Optimization method of raw material tempering process

Abstract

The invention discloses an optimization method of a raw material hardening and tempering process, and relates to the technical field of data model optimization. The method comprises the steps of generating a first parameter set based on different working conditions of variable parameters, and calculating a function value based on a hardening and tempering process model and an actual value; screening the first parameter set through function values, non-dominated sorting and crowding distance to generate a parameter queue, performing crossover and mutation operation on the parameter queue through crossover rate and mutation rate to generate a second parameter set, and updating the first parameter set to participate in next iterative calculation by combining the parameter queue and the second parameter set. And ending iteration and outputting a target parameter set. And determining the interval of the variable parameters according to the mean value and the standard deviation of the target parameter set, and determining the target parameter combination based on the weight coefficient. Furthermore, the crossover rate and the mutation rate are dynamically adjusted according to different iteration stages, and premature convergence and target parameter combination loss are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data model optimization, and in particular to a method for optimizing a raw material conditioning process. Background Art

[0002] In the conditioning process of feed production, the structure of the raw materials can be changed by changing the moisture content or temperature of the feed, making it easier for animals to digest and absorb, thereby improving the nutritional value of the feed. However, in the actual production process, on the same production line, the same raw material formula, different process parameters will affect the quality of the product. The Chinese patent application with publication number CN118348865A discloses an automated control system and method in the livestock feed processing process. The method uses deep learning technology to monitor and analyze parameters such as material temperature, humidity and flow rate in the pellet mill in real time, analyzes the feed state and equipment operation state characteristics based on these parameters, and adaptively optimizes the feed production process. However, this method requires a large amount of historical data to train the deep learning model, and frequent real-time adjustments are required to find the target process parameters. Therefore, it is necessary to further improve the prior art. Summary of the invention

[0003] In view of the above problems, the present invention discloses an optimization method for raw material tempering process. The optimization method pre-generates parameter combinations under different working conditions, generates new parameter combinations after continuously iterating variable parameters based on the existing parameter combinations, improves the diversity of parameter combinations, and finally quickly finds the preferred variable parameters under the current production conditions, realizes effective optimization of parameter combinations, and thus improves the quality of tempered materials.

[0004] The invention objectives of this application can be achieved through the following technical solutions: A method for optimizing a raw material conditioning process comprises the following steps: Step 1: extract the fixed parameters of the tempering production line, the first variable parameters of the raw materials, and the second variable parameters of the inlet steam, generate a tempering process model, generate a parameter combination including the first variable parameter and the second variable parameter, create a first parameter set consisting of multiple parameter combinations, and initialize the number of iterations q=0; Step 2: raw materials and inlet steam are input into the quenching and tempering production line, the quenching and tempering production line produces quenching and tempering materials, the quenching and tempering production line is controlled based on different parameter combinations in the first parameter set, and the moisture content and temperature of the quenching and tempering materials are collected; Step 3: Obtain the target moisture content and target temperature according to the quenching and tempering process model, and establish the objective function of the quenching and tempering material moisture content and moisture content deviation, and the objective function of the quenching and tempering material temperature and temperature deviation; Step 4: Divide the first parameter set into multiple non-dominated sets according to the function value of the objective function and generate non-dominated queues, calculate the crowding distance of the parameter combination in each non-dominated set, and obtain the mean crowding distance of the non-dominated set; Step 5: extract at least one parameter combination based on the mean of the crowding distance of the non-dominated set in the non-dominated queue to generate a parameter queue; Step 6: Based on the current number of iterations q and the maximum number of iterations q max The crossover rate and mutation rate are dynamically adjusted according to the ratio of q, and the parameter queue is processed based on the crossover rate and mutation rate to generate a second parameter set. max , go to step 7, otherwise go to step 8; Step 7: Update the first parameter set according to the parameter queue and the second parameter set, q=q+1, and return to step 2; Step 8: Take the first parameter set as the target parameter set, generate intervals of the first variable parameter and the second variable parameter according to the target parameter set, and select a target parameter combination from the target parameter set based on weight coefficients of the moisture content of the tempering material and the temperature of the tempering material.

[0005] In the present invention, in step 1, the fixed parameters include the effective length of the tempering chamber, the effective diameter of the tempering chamber, the blade installation angle, and the rated speed of the blade; the first variable parameters include the raw material feed rate, the raw material humidity, and the raw material temperature; and the second variable parameters include the inlet steam flow rate, the inlet steam pressure, and the inlet steam temperature.

[0006] In the present invention, in step 1, the output parameters of the tempering production line are collected, steam latent heat is generated according to the second variable parameter and the output parameter, heat energy transfer efficiency is generated according to the fixed parameter, and the tempering process model is generated according to the energy change equation and the mass change equation.

[0007] In the present invention, in step 2, the raw material is one or more of corn flour, wheat bran flour, soybean meal or meat and bone meal, and the conditioning material is starch gelatinized material of corn flour, wheat bran flour, soybean meal or meat and bone meal.

[0008] ​In the present invention, in step 3, the first variable parameter, the second variable parameter, the heat transfer efficiency, the steam latent heat and the output parameter are substituted into the tempering process model to obtain the target moisture content and the target temperature. The output parameters include the outlet steam flow rate M3, the outlet steam pressure P2, the tempering material temperature T2, the outlet steam temperature T4, the steam latent heat H2=R / [ln(P2 / P1)(1 / T4-1 / T3)], and the energy change equation: M2c2(T2-T1)=η1[( M1-M3)c1(100-T2)+M1H2], mass change equation: (W1-W2)M2=η2(M1-M3), where R is the ideal gas constant, P1 is the inlet steam pressure, T3 is the inlet steam temperature, M1 is the inlet steam flow rate, M2 is the raw material feed rate, c1 is the specific heat capacity of water, c2 is the specific heat capacity of raw materials, T1 is the raw material temperature, η1 is the heat energy transfer efficiency, η2 is the steam dryness, W1 is the moisture content of the tempering material, and W2 is the raw material humidity.

[0009] In the present invention, in step 3, the objective function of the moisture content of the tempering material and the moisture content deviation is ΔA1=|A1-A1'|, and the objective function of the tempering material temperature and the temperature deviation is ΔA2=|A2-A2'|, wherein A1 is the moisture content of the tempering material, A2 is the tempering material temperature, A1' is the target moisture content, A2' is the target temperature, the function value ΔA1 is the moisture content deviation, and the function value ΔA2 is the temperature deviation.

[0010] In the present invention, in step 4, non-dominated parameter combinations are extracted from the first parameter set in turn according to the function values, different non-dominated sets are created according to the extraction order of the non-dominated parameter combinations, and the non-dominated queue is filled according to the creation order of the non-dominated sets.

[0011] In the present invention, in step 4, in the same non-dominated set, a moisture content sub-queue and a temperature sub-queue are respectively generated according to the function value, the crowding distance of the parameter combination is calculated according to the normalized difference between adjacent parameter combinations in the moisture content sub-queue and the temperature sub-queue, and then the mean crowding distance of the non-dominated set is calculated.

[0012] In the present invention, in step 6, if q max / 3, then C = C + ΔC and O = O + ΔO, if q max / 3≤q≤2q max / 3, then C = C-ΔC and O = O-ΔO, if 2q max / 3 <q<q max , then O=O-ΔO, where C is the crossover rate, O is the mutation rate, ΔC is the crossover rate adjustment, ΔO is the mutation rate adjustment, and C, ΔC, O and ΔO are all greater than 0 and less than 1.

[0013] ​In the present invention, in step 7, the first parameter set is initialized, at least part of the parameter combinations in the parameter queue are added to the first parameter set, at least part of the parameter combinations in the parameter queue and the second parameter set are merged into a temporary set, and then the parameter queue of the temporary set is generated, and the parameter queue of the temporary set is added to the first parameter set.

[0014] The optimization method of the raw material tempering process of the present invention has the beneficial effect that: the present invention generates a first parameter set based on the variable range of the variable parameters, and controls the tempering production line according to the parameter settings of the first parameter set. The actual moisture content and temperature of the tempering material are collected, and the target moisture content and target temperature are calculated based on the tempering process model, and the deviation between the moisture content and the target temperature is used as the objective function. The variable parameters are continuously iterated and optimized, and new parameter combinations are generated based on the existing parameter combinations to improve the diversity of parameter combinations. The interval of the variable parameters is determined by the first parameter set that is finally iterated, and the target parameter combination is determined according to the weight coefficient, so as to quickly find the preferred variable parameters under the current production conditions, thereby improving the quality of the finished product. Furthermore, the crossover rate and the mutation rate are dynamically adjusted according to different iteration stages to avoid premature convergence and loss of the target parameter combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the optimization method of the raw material conditioning process of the present invention; Figure 2 is a schematic diagram of a quenching and tempering production line; Figure 3 A process diagram for generating a non-dominated queue of the present invention; Figure 4 A schematic diagram of updating a first parameter set according to the present invention; Figure 5 The figure is a structural diagram of the tempering chamber of the tempering production line of the present invention; Figure 6 It is a schematic diagram of the tempering chamber of the tempering production line of the present invention.

[0016] Explanation of some of the reference numerals: tempering equipment 11, raw material warehouse 12, steam warehouse 13, pressure pump 14, waste gas pool 15, storage warehouse 16. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0018] Feed conditioning refers to improving the characteristics of feed raw materials by physical means, especially changing the moisture content and temperature, so as to facilitate subsequent processing, improve the quality of feed, nutritional value and digestion and absorption efficiency of animals. However, while controlling the moisture content of the conditioning material, the temperature of the conditioning material will also be affected. Therefore, in order to optimize the temperature of the conditioning material while controlling the moisture content of the conditioning material, the present invention will combine the fast non-dominated sorting and crowding distance methods of the multi-objective genetic algorithm, iteratively optimize the variable parameters on less basic data, and find the target parameter combination that meets both optimization goals. Embodiment 1

[0019] Reference Figure 1 The optimization method of the raw material conditioning process of the present invention described in detail in this embodiment comprises the following steps: Step 1: Extract the fixed parameters of the tempering production line, the first variable parameter of the raw material, and the second variable parameter of the inlet steam, generate a tempering process model, generate a parameter combination including the first variable parameter and the second variable parameter, create a first parameter set consisting of multiple parameter combinations, and initialize the number of iterations q=0. The fixed parameters include the effective length of the tempering chamber, the effective diameter of the tempering chamber, the blade installation angle, and the rated speed of the blade. The first variable parameter includes the raw material feed amount, the raw material humidity, and the raw material temperature. The second variable parameter includes the inlet steam flow rate, the inlet steam pressure, and the inlet steam temperature. The parameter combination refers to the combination of specific data of the raw material feed amount, the raw material humidity, the raw material temperature, the inlet steam flow rate, the inlet steam pressure, and the inlet steam temperature. Collect the output parameters of the tempering production line, generate steam latent heat according to the second variable parameter and the output parameter, generate heat energy transfer efficiency according to the fixed parameters, and generate the tempering process model according to the energy change equation and the mass change equation.

[0020] In this embodiment, starting from the minimum value of each data of the first variable parameter and the second variable parameter, the specific values ​​of each data of the first variable parameter and the second variable parameter are selected in equal steps, and then a parameter combination is generated, and multiple parameter combinations constitute a first parameter set. In a further embodiment, according to the rated working condition of the quenching and tempering production line, the data of the first variable parameter or the second variable parameter are divided into multiple working condition intervals, and then specific values ​​are selected from the working condition intervals to ensure that at least one specific value is selected for each working condition interval, and then a parameter combination is generated. The number of parameter combinations of the first parameter set is, for example, 50 to 100 groups, and it is necessary to comprehensively consider the limitations of the processing conditions of the quenching and tempering production line and the sample requirements of the iterative algorithm.

[0021] Step 2: Add raw materials and inlet steam to the conditioning production line, the conditioning production line produces conditioning material, the conditioning production line is controlled based on different parameter combinations in the first parameter set, and the moisture content and temperature of the conditioning material are collected. The raw materials are one or more of corn flour, wheat bran flour, soybean meal or meat and bone meal, and the conditioning material is starch gelatinized product of corn flour, wheat bran flour, soybean meal or meat and bone meal. The conditioning production line is as follows: Figure 2 As shown, inlet steam enters the tempering equipment 11 from the steam bin 13 through the pressure pump 14 , the raw material enters the tempering equipment 11 from the raw material bin 12 , the outlet steam enters the exhaust gas pool 15 , and the tempered material enters the storage bin 16 .

[0022] Step 3: According to the tempering process model, the target moisture content and target temperature are obtained, and the target function of the moisture content and moisture content deviation of the tempering material and the target function of the temperature and temperature deviation of the tempering material are established. The first variable parameter, the second variable parameter, the heat transfer efficiency, the latent heat of steam and the output parameter are substituted into the tempering process model to obtain the corresponding target moisture content and target temperature. The specific implementation method refers to Example 2. The target function of the moisture content and moisture content deviation of the tempering material is ΔA1=|A1-A1'|, and the target function of the temperature and temperature deviation of the tempering material is ΔA2=|A2-A2'|, wherein A1 is the moisture content of the tempering material, A2 is the temperature of the tempering material, A1' is the target moisture content, A2' is the target temperature, the function value ΔA1 is the moisture content deviation, and the function value ΔA2 is the temperature deviation.

[0023] Step 4: Divide the first parameter set into multiple non-dominated sets according to the function value of the objective function and generate a non-dominated queue, calculate the crowding distance of each parameter combination in the non-dominated set, and obtain the mean crowding distance of the non-dominated set. For parameter combinations B1 and B2, if the function values ​​of all objective functions of B1 are not greater than the function values ​​corresponding to B2, and at least one function value of B1 is less than the function value corresponding to B2, then B1 is considered to dominate B2. If there is no other parameter combination that can dominate a certain parameter combination, then the parameter combination is a non-dominated parameter combination.

[0024] First create an empty non-dominated queue. Figure 3 , then traverse the first parameter set, extract non-dominated parameter combinations from the first parameter set, create a non-dominated set consisting of the non-dominated parameter combinations, delete the non-dominated parameter combinations in the first parameter set, and then insert the non-dominated set into the non-dominated queue until the first parameter set is traversed. Repeat the above operation, re-traverse the remaining parameter combinations in the first parameter set, extract non-dominated parameter combinations and generate non-dominated sets, and then insert the non-dominated sets into the non-dominated queue. Until the first parameter set is empty, the filling of the non-dominated queue is finally completed.

[0025] In the same non-dominated set, the parameter combinations of the non-dominated set are arranged from small to large according to the function value ΔA1 of the moisture content deviation to generate a moisture content sub-queue. Calculate any parameter combination B in the moisture content sub-queue j Combined with adjacent parameters B j+1 and B j-1 The normalized difference d 1j =[ΔA 1(j+1) –ΔA 1(j-1) ] / (ΔA1 max -ΔA1 min ). Among them, ΔA 1(j+1) is the function value of the moisture content deviation of the j+1th parameter combination, ΔA 1(j-1) is the function value of the moisture content deviation of the j-1th parameter combination, ΔA1 max is the maximum function value of the moisture content deviation in the non-dominated queue, ΔA1 min It is the function value with the minimum moisture content deviation in the non-dominated queue.

[0026] Then, according to the function value ΔA2 of the temperature deviation, the parameter combinations of the non-dominated set are arranged from small to large to generate a temperature sub-queue. Calculate any parameter combination B in the temperature sub-queue j Combined with adjacent parameters B j+1 and B j-1 The normalized difference d 2j =[ΔA 2(j+1) –ΔA 2(j-1) ] / (ΔA2 max -ΔA2 min ). Among them, ΔA 2(j+1) is the function value of the temperature deviation of the j+1th parameter combination, ΔA 2(j-1) is the function value of the temperature deviation of the j-1th parameter combination, ΔA2 max is the maximum function value of temperature deviation in the non-dominated queue, ΔA2 min is the function value with the minimum temperature deviation in the non-dominated queue.

[0027] According to the normalized difference d 1j and d 2j Calculate the parameter combination B j The crowding distance Then the mean crowding distance of the non-dominated set is calculated by the crowding distance of multiple parameter combinations. , J is the number of parameter combinations in the non-dominated set. The crowding distances of the first and last parameter combinations in the moisture content subqueue and the temperature subqueue are usually preset to a constant greater than 1, and the constant is greater than all crowding distances.

[0028] Step 5: Extract at least one parameter combination based on the average crowding distance of the non-dominated sets in the non-dominated queue to generate a parameter queue. In this implementation, real number encoding is used to represent parameter combinations. Searching starts from the first non-dominated set according to the order of the non-dominated sets in the non-dominated queue, and the parameter combinations in the non-dominated set with a crowding distance greater than the corresponding average crowding distance are added to the parameter queue until all non-dominated sets are searched or the capacity limit of the parameter queue is reached, and finally a parameter queue in the form of a stack structure is obtained.

[0029] Step 6: Dynamically adjust the crossover rate and mutation rate based on the ratio of the current iteration number q to the maximum iteration number q max , and process the parameter queue based on the crossover rate and mutation rate to generate a second parameter set. If q < q max , go to Step 7, otherwise go to Step 8. If q < q max / 3, at this time it is the initial stage of iteration, and higher crossover rate and mutation rate are required to quickly generate diverse parameter combinations, so C = C + ΔC, and O = O + ΔO. If q max / 3 ≤ q ≤ 2q max / 3, at this time it is the middle stage of iteration, gradually reducing the probabilities of crossover and mutation can promote convergence, so C = C - ΔC and O = O - ΔO. If 2q max / 3 < q < q max , at this time it is the late stage of iteration, in the process of concentrating on optimizing the discovered parameter combinations, the crossover rate needs to be maintained and the mutation rate is further appropriately reduced, so O = O - ΔO, where C is the crossover rate, O is the mutation rate, ΔC is the crossover rate adjustment amount, ΔO is the mutation rate adjustment amount, and C, ΔC, O, and ΔO are all greater than 0 and less than 1.

[0030] In this embodiment, 0.5 < C < 0.9, ΔC = 0.005, 0.01 < O < 0.1, ΔO = 0.001. In the initial stage of iteration, when the crossover rate reaches 0.9, it will not increase anymore, and when the mutation rate reaches 0.1, it will not increase anymore; in the middle stage of iteration, when the crossover rate reaches 0.5, it will not decrease anymore, and when the mutation rate reaches 0.01, it will not decrease anymore; in the late stage of iteration, the crossover rate remains unchanged. If O > 0.01, the mutation rate continues to decrease until the iteration ends or the mutation rate reaches 0.01. Crossover operation: Randomly select two parameter combinations from the parameter queue, and decide whether to perform the crossover operation according to the crossover rate to generate the first offspring parameter combination. Mutation operation: Decide whether the first offspring parameter combination mutates according to the mutation rate to generate the second offspring parameter combination, and generate the second parameter set according to the second offspring parameter combination, specifically referring to that described in Embodiment 3.

[0031] Step 7: Update the first parameter set according to the parameter queue and the second parameter set, q = q + 1, and return to Step 2. Refer to Figure 4, initialize the first parameter set, the top parameter combinations in the parameter queue are of high importance and are necessary to participate in the next genetic iteration. In this embodiment, the top 20% to 30% of the parameter combinations in the parameter queue are directly reserved and added to the first parameter set. Then the remaining parameter combinations in the parameter queue and all parameter combinations in the second parameter set are merged into a temporary set. If there are repeated parameter combinations, one of the parameter combinations is retained. Referring to steps 4 and 5, a parameter queue of a temporary set is generated using a method based on non-dominated sorting and crowding distance, and the parameter queue of the temporary set is added to the first parameter set until the first parameter set reaches the capacity limit or the parameter queue of the temporary set is empty.

[0032] Step 8: Take the first parameter set as the target parameter set, generate the interval of the first variable parameter and the second variable parameter according to the target parameter set, and select the target parameter combination from the target parameter set based on the weight coefficient of the moisture content of the tempering material and the tempering material temperature. The interval of the first variable parameter and the second variable parameter is generated by the method based on the mean and standard deviation. Calculate the mean and standard deviation of each first variable parameter and the mean and standard deviation of the second variable parameter in the target parameter set. The upper limit of the interval of the first variable parameter and the second variable parameter is the corresponding mean plus twice the standard deviation, and the lower limit of the interval of the first variable parameter and the second variable parameter is the corresponding mean minus twice the standard deviation. In actual production, the first variable parameter and the second variable parameter can be fine-tuned based on the interval, which helps to quickly respond to small deviations in the production process and ensure that the production line always operates in the best state.

[0033] The weight coefficients of the moisture content of the quenching and tempering material and the temperature of the quenching and tempering material can be determined according to the emphasis of the actual production demand, and the target parameter combination is selected from the target parameter set based on the weight coefficient. Since the temperature of the quenching and tempering material can be adjusted again in the subsequent granulation process, but the water content directly affects the product quality, in a simpler embodiment, the weight coefficients of the preset moisture content of the quenching and tempering material and the temperature of the quenching and tempering material are 0.6 and 0.4 respectively. The present invention can also use a multi-objective comprehensive model to evaluate the parameter combination in the target parameter set, that is, using, for example, the CRITIC algorithm to quantify the conflict of two objective functions, determine the weight coefficient of each objective function, each parameter combination corresponds to a set of function values ​​of the objective function, and further based on the TOPSIS algorithm and the weight coefficient, the function value is comprehensively scored. The larger the comprehensive score, the smaller the comprehensive error of the corresponding parameter combination. Select the parameter combination with the largest score as the target parameter combination. Embodiment 2

[0034] This embodiment further discloses a method for obtaining a target moisture content and a target temperature according to a quenching and tempering process model.

[0035] Flow sensors, pressure sensors, temperature sensors and other devices are installed on the quenching and tempering production line to collect the output parameters of the quenching and tempering production line, including outlet steam flow M3, outlet steam pressure P2, quenching and tempering material temperature T2, and outlet steam temperature T4.

[0036] The latent heat of steam is generated according to the second variable parameter and the output parameter. According to the Clausius-Clapeyron equation, the latent heat of steam H2=R / [ln(P2 / P1)(1 / T4-1 / T3)], where P1 is the inlet steam pressure, T3 is the inlet steam temperature, and R is the ideal gas constant, which is 8.314 J / (mol·K). At this time, the temperature is expressed in Kelvin.

[0037] The heat transfer efficiency is generated according to the fixed parameters. The heat conversion efficiency η1=1-exp(-t / t0), the effective tempering time t=L1 / (2NtanαL2f), such as Figure 5 , L1 is the effective length of the tempering chamber, L2 is the effective diameter of the tempering chamber, α is the blade installation angle, N is the rated speed of the blade, and f is the damping coefficient of the raw material, which is generally 0.2-0.4. t0 is the reference tempering time. According to the blade structure and material type of the tempering production line, the reference tempering time can be 0.6 to 1 minute. In another embodiment, the fixed parameters include heat transfer efficiency and humidity absorption rate. When the tempering production line leaves the factory, the heat transfer efficiency is calibrated by testing, and the heat conversion efficiency is usually 60%.

[0038] The quenching and tempering process model is generated based on the energy change equation and the mass change equation. Energy change equation: M2c2(T2-T1)=η1[(M1-M3)c1(100-T2)+M1H2]. During the quenching and tempering process, the raw material and the inlet steam enter the quenching and tempering chamber, and the outlet steam and quenching and tempering material are discharged from the quenching and tempering chamber. Figure 6 , the raw material and the inlet steam bring in moisture, the outlet steam and the tempering material bring out moisture, and the tempering material absorbs condensed water. The mass change of moisture is absorbed by the tempering chamber, that is, the mass change equation: (W1-W2)M2=η2(M1-M3), where M1 is the inlet steam flow, M2 is the raw material feed amount, c1 is the water specific heat capacity, c2 is the raw material specific heat capacity, T1 is the raw material temperature, W1 is the moisture content of the tempering material, W2 is the raw material humidity, and η2 is the steam dryness, which is usually 70%-90%. The temperature in the tempering process model is expressed in degrees Celsius, and other units are in the International System of Units.

[0039] The first variable parameter, the second variable parameter, the heat energy transfer efficiency, the moisture absorption rate, the steam latent heat and the output parameters of the current parameter combination are substituted into the tempering process model to obtain the target moisture content and the target temperature under the current parameter combination. Embodiment 3

[0040] This embodiment further discloses a method for processing a parameter queue with a crossover rate and a mutation rate and generating a second parameter set in step 6.

[0041] Parameter initialization: Preset a crossover distribution index λ1, and the range of λ1 is [1, 20]. A smaller value of λ1 will cause the offspring parameter combinations to be more closely distributed around the parameter combination, reducing the diversity of the parameter combinations. A larger value of λ1 will make the offspring parameter combinations more widely distributed, increasing the diversity of the parameter combinations but reducing the convergence speed. At the same time, preset a mutation distribution index λ2 to control the magnitude of the mutation, and the range of λ2 is [1, 20]. In this embodiment, λ1 is set to 10 and λ2 is set to 20.

[0042] Crossover parameter preset: Select a random parameter.

[0043] Parameter crossover operation: Randomly select two parameter combinations B1 and B2 from the parameter queue. If u1 < C, use the crossover algorithm to generate the first offspring parameter combinations B1' and B2'. Otherwise, copy the parameter combinations B1 and B2 as the first offspring parameter combinations, that is, B1' = B1, B2' = B2.

[0044] Mutation parameter preset: Select a random parameter.

[0045] Parameter mutation operation: If u2 < O, use the mutation algorithm on the first offspring parameter combinations B1' and B2' to generate new second offspring parameter combinations B1'' and B2''. Otherwise, copy the first offspring parameter combinations B1' and B2' as the second offspring parameter combinations, that is, B1'' = B1', B2'' = B2'.

[0046] If the remaining parameter combinations in the parameter queue are not empty, return to the crossover parameter preset step. If the remaining parameter combinations in the parameter queue are empty, merge all the second offspring parameter combinations into the second parameter set.

[0047] Among them, the preferred crossover algorithm in this embodiment is as follows.

[0048] Calculate the crossover parameter . As described in Embodiment 1, the parameter combination B1 is a set composed of a first variable parameter and a second variable parameter. Any element b in B1 1n The corresponding element in the first offspring parameter combination B1' is b 1n ', . Any element b in B2 2n The corresponding element in the first offspring parameter combination B2' is b 2n ', .

[0049] The preferred mutation algorithm in this embodiment is as follows.

[0050] Calculate the variation parameter Any element b in B1' 1n 'The corresponding element in the second child parameter combination B1'' is b 1n '', , any element b in B2' 2n 'The corresponding element in the second child parameter combination B2'' is b 2n '', , where b 1n and b 2n are the nth elements in B1 and B2 respectively, b n max is the maximum value of the nth element in the parameter queue, b n min The minimum value of the nth element in the parameter queue.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing a raw material conditioning process, characterized in that: The following steps are involved: Step 1: extract the fixed parameters of the tempering production line, the first variable parameters of the raw materials, and the second variable parameters of the inlet steam, generate a tempering process model, generate a parameter combination including the first variable parameter and the second variable parameter, create a first parameter set consisting of multiple parameter combinations, and initialize the number of iterations q=0; Step 2: raw materials and inlet steam are input into the quenching and tempering production line, the quenching and tempering production line produces quenching and tempering materials, the quenching and tempering production line is controlled based on different parameter combinations in the first parameter set, and the moisture content and temperature of the quenching and tempering materials are collected; Step 3: Obtain the target moisture content and target temperature according to the quenching and tempering process model, and establish the objective function of the quenching and tempering material moisture content and moisture content deviation, and the objective function of the quenching and tempering material temperature and temperature deviation; Step 4: Divide the first parameter set into multiple non-dominated sets according to the function value of the objective function and generate non-dominated queues, calculate the crowding distance of the parameter combination in each non-dominated set, and obtain the mean crowding distance of the non-dominated set; Step 5: extract at least one parameter combination based on the mean of the crowding distance of the non-dominated set in the non-dominated queue to generate a parameter queue; Step 6: Based on the current number of iterations q and the maximum number of iterations q max The crossover rate and mutation rate are dynamically adjusted according to the ratio of q, and the parameter queue is processed based on the crossover rate and mutation rate to generate a second parameter set. max , go to step 7, otherwise go to step 8;​ Step 7: Update the first parameter set according to the parameter queue and the second parameter set, q=q+1, and return to step 2; Step 8: Take the first parameter set as the target parameter set, generate intervals of the first variable parameter and the second variable parameter according to the target parameter set, and select a target parameter combination from the target parameter set based on weight coefficients of the moisture content of the tempering material and the temperature of the tempering material.

2. The method for optimizing the raw material tempering process according to claim 1, characterized in that: In step 1, the fixed parameters include the effective length of the tempering chamber, the effective diameter of the tempering chamber, the blade installation angle, and the rated speed of the blade; the first variable parameters include the raw material feed rate, raw material humidity, and raw material temperature; and the second variable parameters include the inlet steam flow rate, inlet steam pressure, and inlet steam temperature.

3. The method for optimizing the raw material tempering process according to claim 2, characterized in that: In step 1, the output parameters of the tempering production line are collected, steam latent heat is generated according to the second variable parameter and the output parameter, heat energy transfer efficiency is generated according to the fixed parameter, and a tempering process model is generated according to the energy change equation and the mass change equation.

4. The method for optimizing the raw material tempering process according to claim 1, characterized in that: In step 2, the raw material is one or more of corn flour, wheat bran flour, soybean meal or meat and bone meal, and the conditioning material is starch gelatinized product of corn flour, wheat bran flour, soybean meal or meat and bone meal.

5. The method for optimizing the raw material conditioning process according to claim 3, characterized in that: In step 3, the first variable parameter, the second variable parameter, the heat transfer efficiency, the steam latent heat and the output parameters are substituted into the tempering process model to obtain the target moisture content and the target temperature. The output parameters include the outlet steam flow rate M3, the outlet steam pressure P2, the tempering material temperature T2, the outlet steam temperature T4, the steam latent heat H2=R / [ln(P2 / P1)(1 / T4-1 / T3)], and the energy change equation: M2c2(T2-T1)=η1[(M1- M3)c1(100-T2)+M1H2], mass change equation: (W1-W2)M2=η2(M1-M3), where R is the ideal gas constant, P1 is the inlet steam pressure, T3 is the inlet steam temperature, M1 is the inlet steam flow rate, M2 is the raw material feed rate, c1 is the specific heat capacity of water, c2 is the specific heat capacity of raw materials, T1 is the raw material temperature, η1 is the heat energy transfer efficiency, η2 is the steam dryness, W1 is the moisture content of the tempering material, and W2 is the raw material humidity.

6. The method for optimizing the raw material conditioning process according to claim 1, characterized in that: In step 3, the objective function of the moisture content of the tempering material and the moisture content deviation is ΔA1=|A1-A1'|, and the objective function of the tempering material temperature and the temperature deviation is ΔA2=|A2-A2'|, wherein A1 is the moisture content of the tempering material, A2 is the tempering material temperature, A1' is the target moisture content, A2' is the target temperature, the function value ΔA1 is the moisture content deviation, and the function value ΔA2 is the temperature deviation.

7. The method for optimizing the raw material conditioning process according to claim 6, characterized in that: Non-dominated parameter combinations are extracted from the first parameter set in sequence according to the function values, different non-dominated sets are created according to the extraction order of the non-dominated parameter combinations, and the non-dominated queues are filled according to the creation order of the non-dominated sets.

8. The method for optimizing the raw material conditioning process according to claim 6, characterized in that: In step 4, in the same non-dominated set, a moisture content sub-queue and a temperature sub-queue are generated according to the function value, and the crowding distance of the parameter combination is calculated according to the normalized difference between adjacent parameter combinations in the moisture content sub-queue and the temperature sub-queue, and then the mean crowding distance of the non-dominated set is calculated.

9. The method for optimizing the raw material conditioning process according to claim 1, characterized in that: In step 6, if q max / 3, then C = C + ΔC and O = O + ΔO, if q max / 3≤q≤2q max / 3, then C = C-ΔC and O = O-ΔO, if 2q max / 3 <q<q max , then O=O-ΔO, where C is the crossover rate, O is the mutation rate, ΔC is the crossover rate adjustment, ΔO is the mutation rate adjustment, and C, ΔC, O and ΔO are all greater than 0 and less than 1.​ 10. The method for optimizing the raw material tempering process according to claim 1, characterized in that: In step 7, the first parameter set is initialized, at least part of the parameter combinations in the parameter queue are added to the first parameter set, at least part of the parameter combinations in the parameter queue and the second parameter set are merged into a temporary set, a parameter queue of the temporary set is generated, and the parameter queue of the temporary set is added to the first parameter set.

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