation model constructed based on the BP neural network algorithm to compare the weight of pulp porridge with process parameter group 1 extracted from the process list, and obtain the consumption time by using the glue making consumption time calculation model constructed based on the least squares fitting nonlinear parameter algorithm to compare the weight of pulp porridge with process parameter group 3 extracted from the process list, and obtain the glue liquid refractive index by using the glue liquid refractive index calculation model constructed based on the BP neural network algorithm to compare the process parameter group 2 extracted from the process list, and then input the pulp porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to finally obtain the energy consumption prediction value.

[0046] Historical original data include historical original process parameter data, historical original energy consumption data and historical original intermediate parameter data; historical data include historical process parameter data, historical energy consumption data and historical intermediate parameter data; historical sorted data include historical sorted process parameter data, historical sorted intermediate parameter data and historical sorted energy consumption data; historical screened and sorted data include historical screened and sorted process parameter data, historical screened and sorted intermediate parameter data and historical screened and sorted energy consumption data; intermediate parameter data include glue liquid weight, glue liquid refractive index and consumption time; energy consumption data include electricity energy consumption and steam energy consumption.

[0047] The process sheet includes process parameter data; the process parameter data include evaporation system parameter data, glue liquid and sealing system parameter data, insulation water and vacuum system parameter data and porridge weight; the evaporation system parameter data include evaporation heating system parameter data, evaporation dissolver parameter data and evaporation condensation water system parameter data; the glue liquid and sealing system parameter data include sealing liquid system parameter data and glue liquid transportation parameter data; the insulation water and vacuum system parameter data include insulation water system parameter data and vacuum system parameter data.

[0048] The parameter data of the evaporation heating system include the temperature of the heating system, the liquid level of the heating system and the pressure of the heating system; the parameter data of the evaporation dissolving machine include the temperature of the four zones of the evaporation dissolving machine, the motor speed of the evaporation dissolving machine, the circulation temperature of the lubricating oil of the bearing of the evaporation dissolving machine, the speed of the reducer bearing, the speed of the cooling fan, the liquid level of the evaporation dissolving machine, the outlet pressure of the evaporation dissolving machine and the outlet temperature of the bottom of the evaporation dissolving machine; the temperature of the four zones of the evaporation dissolving machine include the jacket temperature of the four evaporation zones and the glue temperature of the four evaporation zones; the glue delivery parameter data include the temperature of the insulation water heat exchanger in the pump, the flow rate of the insulation water heat exchanger in the pump, and the insulation water heat exchanger outside the pump. The data include the temperature of the sealing liquid tank, the flow rate of the insulation water heat exchanger outside the pump, the speed of the glue delivery pump, the outlet pressure of the glue delivery pump and the outlet temperature of the glue delivery pump; the parameter data of the sealing liquid system include the temperature of the sealing liquid tank, the flow rate of the sealing liquid and the liquid level of the sealing liquid tank; the parameter data of the insulation water system include the outlet temperature of the insulation water circulation pump and the hot water pressure of the insulation system; the parameter data of the evaporative condensate water system include the temperature of the evaporative condenser, the liquid level of the evaporative condenser, the pressure of the evaporative condenser, the flow rate of the cooling water to the evaporative condenser and the outlet flow rate of the evaporative condensate pump; the parameter data of the vacuum system include the liquid level of the vacuum system, the pressure of the vacuum system and the speed of the vacuum pump.

[0049] Example 3 A method for predicting energy consumption in a lyocell fiber glue making process comprises the following specific steps: Step 1: Collect the historical original data of the evaporation system parameter data, glue and sealing system parameter data, insulation water and vacuum system parameter data, slurry weight, glue weight, glue refractive index, consumption time and energy consumption data during the Lyocell fiber glue making process, and perform data preprocessing to obtain historical data; Step 2: Calculate the process similarity of the historical process parameter data in the corresponding historical data according to the current process order, and rearrange the historical data in descending order of process similarity to obtain historical sorted data; Step 3: Select a reference group sequence from the historical sorting data, calculate the process similarity of each reference group sequence, thereby obtaining a historical sorting data sequence of the reference group sequence, and in the process of obtaining the historical sorting data sequence of the reference group sequence, obtain a threshold interval of process similarity, and then use the historical sorting data sequence of the reference group sequence within the threshold interval of process similarity to calculate the energy consumption prediction value sequence of the reference group sequence; the method for solving the process similarity in step 3 is the same as that in step 2; Step 4: Compare the energy consumption prediction value sequence of the reference group sequence with the historical energy consumption data of the corresponding reference group sequence, and record their absolute errors and X; Step 5: Step by step increase the threshold interval percentage P of process similarity to obtain the threshold interval of current process similarity; repeat steps 3 to 4 until the threshold interval of current process similarity reaches the set maximum value, and obtain the threshold interval percentage P of process similarity corresponding to the minimum absolute error and X in the process of repeating steps 3 to 4, and mark P as P s ; Among them, the maximum value of the threshold interval setting is artificially set and should not be too large. If the maximum value of the threshold interval setting is too large, it will lead to too much training data, resulting in overfitting and prolonged calculation time. In practice, the maximum value of the threshold interval setting is half of the maximum similarity. Step 6: Pass the historical sorting data through the threshold interval percentage P of process similarity s The set range is filtered to obtain historical screening and sorting data, and the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network, and the energy consumption prediction model is obtained after the neural network training; Step seven: extract the weight of pulp porridge from the process list, and obtain the weight of glue liquid by using the glue liquid weight calculation model constructed based on the BP neural network algorithm to compare the weight of pulp porridge with process parameter group 1 extracted from the process list, and obtain the consumption time by using the glue making consumption time calculation model constructed based on the least squares fitting nonlinear parameter algorithm to compare the weight of pulp porridge with process parameter group 3 extracted from the process list, and obtain the glue liquid refractive index by using the glue liquid refractive index calculation model constructed based on the BP neural network algorithm to compare the process parameter group 2 extracted from the process list, and then input the pulp porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to finally obtain the energy consumption prediction value.

[0050] Historical original data include historical original process parameter data, historical original energy consumption data and historical original intermediate parameter data; historical data include historical process parameter data, historical energy consumption data and historical intermediate parameter data; historical sorted data include historical sorted process parameter data, historical sorted intermediate parameter data and historical sorted energy consumption data; historical screened and sorted data include historical screened and sorted process parameter data, historical screened and sorted intermediate parameter data and historical screened and sorted energy consumption data; intermediate parameter data include glue liquid weight, glue liquid refractive index and consumption time; energy consumption data include electricity energy consumption and steam energy consumption.

[0051] The process sheet includes process parameter data; the process parameter data include evaporation system parameter data, glue liquid and sealing system parameter data, insulation water and vacuum system parameter data and porridge weight; the evaporation system parameter data include evaporation heating system parameter data, evaporation dissolver parameter data and evaporation condensation water system parameter data; the glue liquid and sealing system parameter data include sealing liquid system parameter data and glue liquid transportation parameter data; the insulation water and vacuum system parameter data include insulation water system parameter data and vacuum system parameter data.

[0052] The parameter data of the evaporation heating system include the temperature of the heating system, the liquid level of the heating system and the pressure of the heating system; the parameter data of the evaporation dissolving machine include the temperature of the four zones of the evaporation dissolving machine, the motor speed of the evaporation dissolving machine, the circulation temperature of the lubricating oil of the bearing of the evaporation dissolving machine, the speed of the reducer bearing, the speed of the cooling fan, the liquid level of the evaporation dissolving machine, the outlet pressure of the evaporation dissolving machine and the outlet temperature of the bottom of the evaporation dissolving machine; the temperature of the four zones of the evaporation dissolving machine include the jacket temperature of the four evaporation zones and the glue temperature of the four evaporation zones; the glue delivery parameter data include the temperature of the insulation water heat exchanger in the pump, the flow rate of the insulation water heat exchanger in the pump, and the insulation water heat exchanger outside the pump. The data include the temperature of the sealing liquid tank, the flow rate of the insulation water heat exchanger outside the pump, the speed of the glue delivery pump, the outlet pressure of the glue delivery pump and the outlet temperature of the glue delivery pump; the parameter data of the sealing liquid system include the temperature of the sealing liquid tank, the flow rate of the sealing liquid and the liquid level of the sealing liquid tank; the parameter data of the insulation water system include the outlet temperature of the insulation water circulation pump and the hot water pressure of the insulation system; the parameter data of the evaporative condensate water system include the temperature of the evaporative condenser, the liquid level of the evaporative condenser, the pressure of the evaporative condenser, the flow rate of the cooling water to the evaporative condenser and the outlet flow rate of the evaporative condensate pump; the parameter data of the vacuum system include the liquid level of the vacuum system, the pressure of the vacuum system and the speed of the vacuum pump.

[0053] like Figure 1 As shown, the weight of pulp porridge is extracted from the process list, and the weight of pulp porridge and process parameter group 1 extracted from the process list are calculated by the glue liquid weight calculation model based on the BP neural network algorithm to obtain the glue liquid weight, and the weight of pulp porridge and process parameter group 3 extracted from the process list are calculated by the glue consumption time calculation model based on the least squares fitting nonlinear parameter algorithm to obtain the consumption time, and the process parameter group 2 extracted from the process list is calculated by the glue liquid refractive index calculation model based on the BP neural network algorithm to obtain the glue liquid refractive index, and then the weight of pulp porridge, the weight of glue liquid, the refractive index of glue liquid and the consumption time are input into the energy consumption prediction model, and the specific method steps for finally obtaining the energy consumption prediction value are as follows: Step a1: Based on the BP neural network algorithm, a glue liquid weight calculation model is constructed; the specific construction method of the glue liquid weight calculation model is: the porridge weight, the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed in the historical screening and sorting process parameter data are used as the input features of the glue liquid weight calculation model training set, and the glue liquid weight in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid weight calculation model training set; Figure 1 As shown, model 2 is the glue weight calculation model; Step b1: Based on the BP neural network algorithm, a glue liquid refractive index calculation model is constructed; the specific construction method of the glue liquid refractive index calculation model is: the motor speed of the evaporation dissolution machine, the temperature of the four zones of the evaporation dissolution machine, the speed of the vacuum pump and the outlet temperature of the insulation water circulation pump in the historical screening and sorting process parameter data are used as the input features of the glue liquid refractive index calculation model training set, and the glue liquid refractive index in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid refractive index calculation model training set; Figure 1 As shown, model 3 is the calculation model of the refractive index of the glue liquid; Step c1: Based on the least squares method to fit the nonlinear parameter algorithm, a glue-making time calculation model is constructed; the specific construction method of the glue-making time calculation model is as follows: the porridge weight, the evaporation dissolution machine motor speed and the vacuum pump speed in the historical screening and sorting process parameter data are used as the input features of the glue-making time calculation model, and the consumption time in the historical screening and sorting intermediate parameter data is used as the target output of the glue-making time calculation model; Figure 1 As shown, model 4 is a calculation model for glue consumption time; Step d1: extract the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 from the process list; input the porridge weight and process parameter group 1 into the glue liquid weight calculation model to obtain the glue liquid weight, input the process parameter group 2 into the glue liquid refractive index calculation model to obtain the glue liquid refractive index, and input the porridge weight and process parameter group 3 into the glue making consumption time calculation model to obtain the consumption time, and finally input the porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to obtain the energy consumption prediction value; the process parameter group 1 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed; the process parameter group 2 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum pump speed and the insulation water circulation pump outlet temperature; the process parameter group 3 includes the evaporation dissolution machine motor speed and the vacuum pump speed. In step a1, the number of input layer neurons of the glue weight calculation model is set to 8, and the number of output layer neurons is 1; there are two hidden layers, the number of neurons in the first layer is 5, and the number of neurons in the second layer is 4; the tanh activation function is selected as the activation function; the MSE is selected as the loss function; the initial weight is randomly generated using a Gaussian distribution with a mean of 0 and a standard deviation of 0.01; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to three decimal places.

[0054] In step b1, the number of input layer neurons of the glue refractive index calculation model is set to 8, and the number of output layer neurons is set to 1; there are two hidden layers, the number of neurons in the first layer is 6, and the number of neurons in the second layer is 4; the tanh activation function is used as the activation function; the MSE is used as the loss function; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to four decimal places.

[0055] The expression of the calculation model of glue consumption time is as follows: ; In the formula, t is the time consumed by the glue making process; a, b and e are parameters to be fitted; is the motor speed of the evaporation dissolution machine; is the vacuum pump speed; is the weight of the porridge; the least squares method based nonlinear parameter fitting algorithm is a mature prior art algorithm in this field, and its specific fitting process is as follows: Each time a new process order is input, fitting needs to be performed once. When initialized for the first time, the initial values ​​of a, b and e are all initialized to 1, otherwise they are initialized to the parameters of the previous fitting; calculate The Jacobian matrix J is the partial derivative matrix of the objective function with respect to the model parameters. Elements The specific calculation formula is as follows: ; In the formula, is the data of the i-th row and j-th column of the Jacobian matrix J, k represents the current number of iterations, is the input feature data of the observation dataset, for The core of nonlinear fitting through Jacobian matrix is ​​to use the local information provided by Jacobian matrix to gradually adjust the parameters through iteration so as to minimize the error function. Calculate the error vector: ; In the formula, k represents the current iteration number, is the input feature data of the observation dataset, The error of the i-th observation data, The consumption time of the glue making process of the i-th observation data, t is the consumption time of the glue making process; in ; In the formula, the observed data has n data rows, i refers to a row, and r is the column vector; Solve for the increment: Solving Linear Equations Get the increment of the parameter ; In the formula, the Jacobian matrix , is the Jacobian matrix The transpose of Update the parameters and continuously update the parameters to be fitted to make them close to the correct values: ; Determine the termination conditions: Repeat the above steps until Up to 3 decimal places.

[0056] Example 4 Based on Example 3, the specific steps of data preprocessing in step 1 are as follows: Step a: Eliminate outliers based on the 3σ principle; Step b: After outliers are removed, the missing values ​​are filled by taking the weighted average of the K neighboring data values ​​near the missing value.

[0057] In step a, outliers are eliminated based on the 3σ principle, and the expression is as follows: ; In the formula, is the mean of each column of historical original data, is the standard deviation of each column of historical raw data. The specific calculation formula is as follows: ; ; In the formula, is the number of historical raw data in each column, It is the numerical value of each column of historical original data.

[0058] Process similarity in step 2 The cosine similarity is used for calculation, and the specific calculation formula is: ; In the formula, It is A vector consisting of the process parameter data of the row history; It is a vector consisting of the current process order data.

[0059] The specific method of selecting the reference group from the historical sorting data in step 3 is: the first m rows of the historical sorting data form a reference group sequence ; The value of m should not be set too large, otherwise it will increase the amount of calculation. m is an even random number in [10,20]; the reference group sequence Each sequence in is used as the current process list, and the historical sorting data corresponding to each reference group sequence is obtained respectively. , thus forming the historical sorting data sequence of the reference group sequence , and record the maximum process similarity value in the process ;Threshold interval of process similarity , calculate the minimum value of the threshold interval The specific calculation formula is as follows: ; In the formula, Indicates the initial value of the threshold interval percentage of the set process similarity; In step 4, the energy consumption prediction value sequence of the reference group sequence is compared with the historical energy consumption data of the corresponding reference group sequence, and their absolute errors and X are recorded. The specific method is as follows: the energy consumption prediction value sequence of the reference group sequence is compared with the historical energy consumption data of the corresponding reference group sequence, and their absolute error values ​​are recorded. The m rows of historical sorted data sequence are traversed to obtain the energy consumption prediction absolute error value sequence. , accumulate the energy consumption prediction absolute error value sequence to obtain the absolute error and X and record it.

[0060] In step three, the energy consumption prediction value sequence of the reference group sequence is calculated using the historical sorted data sequence of the reference group sequence. The specific method is: the porridge weight of the historical sorted intermediate parameter data and the historical sorted process parameter data in the historical sorted data sequence of the reference group sequence are used as the training set of the neural network model to obtain the energy consumption prediction value sequence of the reference group sequence; the process similarity corresponding to the historical sorted intermediate parameter data belongs to the threshold interval range of the process similarity.

[0061] Before using the porridge weight of the historically sorted intermediate parameter data and the historically sorted process parameter data as the training set of the neural network model for training, it is necessary to eliminate the difference between the dimensions of the input data through homogenization processing. The specific calculation formula is: ; In the formula, It is the result of the uniform processing of the i-th row and j-th column of the training set; is the original data of row i and column j of the training set; is the minimum value of the process parameters in the jth column of the training set; is the maximum value of the process parameters in the jth column in the training set.

[0062] The neural network model specifically uses a three-layer BP neural network, with 4 neurons in the input layer; 4 neurons in the output layer; 1 hidden layer with 4 neurons; the Tanh activation function is used as the activation function; the MSE is used as the loss function; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to five decimal places.

[0063] The specific calculation formula for the threshold interval percentage P of the stepwise increase in process similarity in step 5 is: ; In the formula, P represents the threshold interval percentage of the current process similarity; represents the initial value of the threshold interval percentage of the set process similarity; S represents the step value of the threshold interval percentage of the set process similarity; n represents the current step number; The threshold interval of the current process similarity is : ; In the formula, Indicates the current minimum process similarity value; Indicates the maximum process similarity value.

[0064] like Figure 1 As shown, model 1 is an energy consumption prediction model; in step 6, the training set is homogenized to eliminate the differences between the dimensions of the input data.

[0065] In step six, the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network. The specific method of obtaining the energy consumption prediction model after neural network training is as follows: the glue weight, glue refractive index and consumption time in the intermediate parameter data of historical screening and sorting and the porridge weight in the process parameters of historical screening and sorting are used as the input features of the training set of the energy consumption prediction model, and the energy consumption data of historical screening and sorting are used as the target output of the energy consumption prediction model; the energy consumption prediction model uses a three-layer BP neural network, the number of neurons in the input layer is 4, the number of neurons in the output layer is 1; the hidden layer is 1, and the number of neurons in this layer is 4.

[0066] Since the energy consumption data is the sum of electricity energy consumption and steam energy consumption, the specific calculation formula for converting steam energy consumption into electricity energy consumption is as follows: ; In the formula, The electricity consumption for steam; is the price of steam, in yuan / m 3 ; is the price of electric energy, in Yuan / KW·h; is the amount of steam used, in m 3 .

[0067] Example 5 The flow chart of this method is as follows Figure 2 and Figure 3 As shown, Figure 2 : The flowchart diagram of steps 1 to 3 is as follows: collecting historical raw data in the process of making glue of lyocell fiber, and preprocessing the historical raw data to obtain historical data; calculating the process similarity of the historical process parameter data in the corresponding historical data according to the current process sheet, and rearranging the historical data in the order of process similarity from large to small to obtain historical sorted data; selecting a reference group sequence from the historical sorted data, calculating the process similarity of each reference group sequence, thereby obtaining a historical sorted data sequence of the reference group sequence, and obtaining a threshold interval of process similarity in the process of obtaining the historical sorted data sequence of the reference group sequence, and then using the historical sorted data sequence of the reference group sequence to calculate the energy consumption prediction value sequence of the reference group sequence; Figure 3The flowchart of steps 4 to 7 is shown in the figure: the energy consumption prediction value sequence of the reference group sequence is compared with the historical energy consumption data of the corresponding reference group sequence, and their absolute errors and X are recorded; the threshold interval percentage P of the process similarity is increased step by step to obtain the threshold interval of the current process similarity; steps 3 to 4 are repeated until the threshold interval of the current process similarity reaches the set maximum value, and the threshold interval percentage P of the process similarity corresponding to the minimum absolute error and X in the process of repeating steps 3 to 4 is obtained. s ; The historical sorting data is passed through the threshold interval percentage P of process similarity s The set range is filtered to obtain historical filtering and sorting data, and the historical filtering and sorting data is used as the training set of the neural network. After the neural network training, the energy consumption prediction model is obtained; the porridge weight, glue weight, glue refractive index and consumption time are extracted from the process list and input into the energy consumption prediction model to finally obtain the energy consumption prediction value.

[0068] Example 6 Based on Example 4, an application of an energy consumption prediction method for a lyocell fiber glue making process is suitable for energy consumption prediction for a lyocell fiber glue making process.

[0069] The above description is a detailed description of the preferred feasible embodiment of the present application, but the embodiment is not intended to limit the patent application scope of the present application. All equivalent changes or modified changes made under the technical spirit suggested by the present application should fall within the patent scope covered by the present application.

Claims

1. A method for predicting energy consumption in a lyocell fiber glue making process, characterized in that: The specific steps include: Step 1: Collect the historical raw data of evaporation system parameter data, glue and sealing system parameter data, insulation water and vacuum system parameter data, slurry weight, glue weight, glue refractive index, consumption time and energy consumption data during the Lyocell fiber glue making process, and perform data preprocessing to obtain historical data; Step 2: Calculate the process similarity of the corresponding historical data according to the current process order, and rearrange the historical data according to the process similarity to obtain historical sorted data; Step 3: Select a reference group sequence from the historical sorting data, calculate the process similarity of each reference group sequence, obtain the historical sorting data sequence, and find the threshold interval of the process similarity. Then use the historical sorting data sequence within the threshold interval to calculate the energy consumption prediction value sequence; Step 4: Compare the energy consumption forecast value sequence with the historical energy consumption data of the corresponding reference group sequence, and record the absolute error and X; Step 5: Increase the threshold interval percentage P of process similarity to obtain the threshold interval of current process similarity; Repeat steps 3 to 4 until the threshold interval of the current process similarity reaches the set maximum value, and obtain the threshold interval percentage P of the process similarity corresponding to the minimum absolute error and X, and mark P as P s ; Step 6: Pass historical sorting data through P s The historical screening and sorting data are obtained by screening, and the porridge weight, glue liquid weight, glue liquid refractive index and consumption time are used as the training set of the neural network, and the energy consumption prediction model is obtained after training; Step seven: Extract the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 from the process list, and input them into the constructed calculation model to obtain the porridge weight, glue weight, glue refractive index and consumption time, and input them into the energy consumption prediction model to obtain the energy consumption prediction value.

2. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: In the step seven, the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 are extracted from the process list and input into the constructed calculation model to obtain the porridge weight, glue liquid weight, glue liquid refractive index and consumption time, and input into the energy consumption prediction model. The specific method to obtain the energy consumption prediction value is as follows: extract the porridge weight from the process list, compare the porridge weight with the process parameter group 1 extracted from the process list, and obtain the glue liquid weight through the glue liquid weight calculation model constructed based on the BP neural network algorithm, compare the porridge weight with the process parameter group 3 extracted from the process list, and obtain the consumption time through the glue making consumption time calculation model constructed based on the least squares fitting nonlinear parameter algorithm, obtain the glue liquid refractive index through the glue liquid refractive index calculation model constructed based on the BP neural network algorithm from the process parameter group 2 extracted from the process list, and then input the porridge weight, glue liquid weight, glue liquid refractive index and consumption time into the energy consumption prediction model to finally obtain the energy consumption prediction value.

3. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: Historical original data include historical original process parameter data, historical original energy consumption data and historical original intermediate parameter data; historical data include historical process parameter data, historical energy consumption data and historical intermediate parameter data; historical sorted data include historical sorted process parameter data, historical sorted intermediate parameter data and historical sorted energy consumption data; historical filtered and sorted data include historical filtered and sorted process parameter data, historical filtered and sorted intermediate parameter data and historical filtered and sorted energy consumption data.

4. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The process sheet includes process parameter data.

5. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 3 or 4, characterized in that: The process parameter data include evaporation system parameter data, glue liquid and sealing system parameter data, insulation water and vacuum system parameter data and porridge weight; the evaporation system parameter data include evaporation heating system parameter data, evaporation dissolution machine parameter data and evaporation condensation water system parameter data; the glue liquid and sealing system parameter data include sealing liquid system parameter data and glue liquid transportation parameter data; the insulation water and vacuum system parameter data include insulation water system parameter data and vacuum system parameter data.

6. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 5, characterized in that: The parameter data of the evaporation heating system include the temperature of the heating system, the liquid level of the heating system and the pressure of the heating system; the parameter data of the evaporation dissolving machine include the temperature of the four zones of the evaporation dissolving machine, the motor speed of the evaporation dissolving machine, the circulation temperature of the lubricating oil of the bearing of the evaporation dissolving machine, the speed of the reducer bearing, the speed of the cooling fan, the liquid level of the evaporation dissolving machine, the outlet pressure of the evaporation dissolving machine and the outlet temperature of the bottom of the evaporation dissolving machine; the temperature of the four zones of the evaporation dissolving machine include the jacket temperature of the four evaporation zones and the glue temperature of the four evaporation zones; the glue delivery parameter data include the temperature of the insulation water heat exchanger in the pump, the flow rate of the insulation water heat exchanger in the pump, the insulation water heat exchanger outside the pump The data include the temperature of the sealing liquid tank, the flow rate of the sealing liquid tank and the liquid level of the sealing liquid tank; the data include the outlet temperature of the insulation water circulation pump and the hot water pressure of the insulation system; the data include the temperature of the evaporative condenser, the liquid level of the evaporative condenser, the pressure of the evaporative condenser, the flow rate of the cooling water to the evaporative condenser and the outlet flow rate of the evaporative condensate pump; the data include the vacuum system liquid level, the pressure of the vacuum system and the speed of the vacuum pump.

7. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 2, characterized in that: The specific steps of step seven are as follows: Step a1: Based on the BP neural network algorithm, a glue liquid weight calculation model is constructed; the specific construction method of the glue liquid weight calculation model is: the porridge weight, the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed in the historical screening and sorting process parameter data are used as the input features of the glue liquid weight calculation model training set, and the glue liquid weight in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid weight calculation model training set; Step b1: Based on the BP neural network algorithm, a glue liquid refractive index calculation model is constructed; the specific construction method of the glue liquid refractive index calculation model is: the motor speed of the evaporation dissolution machine, the temperature of the four zones of the evaporation dissolution machine, the speed of the vacuum pump and the outlet temperature of the insulation water circulation pump in the historical screening and sorting process parameter data are used as the input features of the glue liquid refractive index calculation model training set, and the glue liquid refractive index in the historical screening and sorting intermediate parameter data is used as the target output of the glue liquid refractive index calculation model training set; Step c1: constructing a glue-making time calculation model based on a least squares fitting nonlinear parameter algorithm; the specific construction method of the glue-making time calculation model is as follows: using the porridge weight, the evaporation dissolution machine motor speed and the vacuum pump speed in the historically screened and sorted process parameter data as input features of the glue-making time calculation model, and using the consumption time in the historically screened and sorted intermediate parameter data as the target output of the glue-making time calculation model; Step d1: extracting the porridge weight, process parameter group 1, process parameter group 2 and process parameter group 3 from the process list; The porridge weight and process parameter group 1 are input into the glue liquid weight calculation model to obtain the glue liquid weight, the process parameter group 2 is input into the glue liquid refractive index calculation model to obtain the glue liquid refractive index, and the porridge weight and process parameter group 3 are input into the glue making consumption time calculation model to obtain the consumption time, and finally the porridge weight, glue liquid weight, glue liquid refractive index and consumption time are input into the energy consumption prediction model to obtain the energy consumption prediction value; the process parameter group 1 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum system pressure and the vacuum pump speed; the process parameter group 2 includes the evaporation dissolution machine motor speed, the evaporation dissolution machine four-zone temperature, the vacuum pump speed and the insulation water circulation pump outlet temperature; the process parameter group 3 includes the evaporation dissolution machine motor speed and the vacuum pump speed.

8. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 7, characterized in that: In the step a1, the number of input layer neurons of the glue weight calculation model is set to 8, and the number of output layer neurons is 1; there are two hidden layers, the number of neurons in the first layer is 5, and the number of neurons in the second layer is 4; the tanh activation function is selected as the activation function; the MSE is selected as the loss function; the initial weight is randomly generated using a Gaussian distribution with a mean of 0 and a standard deviation of 0.01; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to three decimal places.

9. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 7, characterized in that: In the step b1, the number of input layer neurons of the glue refractive index calculation model is set to 8, and the number of output layer neurons is set to 1; there are two hidden layers, the number of neurons in the first layer is 6, and the number of neurons in the second layer is 4; the tanh activation function is selected as the activation function; the MSE is selected as the loss function; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to four decimal places.

10. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 7, characterized in that: The expression of the calculation model of glue consumption time is as follows: ； In the formula, t is the time consumed by the glue making process; a, b and e are parameters to be fitted; is the motor speed of the evaporation dissolution machine; is the vacuum pump speed; It is the weight of porridge.

11. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 3, characterized in that: The intermediate parameter data include glue liquid weight, glue liquid refractive index and consumption time.

12. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The specific steps of data preprocessing in step 1 are as follows: Step a: Eliminate outliers based on the 3σ principle; Step b: After outliers are removed, the missing values ​​are filled by taking the weighted average of the K neighboring data values ​​near the missing value.

13. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 12, characterized in that: In step a, outliers are eliminated based on the 3σ principle, and the expression is as follows: ； In the formula, is the mean of each column of historical original data, is the standard deviation of each column of historical raw data. The specific calculation formula is as follows: ； ； In the formula, is the number of historical raw data in each column, It is the numerical value of each column of historical original data.

14. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: Process similarity in step 2 The cosine similarity is used for calculation, and the specific calculation formula is: ； In the formula, It is A vector consisting of the process parameter data of the row history; It is a vector consisting of the current process order data.

15. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The specific method of selecting the reference group from the historical sorting data in step 3 is: the first m rows of the historical sorting data form a reference group sequence ; By reference group sequence For each sequence in the reference group, we obtain the historical sorting data corresponding to each sequence in the reference group. , thus forming the historical sorting data sequence of the reference group sequence , and record the maximum process similarity value in the process ; The threshold interval of process similarity in step 3 , calculate the minimum process similarity value of the threshold interval The specific calculation formula is as follows: ； In the formula, Indicates the initial value of the threshold interval percentage of the set process similarity.

16. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: In the step three, the historical sorted data sequence within the threshold interval is used to calculate the energy consumption prediction value sequence. The specific method is: the porridge weight of the historical sorted intermediate parameter data and the historical sorted process parameter data in the historical sorted data sequence of the reference group sequence is used as the training set of the neural network model, so as to obtain the energy consumption prediction value sequence of the reference group sequence; the process similarity corresponding to the historical sorted intermediate parameter data belongs to the threshold interval of the process similarity.

17. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 16, characterized in that: Before using the porridge weight of the historically sorted intermediate parameter data and the historically sorted process parameter data as the training set of the neural network model for training, it is necessary to eliminate the difference between the dimensions of the input data through homogenization processing. The specific calculation formula is: ； In the formula, It is the result of the uniform processing of the i-th row and j-th column of the training set; is the original data of row i and column j of the training set; The minimum value of the process parameters in the jth column of the training set; The maximum value of the process parameters in the jth column in the training set.

18. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 17, characterized in that: The neural network model specifically uses a three-layer BP neural network, with 4 neurons in the input layer; 4 neurons in the output layer; 1 hidden layer with 4 neurons; the Tanh activation function is used as the activation function; the MSE is used as the loss function; the initial weight is in the range of -1 to 1; the initial bias value is zero; the learning rate is set to 0.001; and the precision is set to five decimal places.

19. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The percentage of the threshold interval for increasing process similarity in step 5 The specific calculation formula is: ； In the formula, P represents the threshold interval percentage of the current process similarity; represents the initial value of the threshold interval percentage of the set process similarity; S represents the step value of the threshold interval percentage of the set process similarity; n represents the current step number; The threshold interval of the current process similarity is : ； In the formula, Indicates the current minimum process similarity value; Indicates the maximum process similarity value.

20. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 19, characterized in that: In step six, the training set is homogenized to eliminate the differences between the dimensions of the input data.

21. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: In step six, the porridge weight, glue weight, glue refractive index and consumption time in the historical screening and sorting data are used as the training set of the neural network. The specific method of obtaining the energy consumption prediction model after training is as follows: the glue weight, glue refractive index and consumption time in the intermediate parameter data of the historical screening and sorting and the porridge weight in the process parameters of the historical screening and sorting are used as the input features of the training set of the energy consumption prediction model, and the energy consumption data of the historical screening and sorting are used as the target output of the energy consumption prediction model; the energy consumption prediction model uses a three-layer BP neural network, the number of neurons in the input layer is 4, the number of neurons in the output layer is 1; the hidden layer is 1 layer, and the number of neurons in this layer is 4.

22. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The energy consumption data is the sum of electricity energy consumption and steam energy consumption; the specific calculation formula for converting steam energy consumption into electricity energy consumption is as follows: ； In the formula, The electricity consumption for steam; is the price of steam, in yuan / m 3 ; is the price of electric energy, in Yuan / KW·h; is the amount of steam used, in m 3 .

23. The method for predicting energy consumption in a lyocell fiber glue making process according to claim 1, characterized in that: The specific method in step 4 is: compare the energy consumption prediction value sequence of the reference group sequence with the historical energy consumption data of the corresponding reference group sequence, record their absolute error values, traverse the m rows of historical sorted data sequence, and obtain the energy consumption prediction absolute error value sequence , accumulate the energy consumption prediction absolute error value sequence to obtain the absolute error and X and record it.

24. An application of an energy consumption prediction method for a Lyocell fiber glue making process, characterized in that: The energy consumption prediction method for the lyocell fiber glue making process as described in any one of claims 1 to 23 is suitable for predicting the energy consumption of the lyocell fiber glue making process.

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