Kitchen garbage drying process parameter optimization method

Abstract

The invention discloses a kitchen garbage drying process parameter optimization method. The method comprises the specific steps that S1, drying time and power consumption serve as optimization targets, drying temperature, hot air speed and garbage granularity serve as to-be-optimized parameters, and the optimization value range of each parameter is determined through a single-factor experiment; s2, based on the to-be-optimized parameters and the value range thereof, generating an experimental scheme combination by utilizing Box-Behnken response analysis, performing a drying experiment and obtaining a result, and fitting to obtain a regression model between the parameters and an optimization target; s3, performing multi-objective optimization based on the regression model, and determining an optimal parameter combination; and S4, establishing a dryer simulation model, carrying out analogue simulation according to the optimal parameter combination, checking whether the drying time and the power consumption reach the standard or not, and taking the combination passing the checking as an optimization result. According to the method, the optimal design value of each parameter in the kitchen garbage drying device can be quickly and efficiently determined, and a theoretical basis is provided for the design of the kitchen garbage drying device.

Description

Technical Field

[0001] The invention relates to the field of kitchen waste treatment, and in particular to a method for optimizing process parameters of kitchen waste drying. Background Art

[0002] Kitchen waste refers to a type of solid waste generated by residents in their daily lives and in the catering industry, including vegetables, fruit peels, leftovers, and livestock and poultry offal. Kitchen waste is rich in organic matter such as protein and fat, and is very easy to rot and stink, breed bacteria, and thus spread diseases and affect residents' lives.

[0003] With the improvement of living standards and the booming catering industry, the amount of kitchen waste has shown an increasing trend year by year. However, many cities currently have insufficient kitchen waste resource processing facilities. Although a series of measures have been taken, the traditional terminal processing method cannot fundamentally solve the problem of continuous, large-scale and continuously growing kitchen waste. On-site treatment of kitchen waste is an effective way to curb the proliferation of kitchen waste. This approach can not only reduce secondary pollution generated during transportation, but also make better use of resources and realize resource recycling.

[0004] As one of the local treatment technologies, food waste drying has the advantages of saving space, reducing odor, and reducing transportation costs. However, for different drying devices, due to differences in their internal structures and drying methods, the appropriate drying parameters are different. If the drying parameters are not selected properly, it will lead to long drying and dehydration time, low efficiency, and even bacterial growth and odor. However, there is currently no good solution for how to efficiently optimize the food waste drying process parameters in the drying device. Summary of the invention

[0005] The purpose of the present invention is to solve the problem in the prior art that it is difficult to efficiently optimize the drying process parameters of a kitchen waste drying device, and to provide an optimization method for kitchen waste drying process parameters such as drying temperature, hot air velocity, and garbage particle size based on the response surface method, aiming to improve the targeted optimization of the drying process efficiency in the target dryer and reduce the power consumption of the drying device, thereby solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the technical solution specifically adopted by the present invention is as follows:

[0007] A method for optimizing process parameters of kitchen waste drying comprises the following steps:

[0008] S1. Taking drying time and power consumption as two optimization targets, and taking drying temperature, hot air speed and garbage particle size as three parameters to be optimized, a single factor experiment is conducted based on the target dryer to determine the optimal value range of each parameter to be optimized;

[0009] S2. Based on the three parameters to be optimized and their respective optimization value ranges, a three-factor multi-level experimental scheme combination is generated through Box-Behnken response analysis; according to each experimental scheme in the experimental scheme combination, a kitchen waste drying experiment is performed respectively and the experimental results of drying time and power consumption are obtained, and a regression model between the three parameters to be optimized and each optimization target is obtained by fitting according to the experimental results of all experimental schemes;

[0010] S3. Based on the two regression models obtained by fitting, the optimal parameter combination of the kitchen waste drying process is determined through multi-objective optimization;

[0011] S4. Establish a simulation model of the target dryer, and perform simulation according to the optimal parameter combination to check whether the drying time and power consumption meet the preset requirements under the optimal parameter combination, and use the optimal parameter combination that passes the test as the optimization result of the kitchen waste drying process parameters.

[0012] Preferably, the single factor experiment in step S1 includes selecting drying temperature, hot air speed and garbage particle size as independent variables, and experimentally analyzing the drying time and power consumption of each independent variable at different values, so as to determine the optimal value range of each of the three parameters to be optimized.

[0013] Preferably, the single factor experiment in step S1 adopts a simulation experiment or an actual experiment.

[0014] Preferably, in step S2, a three-factor three-level experimental scheme combination is generated by Box-Behnken response analysis.

[0015] Preferably, in step S2, the regression model between the three parameters to be optimized and each optimization target adopts a quadratic polynomial regression model.

[0016] Preferably, in step S3, when determining the optimal parameter combination of the kitchen waste drying process through multi-objective optimization, the optimization goal is set to minimize power consumption when the drying time is within a preset range.

[0017] Preferably, in step S3, multiple groups of optimal parameter combinations need to be generated, and each group of optimal parameter combinations executes S4 separately. According to the drying time and power consumption in the simulation results, the best group is selected from all the optimal parameter combinations as the optimization result of the kitchen waste drying process parameters.

[0018] Preferably, in step S3, the two regression models obtained by fitting need to pass accuracy test analysis in advance before being used for multi-objective optimization. If the test fails, the regression model needs to be rebuilt.

[0019] Preferably, the accuracy test analysis includes variance analysis, fitting accuracy analysis, residual analysis, and comparative analysis of predicted values ​​and actual values.

[0020] Preferably, the Box-Behnken response analysis, the accuracy test analysis, and the multi-objective optimization are all performed by Design-Expert software.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1) Under the premise of ensuring the drying effect of kitchen waste, the present invention optimizes the drying parameters through a regression model based on the response surface method, which not only shortens the drying time but also reduces energy consumption.

[0023] 2) The present invention can consider multiple influencing factors at the same time, and systematically evaluate the influence of each factor on the drying effect by designing experiments and establishing mathematical models. This comprehensive consideration can avoid the complex relationships that may be ignored in single-factor experiments, and help find a more optimal combination of process parameters.

[0024] 3) Compared with traditional single-factor experiments, the present invention obtains more comprehensive information through fewer experiments, thereby saving time and resources and improving experimental efficiency.

[0025] 4) The present invention fits the response surface through statistical methods to more accurately describe the nonlinear relationship in the drying process, which helps to predict the possible results under different drying conditions, thereby providing a scientific basis for process improvement.

[0026] 5) The test scheme of the present invention is simple and easy to understand, which is convenient for engineers to adjust and optimize in the drying process. It can adapt to different types of kitchen waste and different drying equipment, has good versatility, and provides practical guidance for actual production. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flow chart of the method for optimizing process parameters of kitchen waste drying of the present invention;

[0028] Figure 2 is a response surface diagram of the drying temperature, heating wind speed and drying time of the present invention;

[0029] Figure 3 It is a response surface diagram of the drying temperature, garbage particle size and drying time of the present invention;

[0030] Figure 4 It is the response surface diagram of the heating wind speed, garbage particle size and drying time of the present invention;

[0031] Figure 5is a response surface diagram of the drying temperature, heating wind speed and power consumption of the present invention;

[0032] Figure 6 It is a response surface diagram of the drying temperature, garbage particle size and power consumption of the present invention;

[0033] Figure 7 It is a response surface diagram of the heating wind speed, garbage particle size and power consumption of the present invention;

[0034] Figure 8 It is a standard residual analysis diagram of the drying time regression model of the present invention;

[0035] Fig. 9 is a standard residual analysis diagram of the power consumption regression model of the present invention;

[0036] Fig.10 It is a comparison chart of the predicted value of the regression model and the actual value of the drying time of the present invention;

[0037] Fig.11 It is a comparison chart of the predicted value of the power consumption by the regression model of the present invention and the actual value;

[0038] Fig.12 It is a schematic diagram of the structure of the experimental device of the present invention;

[0039] Fig.13 Schematic diagram of temperature change in the drying process in an embodiment of the present invention;

[0040] Fig.14 It is the temperature variation diagram of the dryer of the present invention;

[0041] Fig.15 It is a fluid trajectory diagram of the dryer of the present invention.

[0042] Fig.12 In the figure, 1-box; 2-material bin; 3-flow equalizing plate; 4-inclined plate; 5-PTC heater placement bin; 6-PTC heater; 7-fan; 8-air duct; 9-temperature controller; 10-ventilation port; 11-top cover. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0044] In the description of the present invention, it is to be understood that when an element is considered to be "connected" to another element, it may be directly connected to the other element or indirectly connected, that is, there are intermediate elements. On the contrary, when an element is said to be "directly" connected to another element, there are no intermediate elements.

[0045] like Figure 1 As shown, in an embodiment of the present invention, the method for optimizing the process parameters of food waste drying includes the following specific steps:

[0046] Step 1: Determine the optimization target and parameters to be optimized of the drying process. The optimization target includes drying time and power consumption, and the parameters to be optimized include drying temperature, hot air speed and garbage particle size. Perform a single factor test based on the target dryer to determine the optimization value range of each parameter to be optimized.

[0047] In this embodiment, the drying temperature, hot air speed and garbage particle size are selected as independent variables, the drying time and power consumption of the drying process under different values ​​of each factor are analyzed, and the preferred value range of each parameter to be optimized is determined.

[0048] The dryer targeted in the present invention is a drying device that utilizes a heating component to heat the crushed kitchen waste material, and simultaneously uses a fan to assist in dehumidifying the heated material. The drying temperature refers to the set temperature of the internal heating component of the dryer when it is working, the hot air speed refers to the set speed of the fan, and the garbage particle size refers to the particle size of the crushed kitchen waste material built into the dryer.

[0049] In the Box-Behnken response surface analysis process, single factor experiment is an important step to initially explore the influence of each independent factor on the optimization target. The main purpose of single factor experiment is to determine the optimal value or significant influence area of ​​each factor (i.e., the parameter to be optimized) on the response value within a specific range, providing a basis for subsequent multi-factor response surface analysis. Generally speaking, when a single factor experiment is conducted on one of the independent variables, the other independent variables need to be fixed.

[0050] It should be noted that the single-factor experiment here can be a simulation experiment, or it can be an actual experiment based on the target dryer. The simulation experiment has higher experimental efficiency, but the actual experiment is more in line with the actual situation. In this embodiment, an actual experiment is carried out based on the target dryer to accurately obtain real data. The above three parameter factors are each set with 5 different gradients, among which: the drying temperature is 60°C, 80°C, 100°C, 120°C, and 140°C, the hot air speed is 1.2m / s, 1.4m / s, 1.6m / s, 1.8m / s, and 2.0m / s, and the garbage particle size is 4mm, 8mm, 12mm, 16mm, and 20mm. Taking the drying temperature as an example, Fig.131 is a schematic diagram of temperature change controlled by a 100° C. temperature controller in this embodiment. In this embodiment, the rated temperature of the temperature controller is the drying temperature required for the experiment. It can be seen that the drying temperature measured fluctuates to a certain extent compared with the set rated temperature.

[0051] In this embodiment, after single factor analysis of the experimental results, the optimal range of the drying temperature is finally selected to be 80-120°C, the optimal range of the hot air speed is 1.4-1.8m / s, and the optimal range of the garbage particle size is 4-12mm.

[0052] Step 2: Based on the three parameters to be optimized and their respective optimization value ranges, a three-factor multi-level experimental scheme combination is generated through Box-Behnken response analysis. According to each experimental scheme in the experimental scheme combination, a kitchen waste drying experiment is carried out and the experimental results of drying time and power consumption are obtained. According to the experimental results of all experimental schemes, a regression model between the three parameters to be optimized and each optimization target is fitted.

[0053] In this embodiment, a response surface model is constructed by the Box-Behnken method in Design-Expert, and a kitchen waste drying experiment is carried out based on the response surface model to obtain experimental results. Specifically, according to the results of the single factor test, with a drying temperature of 100°C, a heating wind speed of 1.6m / s, and a garbage particle size of 8mm as the center, a factor level table of three factors and three levels is generated by Box-Behnken response analysis according to the Box-Behnken method in Design-Expert 10.0, as shown in Table 1.

[0054] Table 1. Factor level table of multi-factor experiment

[0055]

[0056] Based on the Box-Behnken factor level table shown in Table 1, a three-factor three-level experimental scheme combination can be further designed and generated in Design-Expert, and then a kitchen waste drying experiment is performed according to each experimental scheme in the experimental scheme combination (corresponding to a set of three-factor values) to obtain the experimental results of drying time and power consumption. Similarly, the kitchen waste drying experiment here can be a simulation experiment, or an actual experiment can be performed based on the target dryer. In this embodiment, an actual kitchen waste drying experiment was performed based on the target dryer, and finally the experimental results of 14 groups of experimental schemes in the experimental scheme combination were obtained. The experimental scheme combination and the experimental results obtained in this embodiment are shown in Table 2.

[0057] Table 2 Multi-factor experimental design and results

[0058]

[0059]

[0060] According to the experimental results of all experimental schemes in Table 2, the regression model between the three parameters to be optimized and each optimization target can be fitted. In this embodiment, the regression model between the three parameters to be optimized and each optimization target adopts a quadratic polynomial regression model. The quadratic polynomial regression model includes a linear term (K0+K1A+K2B+K3C) and a quadratic term (K 12 AB+K 13 AC+K 23 BC+K 11 A 2 +K 22 B 2 +K 33 C 2 ), the existence of quadratic terms enables the model to describe the nonlinear relationship between parameters, thereby fitting the experimental data more accurately. Specifically, the regression model formulas between drying temperature, hot air speed, garbage particle size, drying time, and power consumption are as follows:

[0061] Y1=K0+K1A+K2B+K3C+K 12 AB+K 13 AC+K 23 BC+K 11 A 2 +K 22 B 2 +K 33 C 2

[0062] Y2=K0+K1A+K2B+K3C+K 12 AB+K 13 AC+K 23 BC+K 11 A 2 +K 22 B 2 +K 33 C 2

[0063] Among them, Y1 is the drying time, Y2 is the power consumption, A is the drying temperature, B is the hot air speed, C is the garbage particle size, K0, K1, K2, K3, K 12 , K 13 , K 23 , K 11 , K 22 , K 33 is the fitting coefficient.

[0064] In this embodiment, after data processing and quadratic polynomial regression fitting of the experimental results in Table 2 according to the principle of least squares method, the regression model of drying time and power consumption is obtained as follows:

[0065] Y1=176.375-7.7938A+510B-1.71875C-0.3125AB-5.00534*10 -17 AC-2.1875BC+0.031875A 2 -150.0B 2 +0.48438C 2

[0066] Y2=4.8992-0.04383A-2.477B+9.8958*10 -3 C-1.875*10 -3 AB+1.5625*10 -4 AC-0.05BC+2.167*10 -4 A 2 +0.97917B 2 +4.9479*10 -3 C 2

[0067] Based on the regression model obtained by the above fitting, the response surface diagram between different factors can be drawn. Figure 2 is the response surface diagram of drying temperature, heating wind speed and drying time; Figure 3 is the response surface diagram of drying temperature, garbage particle size and drying time; Figure 4 is the response surface diagram of heating wind speed, garbage particle size and drying time; Figure 5 is the response surface diagram of drying temperature, heating wind speed and power consumption; Figure 6 is the response surface diagram of drying temperature, garbage particle size and power consumption; Figure 7 It is the response surface diagram of heating wind speed, garbage particle size and power consumption.

[0068] Step 3: Perform accuracy test analysis on the two fitted regression models. The analysis dimensions include variance analysis, fitting accuracy analysis, residual analysis, and comparative analysis of predicted values ​​and actual values.

[0069] Before the two regression models obtained by fitting are used for multi-objective optimization, they need to pass the accuracy test in advance. The purpose of the analysis is to test the accuracy and reliability of the regression model and ensure that the best parameter combination obtained by optimization can be close to the true optimal solution.

[0070] In this embodiment, the constructed regression model is analyzed as follows:

[0071] 1) Analysis of variance: Evaluate the impact of each variable factor on the model significance, so as to compare the accuracy of the two regression models. The model significance is determined by the F value and the P value. The larger the F value and the smaller the P value, the stronger the model significance. When the P value is greater than 0.05, the model is not significant; when the P value is less than 0.05, the model is significant, especially when the P value is less than 0.0001, it means that the model is extremely significant.

[0072] 2) Fitting accuracy analysis: The model's complex correlation coefficient (R 2 ) represents the correlation of each model. The larger its value is, the better the model fit is. and the predicted coefficient of determination When the difference is less than 0.2, and the larger the values ​​of the two are, the better the explanatory power of the model is; the coefficient of variation (CV) is used to measure the relative variability of the data set, which is defined as the ratio of the standard deviation to the mean. When it is less than 10%, it indicates that the test results are reliable; sufficient precision (AdeqPrecision) measures the accuracy of the model prediction and is used to judge the predictive ability of the model. Its value greater than 4 proves that the model has sufficient precision.

[0073] 3) Residual analysis: Residual analysis can test the relevance of the model and the reliability of the data. When performing regression analysis, it is necessary to pay attention to the residual value of the model to verify the rationality of the model. Without considering the abnormal points of the experiment, the residual should obey the normal distribution.

[0074] 4) Comparative analysis of predicted values ​​and actual values: The closer the predicted value of the response surface model is to the actual value, the higher the prediction accuracy of the regression model is, and the more reasonable the regression model is.

[0075] In this embodiment, the above accuracy test analysis was performed using Design-Expert software, and the specific results are shown as follows: The variance analysis is shown in Table 3, the P value of the regression model of drying time is <0.0001, the model performance is extremely significant, the P value of the lack of fit item is 0.7492>0.05, indicating that the model fit is good, and the F values ​​of the linear items are 2521.71, 18.84 and 109.92, respectively, indicating that the order of influence of each factor on the drying time is drying temperature> garbage particle size> hot air speed. The P value of the regression model of power consumption is 0.0056<0.001, the model is highly significant, the P value of the lack of fit item is 0.4343>0.05, indicating that the model fit is good, and the F values ​​of the linear items are 22.22, 1.14 and 69.53, respectively, indicating that the order of influence of each factor on power consumption is garbage particle size> drying temperature> hot air speed.

[0076] Table 3. Analysis of variance of regression model

[0077]

[0078] The fitting accuracy analysis is shown in Table 4. The R 2 and Both are close to 1. and The difference between them is less than 0.2, indicating that the model has a sufficiently high fitting accuracy and can accurately reflect the experimental results. The CV of the two models is less than 10%, and the Adeq Precision is greater than 4, indicating that the model has greater authenticity and repeatability.

[0079] Table 4 Regression model fitting accuracy analysis

[0080]

[0081] Residual analysis of the drying time regression model is shown in Figure 8 , the residual analysis of the power consumption regression model is shown in Fig. 9 , the scattered points in the two residual analysis graphs are evenly distributed on a straight line, indicating that the residuals of the two models are normally distributed and no abnormal data points are found.

[0082] In addition, the comparison chart of the predicted value and actual value of the regression model for drying time and power consumption is shown in Fig.10 and Fig.11 The scatter points of the regression model are evenly distributed on the y=x line and on both sides of the line, indicating that the predicted value of the model is consistent with the experimental value and the fitting effect is good.

[0083] Therefore, the two regression models fitted in this embodiment pass the accuracy test analysis and can be used for multi-objective optimization. However, if the two regression models do not pass the accuracy test analysis in this step, it is necessary to re-construct the regression models according to the previous steps.

[0084] Step 4: Based on the two regression models obtained through accuracy test analysis, the optimal parameter combination of the kitchen waste drying process is determined through multi-objective optimization.

[0085] It should be noted that when determining the optimal parameter combination of the kitchen waste drying process through multi-objective optimization, the corresponding optimization target can be adjusted according to the actual performance focus of the dryer. For example, in this embodiment, the optimization target is set to minimize power consumption when the drying time is within a preset range. In addition, during the multi-objective optimization process, one or more sets of optimal parameter combinations can be generated as needed.

[0086] Specifically, this embodiment uses the Numeral function of Design-Expert, sets the target value of power consumption to minimize, and the target value of drying time to 70-90 minutes, and obtains 5 groups of optimal parameter combinations for the drying process as shown in Table 5.

[0087] Table 5 Best parameter combination table

[0088]

[0089] Each set of optimal parameter combinations is individually tested in the subsequent steps of simulation, and those that pass the test can be used as candidate optimization results for the food waste drying process parameters. Of course, the best set can also be selected from all the optimal parameter combinations according to the drying time and power consumption in the simulation results as the optimization result of the food waste drying process parameters.

[0090] In this embodiment, since the control parameters in the above five optimal parameter combinations are all multiple decimal places, in order to reduce the difficulty of actual process control, these five parameter groups can be comprehensively optimized and corrected to a drying temperature of 100°C, a hot air speed of 1.5m / s, and a garbage particle size of 5mm. The corrected set of parameters is used for simulation test in subsequent steps.

[0091] Step 5: Based on the three-dimensional model of the target dryer, a simulation model of the target dryer is established in the simulation software, and simulation is performed according to the optimal parameter combination corrected in the above step 4 to verify whether the drying time and power consumption meet the preset requirements under the optimal parameter combination, and the optimal parameter combination that passes the test is used as the optimization result of the kitchen waste drying process parameters.

[0092] In this embodiment, the kitchen waste dryer is designed as follows Fig.12As shown, it includes a box body (1), a material bin (2), a flow equalizing plate (3), an inclined plate 4, a PTC heater placement bin 5, a PTC heater (6), a centrifugal fan (7), an air duct 8, a temperature controller (9), a vent 10, and a top cover 11. The material bin (2) is built into the box body (1) and is used to accommodate crushed materials, and the particle size of the materials is the particle size of garbage. The material bin (2) has handles at both ends, and a diamond iron plate mesh is welded at the bottom as a material support. The top cover 11 can be opened to add or remove materials. A flow equalizing plate (3) with uniform openings is set below the material bin (2) for smooth airflow. An air inlet and an air outlet with adjustable size are set on the side of the box body 1. The air inlet is located on the side wall of the space below the flow equalizing plate (3) in the box body (1), and the air outlet is located on the side wall of the space above the material bin (2) in the box body (1). The space below the flow equalizing plate (3) is also provided with a PTC heater (6) placed in a PTC heater placement bin 5, and an inclined plate 4 facing the air inlet direction. The PTC heater (6) is a heating component of the dryer, and its rated temperature is controlled by a temperature controller (9). The rated temperature set in the temperature controller (9) is the drying temperature. After the centrifugal fan (7) blows air into the box (1) through the air inlet, the air is heated by the PTC heater (6) to form hot air, and then the hot air is evenly distributed through the flow equalizing plate (3) under the guidance of the inclined plate 4 and passes through the material bin (2), thereby discharging the moisture of the internal material, and then returning to the centrifugal fan (7) through the air duct 8 to continue the cycle. Part of the hot and humid air will be ventilated with the external atmosphere through the vent 10. The wind speed set by the centrifugal fan (7) is the hot air speed.

[0093] Based on the three-dimensional model of the dryer product, it is imported into the simulation software for finite element simulation to verify whether the drying time and power consumption under the optimal parameter combination meet the preset requirements. It should be noted that the specific finite element simulation method and software are not limited and can be selected according to actual needs.

[0094] In this embodiment, fluid heat transfer simulation is performed based on FLUENT, and the specific steps are as follows:

[0095] S5-1, simplify the 3D model of the dryer, repair the small faces and erroneous faces in the model, perform the flow domain extraction operation on the modified model, name the contact surfaces between the solid domain and the fluid domain, the inlet and outlet surfaces of the fluid domain, the other surfaces of the solid domain, and the inlet and outlet surfaces of the fan respectively, and then divide the fluid domain and the solid domain into grids in meshing. The number of model boundary grids is 34598, and the average grid quality is 0.856.

[0096] S5-2, set the initial temperature conditions and boundary conditions, set the external environment temperature and the initial temperature of the dryer to 30℃, the power of the heat exchanger to 400W, the inlet and outlet of the dryer to pressure outlet and pressure inlet, limit the backflow, set the inlet and outlet of the internal fan to velocity inlet, the wind speed is 10m / s, and define a function whose value is the average temperature of the fan inlet, and set the temperature of the fan outlet to this function.

[0097] S5-3, run the simulation calculation, and get Fig.14 The temperature variation diagram of the dryer shown and Fig.15 The fluid trajectory diagram of the dryer is shown in the figure. The results show that after the heat exchanger has been working continuously for 225 seconds, the temperature inside the dryer reaches 100°C relatively evenly, and the wind speed of the material passing through the dryer is sufficient to meet the requirement of 1.5m / s. The final simulation results show that under the parameters of drying temperature of 100°C, hot air speed of 1.5m / s, and garbage particle size of 5mm, the drying time and power consumption of the dryer for kitchen waste materials can meet the set requirements. Therefore, this set of parameter combinations can be output as an alternative optimization result of the kitchen waste drying process parameters.

[0098] In summary, the present invention uses the response surface methodology to optimize the parameters of the kitchen waste drying process, and determines that the heating temperature of 100°C, the garbage particle size of 5mm, and the hot air speed of 1.5m / s are the optimal parameter combination, which further improves the kitchen waste drying efficiency and saves power consumption during the drying process.

[0099] Based on the present invention, different optimal parameter combinations can be quickly optimized according to different optimization goals, and the dryer product model using the parameter combination can be simulated and analyzed to ensure that the product can meet the parameter requirements. Therefore, the iterative update efficiency of the product is greatly improved. The present invention ensures the working performance of the product when it is put into use, and can also save the experimental cost of the sample and shorten the development cycle.

[0100] The above-described embodiments are only some preferred implementations of the present invention, but are not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for optimizing process parameters of kitchen waste drying, characterized in that: The following steps are involved: S1. Taking drying time and power consumption as two optimization targets, and taking drying temperature, hot air speed and garbage particle size as three parameters to be optimized, a single factor experiment is conducted based on the target dryer to determine the optimal value range of each parameter to be optimized; S2. Based on the three parameters to be optimized and their respective optimization value ranges, a three-factor multi-level experimental scheme combination is generated through Box-Behnken response analysis; according to each experimental scheme in the experimental scheme combination, a kitchen waste drying experiment is performed respectively and the experimental results of drying time and power consumption are obtained, and a regression model between the three parameters to be optimized and each optimization target is obtained by fitting according to the experimental results of all experimental schemes; S3. Based on the two regression models obtained by fitting, the optimal parameter combination of the kitchen waste drying process is determined through multi-objective optimization; S4. Establish a simulation model of the target dryer, and perform simulation according to the optimal parameter combination to check whether the drying time and power consumption meet the preset requirements under the optimal parameter combination, and use the optimal parameter combination that passes the test as the optimization result of the kitchen waste drying process parameters.

2. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: The single factor experiment in step S1 includes selecting drying temperature, hot air speed and garbage particle size as independent variables, and analyzing the drying time and power consumption of each independent variable at different values ​​through experiments, so as to determine the optimal value range of each of the three parameters to be optimized.

3. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: The single factor experiment in step S1 is a simulation experiment or an actual experiment.

4. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: In step S2, a three-factor three-level experimental scheme combination is generated through Box-Behnken response analysis.

5. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: In step S2, the regression model between the three parameters to be optimized and each optimization target adopts a quadratic polynomial regression model.

6. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: In step S3, when determining the optimal parameter combination of the kitchen waste drying process through multi-objective optimization, the optimization goal is set to minimize power consumption when the drying time is within a preset range.

7. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: In the step S3, multiple groups of the optimal parameter combinations need to be generated, and each group of the optimal parameter combinations executes the S4 separately. According to the drying time and power consumption in the simulation results, the best group is selected from all the optimal parameter combinations as the optimization result of the kitchen waste drying process parameters.

8. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: In step S3, the two regression models obtained by fitting need to pass accuracy test analysis before being used for multi-objective optimization. If they fail the test, the regression models need to be rebuilt.

9. The method for optimizing process parameters of kitchen waste drying according to claim 8, characterized in that: The accuracy test analysis includes variance analysis, fitting accuracy analysis, residual analysis, and comparative analysis of predicted values ​​and actual values.

10. The method for optimizing process parameters of kitchen waste drying according to claim 1, characterized in that: The Box-Behnken response analysis, the accuracy test analysis, and the multi-objective optimization are all performed using Design-Expert software.

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