Patent Description:
From the viewpoint of effective use of resources and reduction of CO<NUM> emission, improvement in a recycling rate of plastic is required.

Patent Literature <NUM> discloses that, in order to widen an object of a recovery material used in production of a recovery thermoplastic resin, a blending composition of an additional material to be added to the recovery material is calculated based on an index indicating a state of the recovery material and a target value of resin design. For the calculation, a relational expression prepared in advance Further, the publication "<NPL> a plastic recycling supporting apparatus which determines treatment and mixing parameters for recycled materials based on user input of a matrix type and recycling route using an AI-model.

In general, a physical property of the plastic can be controlled by adding an additive. However, in the case of recycled plastic, since it is unknown what physical property a waste plastic has as a base material, it is difficult to know optimal blending of the additive for obtaining a recycled plastic having a desired physical property. Furthermore, since the waste plastic often deteriorates due to oxidation, thermal history, or the like, it is necessary to grasp a deterioration degree of the waste plastic and optimize the blending of the additive according to the deterioration, but the method is unclear.

An object of the invention is to make it possible to determine, based on data, whether a waste plastic can be recycled into a recycled plastic having a desired physical property even if use history of the waste plastic is unknown. Even if the use history of the waste plastic is unknown, blending the additive for recycling into raw plastic having the desired physical property can be estimated with high accuracy.

A plastic recycling supporting apparatus according to an embodiment of the invention is a plastic recycling supporting apparatus for supporting plastic recycling in which a plastic is blended with an additive and is recycled into a recycled plastic having a desired physical property, the plastic recycling supporting apparatus including: a physical property and deterioration estimator configured to estimate, using a physical property and deterioration estimation model, a physical property and a deterioration degree of the plastic based on a texture structural feature extracted from surface analysis data of the plastic; and a blending estimator configured to estimate a physical property of the recycled plastic based on the physical property and the deterioration degree of the plastic and a blending condition of the additive using a physical property recovery model. The physical property and deterioration estimator estimates a physical property and a deterioration degree of a sample based on a texture structural feature extracted from surface analysis data of the sample, and the blending estimator inversely estimates the blending condition of the additive to be blended in the sample based on the physical property and the deterioration degree of the sample, that are estimated by the physical property and deterioration estimator, and the desired physical property of the recycled plastic.

A plastic recycling process with high reliability is realized. Widening a range of available waste plastics leads to improvement in the recycling rate. Other problems and novel characteristics will be apparent from the description of the present specification and the accompanying drawings.

Hereinafter, an embodiment of the invention will be described with reference to the drawings.

A scheme of plastic recycling supporting according to the present embodiment is shown in <FIG>. In the plastic recycling supporting according to the present embodiment, a plurality of features (hereinafter, referred to as texture structural features) of a texture structure of a base material are extracted using surface analysis data of waste plastic serving as the base material, and a physical property and a deterioration degree of the base material are estimated based on the extracted texture structural features (first estimation). In the first estimation, a physical property and deterioration estimation model is used. The physical property and deterioration estimation model is a model in which the texture structural features of the base material are used as explanatory variables and the physical property and the deterioration degree of the base material are used as target variables. Hereinafter, an example of using, as the physical property and deterioration estimation model, a model trained using a machine learning method will be described. Subsequently, based on the estimated physical property and the deterioration degree of the base material, a blending condition of an additive to be blended into the waste plastic (the base material) is estimated (second estimation) for obtaining a recycled plastic (compound) having a desired physical property. In the second estimation, a physical property recovery model is used. The physical property recovery model is a model in which the physical property and the deterioration degree of the base material and the blending condition of the additive are used as explanatory variables, and the physical property of the compound is used as a target variable. Hereinafter, an example of using, as the physical property recovery model, a model trained using a machine learning method will be described.

<FIG> shows examples of a surface analysis method for extracting the texture structural feature, the physical property, the deterioration degree, and the additive, which are used in the first estimation. The surface analysis method, the physical property, the deterioration degree, and the additive are merely examples, and the invention is not limited thereto. A generalized evaluation and measurement method for deterioration of a plastic is unknown. As an example, an accelerated deterioration test is performed on the plastic, and the deterioration degree is defined based on a condition of the accelerated deterioration test. For example, the deterioration degree can be quantitatively defined such that the deterioration degree of the plastic on which the accelerated deterioration test is performed for a longer time is larger.

Here, an example of extracting the texture structural feature of the base material from the surface analysis data will be described. <FIG> is an XRD spectrum obtained by performing X-ray diffraction on the base material. The horizontal axis represents a diffraction angle, and the vertical axis represents a diffracted X-ray intensity. With respect to such the XRD spectrum, for example, a pseudo-fort function shown in Math. <NUM> is fitted as a fitting function.

By fitting the pseudo-fort function, four texture structural features (Δ2θ<NUM>: peak position, A: peak height, Hk: peak width, η: Lorentz component) are obtained for each peak included in the XRD spectrum. A spectrum example shown in <FIG> includes <NUM> peaks, and the four texture structural features are extracted for each peak.

<FIG> shows a plastic recycling supporting system. The plastic recycling supporting system includes a plastic recycling supporting apparatus <NUM> and is communicably connected to a terminal <NUM> via a network <NUM>. The terminal <NUM> includes a display device <NUM> such as a display and an input device <NUM> such as a keyboard. A user accesses the plastic recycling supporting apparatus <NUM> through the terminal <NUM>, determines, by using the scheme shown in <FIG>, whether a plastic is a waste plastic through which the compound having the desired physical property can be obtained, and determines a blending condition of an additive to be added to the waste plastic (the base material) if the compound having the desired physical property can be obtained. The surface analysis data and the physical property data of the waste plastic are transmitted from the terminal <NUM> to the plastic recycling supporting apparatus <NUM>. In <FIG>, a differential scanning calorimeter (DSC) <NUM>, a Fourier transform infrared spectrophotometer (FTIR) <NUM>, and an X-ray diffractometer (XRD) <NUM> are shown as surface analysis devices, and an impact resistance measuring device <NUM> and a melt mass flow rate (MFR) measuring device <NUM> are shown as physical property measuring devices. The devices are merely examples, and the present system is not limited to the devices.

The plastic recycling supporting apparatus <NUM> is implemented by an information processing apparatus, as shown in <FIG>, including a processor (CPU) <NUM>, a memory <NUM>, a storage device <NUM>, an input device <NUM>, an output device <NUM>, a communication device <NUM>, and a bus <NUM> as main components. The processor <NUM> functions as a functional unit (functional block) that provides a predetermined function, by executing processing according to a program loaded into the memory <NUM>. The storage device <NUM> stores data and the program used by the functional unit. In the storage device <NUM>, a nonvolatile storage medium such as a hard disk drive (HDD) or a solid-state drive (SSD) is used. The input device <NUM> is a keyboard, a pointing device, or the like, and the output device <NUM> is a display or the like. The communication device <NUM> enables communication with the terminal <NUM> and other information processing apparatuses via the network <NUM>. The processor <NUM>, the memory <NUM>, the storage device <NUM>, the input device <NUM>, the output device <NUM>, and the communication device <NUM> are communicably connected to each other by the bus <NUM>.

The plastic recycling supporting apparatus <NUM> is not necessarily implemented by one information processing apparatus, and may include a plurality of information processing apparatuses. In addition, a part or all of functions of the plastic recycling supporting apparatus <NUM> may be implemented as an application on a cloud.

Hereinafter, processing of the plastic recycling supporting apparatus <NUM> will be described with reference to flowcharts and a functional block diagram of the plastic recycling supporting apparatus <NUM> shown in <FIG>.

<FIG> is a flowchart showing the entire plastic recycling supporting processing. The user inputs a type of the waste plastic to be recycled and a target specification of the recycled plastic (S01). <FIG> shows an example of an input screen displayed on the terminal <NUM>. An input screen <NUM> includes a to-be recycled waste plastic information input unit <NUM>, a target specification input unit <NUM>, and prediction condition input units <NUM> to <NUM>. Information to be input from the waste plastic information input unit <NUM> includes the type of the waste plastic that is the base material of the recycled plastic. The plastic includes polypropylene (PP), polyethylene (PE), polystyrene (PS), or blends thereof, and is recycled for each type of base material. In addition, it is desirable to input origin information of the waste plastic. The target specification of the recycled plastic is input from the target specification input unit <NUM>. The target specification includes a physical property parameter name, a target value, and an allowable range. Hereinafter, a physical property parameter defined as the target specification is referred to as a target physical property parameter, and unless otherwise specified, a target value including the allowable range is referred to as a target physical property parameter value.

The number of the physical property parameter serving as the target specification is not limited. Further, conditions for selecting a model to be used in the plastic recycling supporting processing are input in advance from the prediction condition input units, and the model is easily narrowed down by the plastic recycling supporting apparatus <NUM>. Here, an example is shown, in which estimation accuracy <NUM> of the model, a cost <NUM> allowable for surface analysis for obtaining input data serving as an explanatory variable of the physical property and deterioration estimation model, and a time <NUM> are input.

The user obtains a sample of the waste plastic to be recycled (S02), and the plastic recycling supporting apparatus <NUM> makes acceptance determination on the sample (S03). The sample is the waste plastic of the type input in the input step S01, but the user does not have information on a physical property and a deterioration degree of the sample, and does not know whether the sample can be recycled into plastic (compound) having a desired property. For example, when the physical property of the base material greatly deviates from the target specification of the recycled plastic or the deterioration significantly progresses, the target specification may not be achieved. Thus, in an acceptance determination step (S03), it is determined whether there is a possibility that the sample satisfies the target physical property parameter value input in the input step S01, and when it is determined that the target physical property parameter value can be satisfied, the sample is acceptable. Details of the acceptance determination step (S03) will be described later.

The plastic recycling supporting apparatus <NUM> performs blending optimization on the acceptable waste plastic (S04). In the blending optimization step (S04), since the physical property and the deterioration degree of the sample are estimated by an estimator <NUM> of a physical property and deterioration estimator <NUM>, an inverse estimator <NUM> of a blending estimator <NUM> uses the physical property recovery model to estimate a blending condition of an additive satisfying the target physical property parameter value.

<FIG> is a flowchart for determining an acceptance determination criterion in the acceptance determination step (S03). The flow is mainly executed by a model selector <NUM>. First, the model selector <NUM> searches a model database <NUM> based on the type of the waste plastic input in the input step (S01) and the target specification of the recycled plastic (S11). The model database <NUM> stores a model created by the plastic recycling supporting apparatus <NUM> in the past. In a case of a model created based on machine learning, the ability to make an appropriate inference depends on training data used for model learning. Therefore, a searcher <NUM> selects a trained model available for the input contents in the input step (S01) as a candidate model when such a trained model is stored in the model database <NUM> (S12), and constructs the candidate model when the trained model is not stored (S13). When prediction conditions are input by the user (see <FIG>), the searcher <NUM> selects a model satisfying the prediction conditions.

Details of the candidate model construction step (S13) are shown in <FIG>. Based on the type of plastic and the target physical property parameter value, data stored in a plastic database <NUM> is referred to (S21). The plastic DB <NUM> stores, for each type of plastic, the surface analysis data, the physical property data, and deterioration degree data of the plastic. Data on the plastic obtained by performing the accelerated deterioration test on the sample under different conditions is stored, and the deterioration degree data is based on the condition of the accelerated deterioration test. Further, the physical property data of the recycled plastic into which the plastic is recycled by being blended with an additive and the blending condition of the additive at that time are also stored.

The physical property and deterioration estimation model and the physical property recovery model are constructed by using the data stored in the plastic DB <NUM> as training data (S22, S23). In a case of the physical property and deterioration estimation model, a learning unit <NUM> of a first model constructor <NUM> performs a construction by performing supervised learning using, as training data, a combination of the texture structural feature of the base material (the plastic on which the accelerated deterioration test is performed) with the physical property and the deterioration degree of the base material which are stored in the plastic DB <NUM>, for example.

In a case of the physical property recovery model, a learning unit <NUM> of a second model constructor <NUM> performs a construction by performing supervised learning using, as training data, a combination of the physical property and the deterioration degree of the base material and the blending condition of the additive which are stored in the plastic DB <NUM>, with the physical property of the compound (the recycled plastic into which the base material is recycled by being blended with the additive under the blending condition) which is stored in the plastic DB <NUM>.

Here, it is desirable to construct a plurality of physical property and deterioration estimation models and a plurality of physical property recovery models. Generally, estimation accuracy of a model may be improved by using various types of explanatory variables, on the other hand, when it is necessary to perform various types of surface analyses, a cost and a time for acquiring analysis data increase. In addition, a degree of contribution to the improvement of the estimation accuracy differs depending on the explanatory variables. Therefore, it is desirable to construct a plurality of models with different surface analysis methods for obtaining the texture structural features and different physical property parameters to be predicted, and to allow the user to select an optimal model by weighing the accuracy of the models with a cost of acquiring data for using the models.

Thus, the estimation accuracy and the data acquisition cost are calculated for each constructed model (S24), and the constructed model is registered in the model database <NUM> in association with the type of the plastic, the target physical property parameter value, the estimation accuracy, and the data acquisition cost (S25). The estimation accuracy of each model is calculated by an accuracy calculator <NUM> of the first model constructor <NUM> and an accuracy calculator <NUM> of the second model constructor <NUM>. When the prediction conditions are input by the user (see <FIG>), the first model constructor <NUM> and the second model constructor <NUM> construct models that satisfy the prediction conditions.

The description returns to <FIG>. The model selector <NUM> presents the estimation accuracy and the data acquisition cost of the selected or constructed candidate model to the terminal <NUM> (S14). <FIG> shows a display screen example <NUM> that presents candidate models of the physical property and deterioration estimation model.

A model <NUM> corresponds to each candidate model, and the number of parameters <NUM>, a surface analysis method <NUM>, a time <NUM>, a measurement cost <NUM>, physical property estimation accuracy <NUM>, and deterioration estimation accuracy <NUM> are displayed for each candidate model. The number of parameters <NUM> is the number of parameters (in this case, the texture structural feature) serving as input data of the model. The surface analysis method <NUM> is a surface analysis method necessary for obtaining the parameters (the texture structural feature) serving as the input data. A plurality of types of surface analyses may be required according to the texture structural feature to be input into the model. The time <NUM> and the measurement cost <NUM> respectively indicate a time and a cost necessary for acquiring the input data of the model by the method specified in the surface analysis method <NUM>. The physical property estimation accuracy <NUM> and the deterioration estimation accuracy <NUM> respectively indicate the estimation accuracy for the physical property of the base material and the estimation accuracy for the deterioration degree of the base material in output data of the model. As the estimation accuracy, for example, a determination coefficient R<NUM> can be used.

The user selects a model to be used based on the information of the model presented on the terminal <NUM>. Accordingly, the surface analysis method for the base material (the waste plastic) and the texture structural feature used for the analysis are determined (S15).

The inverse estimator <NUM> of the blending estimator <NUM> estimates, using the selected physical property recovery model, allowable ranges of the physical property and the deterioration degree of the base material based on the target physical property parameter value input in the input step (S01) (S16). Subsequently, an inverse estimator <NUM> of the physical property and deterioration estimator <NUM> converts the allowable ranges of the physical property and the deterioration degree of the base material obtained in step S16 into a texture structural feature space using the selected physical property and deterioration estimation model, and stores the texture structural feature space in an allowable range storage device <NUM> (S17). In the flowchart, it is desirable to obtain not only the allowable ranges of the physical property and the deterioration degree of the base material but also unacceptable ranges of the physical property and the deterioration degree of the base material in step S16, and respectively convert the allowable ranges and the unacceptable ranges into the texture structural feature space in step S17. Thus, as will be described later, based on the texture structural feature of the sample, determinations can be made including a determination as to whether a sample can be appropriately determined to be acceptable with respect to the physical property of the base material by the model or the sample cannot be appropriately determined in the model.

The texture structural feature space indicating the allowable ranges of the physical property and the deterioration degree of the base material obtained in step S17 is the acceptance determination criterion used in the acceptance determination step (S03) (see <FIG>). That is, in the acceptance determination step (S03), when the analysis data analyzed by the surface analysis method determined in step S15 is included in the texture structural feature space obtained in step S17, it is highly possible that the sample can be recycled so as to satisfy the target specification input in input step S01, and it is determined that the sample is acceptable.

A detailed example of the sample acceptance determination step (S03) is shown in <FIG>. First, the user performs surface analysis of the sample by the surface analysis method determined in step S15 (S31). The surface analysis data is input from the terminal <NUM> to a data input unit <NUM> of the plastic recycling supporting apparatus <NUM>. A feature extraction unit <NUM> receives the surface analysis data from the data input unit <NUM>, and extracts the texture structural feature by fitting (S32). Thereafter, the estimator <NUM> of the physical property and deterioration estimator <NUM> inputs the texture structural feature that is the input data to the physical property and deterioration estimation model to estimate the physical property and the deterioration degree of the sample (the base material) (S33).

Here, as the physical property and deterioration estimation model, a model in which a first texture structural feature obtained from the analysis data obtained by a first surface analysis method and a second texture structural feature obtained from the analysis data obtained by a second surface analysis method are used as the input data is taken as an example. Actually, a plurality of texture structural features can be extracted from the analysis data obtained by one surface analysis method as exemplified with reference to <FIG>, and for simplification of description, one texture structural feature is obtained by one surface analysis method. <FIG> schematically shows the texture structural feature space of the model.

In this example, the texture structural feature space of the model is defined as a space <NUM> defined by the first texture structural feature and the second texture structural feature. A region <NUM> is a region where the model can estimate that the sample is acceptable, a region <NUM> is a region where the model can estimate that the sample is not acceptable, and the remaining region <NUM> is a region where the model cannot determine that the sample is acceptable or not acceptable. For example, with respect to a region where the model is not trained using the training data, the reliability of model inference decreases. Such a region with low inference reliability is the region <NUM>. Ranges of these regions are stored in the allowable range storage device <NUM> of the model selector <NUM>. A comparator <NUM> of a determiner <NUM> determines in which region of the texture structural feature space of the model the texture structural feature obtained from the analysis data is (S34, S35).

The comparator <NUM> determines that the sample is acceptable when the texture structural feature obtained from the analysis data is in the region <NUM> (S36), determines that the sample is not acceptable when the texture structural feature obtained from the analysis data is in the region <NUM> (S37), and determines that the estimation cannot be performed by the model when the texture structural feature obtained from the analysis data is in the region <NUM>.

When the comparator <NUM> determines that the sample cannot be estimated, the user measures a physical property value of the sample and stores the result in the plastic database <NUM> (S38). When the measured physical property value is within the allowable range of the physical property of the base material obtained in step S16, the comparator <NUM> determines that the sample is acceptable (S36), and when the measured physical property value and the like are outside the allowable range of the physical property of the base material obtained in step S16, the comparator <NUM> determines that the sample is not acceptable (S40). By storing the physical property value measured for the sample together with the surface analysis data in the plastic database <NUM>, the physical property value can be utilized in training the subsequent model.

Another detailed example of the sample acceptance determination step (S03) is shown in <FIG>. In the example shown in <FIG>, the physical property and deterioration estimation model is taken as an example, in which the first texture structural feature obtained from the analysis data obtained by the first surface analysis method and the second texture structural feature obtained from the analysis data obtained by the second surface analysis method are used as the input data. On the other hand, in a flowchart shown in <FIG>, the acceptance determination is made first, using a first physical property and deterioration estimation model in which the first texture structural feature obtained from the analysis data obtained by the first surface analysis method is used as the input data, and when the determination cannot be made by the first physical property and deterioration estimation model, the acceptance determination is made using a second physical property and deterioration estimation model in which the first texture structural feature and the second texture structural feature obtained from the analysis data obtained by the second surface analysis method are used as the input data. Accordingly, when the acceptance determination of the sample can be made by the first physical property and deterioration estimation model, the acceptance determination can be made at a lower cost.

First, the user performs the surface analysis of the sample by the first surface analysis method (S51). The surface analysis data is input from the terminal <NUM> to the data input unit <NUM> of the plastic recycling supporting apparatus <NUM>. The feature extraction unit <NUM> receives the surface analysis data from the data input unit <NUM>, and extracts the first texture structural feature by fitting (S52). Thereafter, the estimator <NUM> of the physical property and deterioration estimator <NUM> inputs the first texture structural feature that is the input data to the first physical property and deterioration estimation model to estimate the physical property and the deterioration degree of the sample (the base material) (S53).

<FIG> schematically shows the texture structural feature space of the first physical property and deterioration estimation model and the second physical property and deterioration estimation model. The texture structural feature space of the second physical property and deterioration estimation model is the same as the texture structural feature space shown in <FIG>. Meanwhile, the texture structural feature space of the first physical property and deterioration estimation model becomes a one-dimensional space and is divided into regions <NUM> to <NUM>. The region <NUM> is a region where the first physical property and deterioration estimation model can determine that the sample is acceptable, the region <NUM> is a region where the first physical property and deterioration estimation model can determine that the sample is not acceptable, and the regions <NUM>, <NUM> are regions where the acceptance determination cannot be made by the first physical property and deterioration estimation model. Since the region <NUM> is a region where the acceptance determination changes depending on the second texture structural feature, the reliability of the inference result of the first physical property and deterioration estimation model is low. Since the region <NUM> is a region where the model is not trained using the training data, the reliability of the inference result is also low in the region, and the determination cannot be made.

The comparator <NUM> of the determiner <NUM> determines in which region of the one-dimensional texture structural feature space of the first physical property and deterioration estimation model the texture structural feature obtained from the analysis data is (S54, S55). The comparator <NUM> determines that the sample is acceptable when the first texture structural feature obtained from the analysis data is in the region <NUM> (S56), determines that the sample is not acceptable when the first texture structural feature is in the region <NUM> (S57), and determines that the determination cannot be made by the first physical property and deterioration estimation model when the first texture structural feature is in the regions <NUM>, <NUM>.

When the comparator <NUM> determines that the determination cannot be made, the comparator <NUM> switches the model to be used from the first physical property and deterioration estimation model to the second physical property and deterioration estimation model (S58). The user performs the surface analysis of the sample by the second surface analysis method (S59). The surface analysis data is input from the terminal <NUM> to the data input unit <NUM> of the plastic recycling supporting apparatus <NUM>. The feature extraction unit <NUM> receives the surface analysis data from the data input unit <NUM>, and extracts the second texture structural feature by fitting (S60). Thereafter, the estimator <NUM> of the physical property and deterioration estimator <NUM> inputs the first texture structural feature and the second texture structural feature that are the input data to the second physical property and deterioration estimation model to estimate the physical property and the deterioration degree of the sample (the base material) (S61). Since processing after step S61 is the same as the processing after step S33 in the flowchart shown in <FIG>, repeated description will be omitted.

Claim 1:
A plastic recycling supporting apparatus for supporting plastic recycling in which a plastic is blended with an additive and is recycled into a recycled plastic having a desired physical property, the plastic recycling supporting apparatus (<NUM>) comprising:
a physical property and deterioration estimator (<NUM>) configured to estimate, using a physical property and deterioration estimation model, a physical property and a deterioration degree of the plastic based on a texture structural feature extracted from surface analysis data of the plastic; and
a blending estimator (<NUM>) configured to estimate a physical property of the recycled plastic based on the physical property and the deterioration degree of the plastic and a blending condition of the additive using a physical property recovery model, wherein
the physical property and deterioration estimator (<NUM>) estimates a physical property and a deterioration degree of a sample based on a texture structural feature extracted from surface analysis data of the sample, and
the blending estimator (<NUM>) inversely estimates the blending condition of the additive to be blended in the sample based on the physical property and the deterioration degree of the sample, that are estimated by the physical property and deterioration estimator (<NUM>), and the desired physical property of the recycled plastic.