CN114462675A

CN114462675A - Cost reduction and efficiency improvement method for kitchen waste treatment and oil extraction process

Info

Publication number: CN114462675A
Application number: CN202111670956.9A
Authority: CN
Inventors: 王晓东; 张慧; 周喜; 王首文
Original assignee: Global Tone Communication Technology Co ltd
Current assignee: Global Tone Communication Technology Co ltd
Priority date: 2021-07-02
Filing date: 2021-12-31
Publication date: 2022-05-10

Abstract

The invention discloses a cost reduction and efficiency improvement method for an oil extraction process for kitchen waste treatment, which comprises the following steps: acquiring influence factor data related to an oil extraction process; performing exploratory analysis on the data acquired in the step one; step three, data cleaning is carried out on the data collected in the step one; step four, feature processing; step five, selecting an algorithm; step six, constructing a model; step seven, evaluating the model; and step eight, selecting an optimal algorithm. The invention has the advantages of effectively improving the oil yield and reducing the production cost.

Description

Cost reduction and efficiency improvement method for kitchen waste treatment and oil extraction process

Technical Field

The invention relates to the technical field of oil extraction by treating kitchen waste, in particular to a cost reduction and efficiency improvement method for an oil extraction process by treating the kitchen waste.

Background

Because the water content and the organic matter content of the kitchen waste are high, the traditional waste incineration and landfill mode becomes a great amount of waste of organic matters and grease. Meanwhile, the kitchen waste has low heat value and high moisture content and cannot meet the incineration requirement of household garbage, and leachate with high grease and salt content in the landfill process is not easy to treat, so that secondary pollution is easily caused. Therefore, the method is particularly important for harmless disposal and resource utilization of kitchen waste. The biological process of converting organic matters into carbon dioxide and methane under the anaerobic condition by the anaerobic digestion principle and synthesizing cell substances per se simultaneously is an effective method for realizing the reclamation and harmlessness of organic solid wastes.

Under the mode of anaerobic treatment of kitchen waste, the particle size of the treated material is controlled below a certain particle size through the pretreatment links of crushing, sorting and the like, so that impurities are efficiently removed, and grease is extracted to meet the requirements of the subsequent anaerobic treatment process. The extracted oil can generate considerable economic benefit, and meanwhile, the efficient oil extraction is more beneficial to the work of the subsequent anaerobic process. Therefore, the significance of increasing the oil yield of the oil extraction unit in the pretreatment link is great.

At present, no effective means exists for improving the grease yield of the oil extraction unit. Basically, the relevant technological production parameters are controlled by manual experience.

Disclosure of Invention

The invention aims to solve the problems and designs a cost reduction and efficiency improvement method for an oil extraction process for kitchen waste treatment.

A cost reduction and efficiency improvement method for an oil extraction process in kitchen waste treatment comprises the following steps:

step one, acquiring influence factor data related to an oil extraction process, taking the acquired factor data as a model independent variable for standby, and taking an oil extraction amount as a model dependent variable for standby, wherein the data comprises the following steps: material temperature, material oil content, motor speed, material particle size, feed flow, centripetal pump opening, animal and vegetable oil proportion, holidays and seasons;

secondly, exploratory analysis is carried out on the data collected in the first step, a data set is established on the basis of the collected data, and the mutual relation among the influencing factors and the relation between the influencing factors and the oil extraction amount are known by knowing the distribution condition of the data set;

step three, data cleaning is carried out on the data collected in the step one, and the data cleaning condition comprises the following steps:

data set missing value: for the case of few missing values, missing data filling can be performed through an interpolation method, for the case of many missing values, the difficulty of supplementing the missing data is high, if the missing data is forcibly filled, the filled data may have adverse effect on the model, and in this case, the data with many missing data needs to be removed;

data set outliers: finding out abnormal values and deleting the abnormal values in a box line graph mode;

data difference: checking whether the difference of independent variable data is large enough or not through the variance, if the difference is too small, the data is basically consistent, and the oil extraction amount is not obviously influenced;

and (3) correlation checking: checking linear correlation or visualization modes of a scatter diagram and a line drawing through a Pearson correlation system to check trend relations between independent variables and dependent variables;

step four, characteristic processing, namely screening factors with strong correlation with the oil extraction amount, removing variation factors with low correlation with the oil extraction amount, selecting and reserving the factors with strong correlation among the influence factors, and adopting a Pearson correlation coefficient or a tree model for correlation checking;

step five, selecting an algorithm, wherein the algorithm for constructing the model can select linear regression, a tree model, support vector regression and a neural network, and preferably selects the tree model, so that the interpretability of the tree model and the screening effect on the model characteristics have more advantages in the aspects of explaining the importance of the characteristics in the model and industrial optimization;

step six, constructing a model, wherein the method for constructing the model comprises the following steps:

the data set was first divided into 2 parts, 80% of the data was used for training, the remaining 20% was used to test the training results,

secondly, modeling is carried out through a K-fold cross validation and grid search mode, and an optimal hyper-parameter is searched; the K-fold cross validation divides data into K groups, each group is used as test data, the rest K-1 group of data are used as training data and validate the test data, the process is repeated for K times, and finally, the evaluation result is subjected to arithmetic mean;

step seven, evaluating the model, namely combining a decision coefficient R2 and an average absolute error MAE as evaluation indexes, wherein the decision coefficient R2 is used for evaluating the goodness of fit of the regression model coefficient after the model is regressed, the greater the goodness of fit is, the higher the interpretation degree of an independent variable on a dependent variable is, the percentage ratio of variation caused by the independent variable to total variation is high, the maximum value of R2 is 1, the average absolute error MAE is the average value of the absolute values of errors of an observed value and a real value, and finally, the model with a smaller MAE and a larger value of R2 is selected as a final model;

and step eight, selecting an optimal algorithm, training and modeling the optimal algorithm, taking the screened influence factors as model input parameters, predicting the oil extraction amount, wherein each group of parameters corresponds to the sum of input costs, such as power consumption corresponding to the rotating speed of the motor, steam usage corresponding to the oil extraction temperature and the like, the final net benefit is the weight of the produced oil and the unit price of the oil, and a group of working parameters with the highest net benefit is found, namely the net benefit is the benefit-input cost.

And seventhly, when model evaluation is carried out, model overfitting traps need to be avoided, if the model performs better on a training set than in a test set, the model is likely to be overfitted, the generalization capability of the model is poor, and finally the model cannot be put into practical production and use.

The method for solving model overfitting comprises the following steps:

1) the regularization approach, which will depend on the type of model used;

2) deleting useless features, and removing features which have no obvious influence on the oil extraction amount by combining means such as difference inspection, correlation inspection, model independent variable importance degree output and the like;

3) the learning is stopped in time, when the model is iterated to a certain number of times, new iteration can continuously improve the model, but then the generalization capability of the model is weakened as the training data begins to be over-fitted;

4) the prediction tasks are jointly completed by combining predictions of a plurality of different models by using an integration method, and the most common integration method is as follows: bagging and Boosting, Bagging uses complex base models to try to "smooth" their predictions, a common Bagging algorithm has random forests, while Boosting uses simple base models to try to "improve" their overall complexity, and common Boosting algorithms have Adaboost, Xgboost.

The regularization approach may prune the tree in the tree model.

The regularization approach uses dropout on the neural network.

The regularization approach adds penalty parameters to the objective function in the regression.

Advantageous effects

The cost reduction and efficiency improvement method for the kitchen waste treatment oil extraction process manufactured by the technical scheme of the invention has the following advantages:

the method predicts the grease yield of the oil extraction unit of the kitchen waste treatment oil extraction process under different conditions in a model prediction mode, thereby ensuring that the grease yield is improved by controlling and adjusting the production parameters of related processes, and reducing the energy cost in the production process.

Drawings

FIG. 1 is a process flow diagram of a cost reduction and efficiency enhancement method for a kitchen waste treatment oil extraction process according to the present invention;

FIG. 2 is a schematic diagram of the values of anomalies in oil holdup according to the present invention;

FIG. 3 is a graphical representation of the value of anomalies in recovery according to the invention;

FIG. 4 is a histogram of oil holdup according to the present invention;

FIG. 5 is a graph of temperature and amount of oil extracted according to the present invention;

fig. 6 is a schematic view of embodiment 1 of the present invention.

Detailed Description

The invention is described in detail below with reference to the drawings, as shown in FIGS. 1-6;

factors affecting the amount of oil extracted by an oil extraction unit are generally: material temperature, material oil content, motor speed, material particle size, feed flow, centripetal pump opening, animal and vegetable oil proportion, holidays, seasons and other factors. Under the influence of multidimensional factors, the traditional mode based on artificial experience has strong subjectivity and randomness, the experience between different projects cannot be copied, the operation under the optimal parameters is difficult to achieve in the same project, the economic benefit is lost, and the anaerobic digestion of materials in the subsequent anaerobic links is not facilitated.

According to the invention, through a machine learning mode, multidimensional influence factors are: the material temperature, the material oil content, the motor rotating speed, the material particle size, the feeding flow, the centripetal pump opening degree, the animal and vegetable oil proportion, the holiday, the season and other factors are used as independent variables of the model, and the oil extraction amount is used as a dependent variable of the model. And (3) constructing a prediction model of the influence factors on the oil extraction amount. And then substituting cost parameters related to the influence factors, such as electricity consumption, steam usage, oil price and the like, to obtain a group of working parameters which enable net benefits to be highest, and finally achieving the purposes of cost reduction and efficiency improvement.

Machine learning is a collection of numerous algorithms. From the aspect of learning methods, machine learning algorithms can be classified into supervised learning and unsupervised learning. Common algorithms are: decision trees, random forests, logistic regression, multiple linear regression, support vector machines, Adaboost, Xgboost, hierarchical clustering, density clustering, and the like.

Deep learning is a branch of machine learning. Neural networks are one way to achieve deep learning. Neural networks are mathematical models that simulate human brain thinking. The neural network has strong learning ability, generalization ability and nonlinear mapping ability, and can avoid the traditional complex modeling process. Common algorithms are: BP neural networks, convolutional neural networks, cyclic neural networks, and the like.

The technical scheme of the invention mainly aims to solve the technical problems of cost reduction and efficiency improvement of the oil extraction process in the kitchen waste treatment process, namely, the optimal working parameters of the oil extraction process are found, so that the oil extraction process has the maximum income when working under the optimal parameters:

due to objective conditions such as sensor errors and errors in the data storage/transmission process, the acquired data may have situations such as partial data loss, duplication, incompleteness, noise, abnormality and the like. To ensure the correctness and validity of the training data used to build the model, the data needs to be cleaned. Before data cleansing, exploratory analysis is performed on the data set to understand the condition of the data. The dimensionality of the data set comprises factors influencing the oil extraction amount, such as material temperature, material oil content, motor rotating speed, material particle size, feeding flow, centripetal pump opening degree, animal and vegetable oil proportion, holidays, seasons and the like, and the influencing factors are used as independent variables, and the oil extraction amount is used as a dependent variable. By knowing the distribution condition of data of the data set, the mutual relation among the influencing factors and the relation between the influencing factors and the oil extraction amount are known, so that the data cleaning, the feature processing and the model building can be better carried out in the later period.

Data set missing value: in the case of a small number of missing values, the missing data padding may be performed by interpolation. For the case of a large number of missing values, it is difficult to supplement the missing data, and if the missing data is forcibly filled, the filled data may adversely affect the model, and in this case, the data with a large number of missing data generally needs to be removed.

Data set outliers: and finding abnormal values and deleting the abnormal values in a box plot and other ways. The values of abnormality in oil content and recovery ratio shown in fig. 2 and 3. The oil content shown in fig. 4 approximately conforms to the positive distribution, but there is a bump around the leftmost value of 1.4, which indicates that there may be an anomaly around the value of 1.4, and in conjunction with fig. 2, there is an anomaly below the value of 1.4, and fig. 2 and fig. 4 mutually prove, and provide a strong basis for finding the anomaly.

Data difference: whether the variance of independent variable data is large enough is generally checked through the variance size, and if the variance is too small, the data is basically consistent and has no significant influence on the oil extraction amount.

And (3) correlation checking: and checking linear correlation or visualization modes such as a scatter diagram and a line drawing through a Pearson correlation system, and checking trend relations between independent variables and dependent variables. Is an important basis for the next feature processing. Fig. 5 shows that the oil extraction amount increases with the increase of the oil extraction temperature, but the increase of the oil extraction amount tends to be gentle.

The technical scheme needs to screen factors with strong correlation with the oil extraction amount. Factors with high correlation with the oil extraction amount can better reflect the change trend of the oil extraction amount and can be key indexes influencing the oil extraction amount. In the construction of the model, the potential factors are considered as features, the variation factors with low correlation to the oil extraction amount are removed, and the factors with strong correlation among the influence factors are removed (one of the factors is reserved), so that the modeling dimension can be effectively reduced, and the accuracy of the model is improved. Common correlation checking means include: pearson correlation coefficients or models with independent variable importance outputs, such as tree models.

According to the technical scheme, in the process of model construction, linear regression, tree models, support vector regression and neural networks are used for respectively establishing models, and after evaluation, the optimal model is selected. In contrast, the interpretability and screening effect on model features of tree models make them more advantageous in explaining the importance of features in models and in industrial optimization.

The data set was first divided into 2 parts, 80% of the data was used for training and the remaining 20% were used to test the training results.

Modeling is carried out through a K-fold cross validation and grid search mode, and the optimal hyper-parameter is found. And (3) splitting the data into K groups by K-fold cross validation, using each group as test data, using the rest K-1 group data as training data and validating the test data, repeating the process for K times, and finally taking the arithmetic mean of the evaluation results. K-fold cross-validation is particularly useful for small datasets because it can maximize test and training data. Grid search is a method of performing hyper-parametric optimization, a means to find the best combination of hyper-parameters for a given model.

Evaluating the model after the model is constructed; the determination coefficient R2 and the mean absolute error MAE were used as evaluation indexes. The determination coefficient R2 is obtained by estimating the goodness of fit of the regression model coefficients after regression of the model. The greater the goodness of fit, the greater the interpretation of the independent variable on the dependent variable, the higher the percentage of the total variation that is accounted for by the independent variable, and the maximum value of R2 is 1. The mean absolute error MAE is the average of the absolute values of the errors of the observed value and the true value. Finally, the model with smaller MAE and larger R2 value is selected as the final model.

To avoid model overfitting traps. If the model performs much better on the training set than in the test set, the model is likely to be over-fit, and the generalization capability of the model is poor, which finally results in that the model cannot be put into practical production and use. Common methods for solving the overfitting are: 1) the regularization approach, this approach will depend on the type of model used. For example, a tree can be pruned in the tree model, dropout can be used on the neural network, and penalty parameters can be added to the objective function in regression. 2) And deleting useless features, and removing features which have no obvious influence on the oil extraction amount by combining means such as difference inspection, correlation inspection, model independent variable importance degree output and the like. 3) And the learning is terminated in time. When the model is iterated a certain number of times, new iterations will continuously improve the model. However, the generalization ability of the model then diminishes as the training data begins to over-fit. 4) And (3) combining the predictions of a plurality of different models together by using an integration method to jointly complete a prediction task. The most common integration methods are: bagging and Boosting. Bagging uses complex underlying models to try to "smooth" their predictions, and a common Bagging algorithm has a random forest. While Boosting uses a simple base model and attempts to "boost" their overall complexity. Common Boosting algorithms are Adaboost, Xgboost.

And after screening and selecting an optimal algorithm, training and modeling the optimal algorithm, and predicting the oil extraction amount by taking the screened influence factors as model input parameters. Each group of parameters corresponds to a sum of investment costs, such as power consumption corresponding to the rotating speed of the motor, steam usage corresponding to the oil extraction temperature and the like. The net final benefit is the weight of grease produced per unit of grease. And finding a set of working parameters with the highest net income, namely net income-investment cost.

Example 1

Implementation objects are as follows: a pretreatment process of a kitchen waste treatment project in a certain place;

the implementation purpose is as follows: optimizing working parameters in an oil extraction link of the pretreatment process so as to maximize the benefit of the oil extraction link;

the implementation process comprises the following steps: the kitchen waste treatment capacity is limited to be 70 tons/day, the sources of the kitchen waste are consistent, the proportion of animal and vegetable oil of the kitchen waste and the particle size of materials are not obviously different, and then the oil content, the rotating speed of a main motor, the feeding flow, the opening degree of a centripetal pump and the season are set to be constant, for example: the oil content is 1.84%, the rotating speed of a main motor is 2800 r/min, and the feeding flow is 8m³The data are analyzed for exploratory analysis, which is a method of analyzing a data set to summarize its main characteristics, and a visualization method is generally used. Statistical models can be used or used, but mainly EDA is to understand what the data can tell us outside the formalized modeling or hypothesis testing task. Exploratory data analysis is the research data that John Tukey promoted to encourage statisticians and to make assumptions as much as possible and to generate new data collections and experiments as much as possible. EDA differs from Initial Data Analysis (IDA) in that it focuses more on checking the hypotheses required for model fitting and hypothesis testing, and on processing missing values and performing variable transformations as needed. EDA includes IDA, then set up the data set on the basis of the data gathered, through knowing the distribution situation of the data set data, know the interrelation among the influence factor and relation between influence factor and oil extraction amount, then screen and carry the strong relevant factor of oil extraction amount, such as the temperature of oil extraction, remove the change factor not high to the relevance of oil extraction amount, choose to keep to the strong relevant factor among the influence factor; selecting a tree model as a build modelThe model is built, the model built in the step six is evaluated by utilizing a decision coefficient R2 and an average absolute error MAE, and the model with a smaller MAE and a larger R2 value is selected as a final model; the screened influence factors (such as the oil extraction temperature) are input into a final model as parameters to predict the oil extraction amount, and a group of working parameters with the highest net benefit is found, as shown in fig. 6, the traditional kitchen waste treatment temperature is generally 60 ℃, while the method can easily predict the maximum net benefit of the project when the oil extraction temperature is 74 ℃, and compared with the traditional oil extraction temperature of 60 ℃, the net benefit can be increased by 11% when the project is operated at 74 ℃.

Example 2

Under the condition of ensuring that the oil extraction temperature and other factors are not changed, the rotating speed of the main motor is changed from 2800 revolutions per minute to 3200 revolutions per minute, and every time 50 revolutions per minute is increased to serve as an interval, the method of the embodiment 1 can be used for easily predicting that the net oil extraction yield is the maximum when the rotating speed of the main motor reaches 3000 revolutions per minute; the net gain of 13% for operation at main motor speeds up to 3000 rpm can be increased compared to 2800 rpm.

Example 3

Under the condition of ensuring that the oil extraction temperature, the rotating speed of a main motor and other factors are not changed, the feeding flow is changed from 7m³Hour to 11m³Per hour, 0.5m increase³Hour as an interval. Using the method of example 1, it can be easily predicted that when the feed rate reaches 9m³The net benefit of oil extraction is the maximum in hour; with a feed flow of 8m³In a feed rate of 9 m/hr³Operation per hour can increase the net gain by 16%.

It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation. The use of the phrase "comprising" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the same.

The technical solutions described above only represent the preferred technical solutions of the present invention, and some possible modifications to some parts of the technical solutions by those skilled in the art all represent the principles of the present invention, and fall within the protection scope of the present invention.

Claims

1. A cost reduction and efficiency improvement method for an oil extraction process in kitchen waste treatment is characterized by comprising the following steps:

acquiring influence factor data related to an oil extraction process;

performing exploratory analysis on the data acquired in the step one;

step three, data cleaning is carried out on the data collected in the step one;

screening factors with strong correlation with the oil extraction amount;

step five, selecting an algorithm for constructing a model;

step six, constructing a model by using the algorithm selected in the step five;

step seven, evaluating the model constructed in the step six by using a decision coefficient R2 and an average absolute error MAE to determine a final model;

and step eight, inputting the influence factors screened in the step four as parameters into the final model in the step seven to predict the oil extraction amount, and finding out a group of working parameters with the highest net benefit.

2. The method of claim 1, wherein step seven is performed to avoid model overfitting traps.

3. The method of claim 2, wherein the solution to model overfitting comprises:

1) the regularization approach, which will depend on the type of model used;

2) deleting useless features, and removing features which have no obvious influence on the oil extraction amount by combining means of difference inspection, correlation inspection and model independent variable importance degree output;

3) stopping learning in time, and stopping learning after the model is iterated to a set number of times;

4) and (3) combining the predictions of a plurality of different models together by using an integration method to jointly complete the prediction task.

4. The method of claim 3, wherein the regularization is used in a tree model to prune the tree.

5. The cost reduction and efficiency improvement method for the kitchen waste treatment oil extraction process as claimed in claim 3, wherein the regularization mode uses dropout on a neural network.

6. The method of claim 3, wherein the regularization adds penalty parameters to the objective function in a regression.

7. The method of claim 1, wherein the data in the first step comprises: material temperature, material oil content, motor speed, material particle size, feed flow, centripetal pump opening, animal and vegetable oil proportion, holidays and seasons.

8. The method of claim 1, wherein the step three of cleaning the data comprises: missing data set values, outlier data set values, data dissimilarity, and correlation checks.

9. The method of claim 1, wherein the correlation between factors is checked by using Pearson correlation coefficient or tree model in the fourth step.

10. The cost reduction and efficiency improvement method for the kitchen waste treatment oil extraction process as claimed in claim 1, wherein the method for constructing the model in the sixth step comprises the following steps:

secondly, modeling is carried out through a K-fold cross validation and grid search mode, and an optimal hyper-parameter is searched; the K-fold cross validation divides data into K groups, each group is used as test data, the rest K-1 group of data are used as training data and validate the test data, the process is repeated for K times, and finally, the evaluation result is subjected to arithmetic mean.