Methods and systems for treating a heterogeneous mixture of materials
A system and method for classifying a material in a heterogeneous mixture of materials, and predicting a processing method for treating the material are disclosed. At least some of the heterogeneous mixture of material pieces comprise different chemical information such as different chemical composition and/or physical characteristics. Embodiments of the system and method of the present invention use machine learning to classify and predict a processing protocol for material pieces based on the chemical compositions or reactivities of each of the material pieces.
1 . A system for predicting a processing protocol for treating a heterogeneous mixture of materials, comprising:
one or more sensor systems configured to analyze a material;
one or more processors configured to process the material;
one or more data analysis systems configured to receive and analyze an output of the material upon processing of the material;
a computer system operatively connected to the one or more sensor systems, the one or more data analysis systems, and the one or more processors, the computer system configured to perform the steps of:
receiving, from the one or more sensor systems, a materials information about a material from a control set of materials;
receiving, from the one or more data analysis systems, a training results from analysis of the output, wherein the output is produced by treating the material in the one or more processors using a processing protocol from a plurality of processing protocols that identify conditions for processing materials;
creating a machine-learning model by training a machine-learning algorithm using the materials information, the processing protocol, and the training results from each of a plurality of experiments;
receiving, from the one or more sensor systems, an input for a desired material for treating, wherein the input comprises one or more physical characteristics, a composition, or a spectral data of the desired material;
creating, by the machine-learning model, a new processing protocol and grouping strategy for treating the desired material based on the input;
communicating the new processing protocol to the one or more processors to control conditions for treating the desired material, wherein the one or more processors are configured to treat the desired material with the new processing protocol;
receiving, from the one or more data analysis systems, further training results from analysis of the output, wherein the output is produced by treating the desired material in the one or more processors using the new processing protocol; and
updating the machine-learning model by further training the machine-learning algorithm by inputting the input, the new processing protocol, and the further training results.
2 . The system according to claim 1 wherein the one or more sensor systems comprises an infrared sensor.
3 . The system according to claim 1 , wherein the one or more processors comprise one or more reactors, and wherein the one or more reactors is configured to depolymerize the material.
4 . The system according to claim 3 , wherein the one or more reactors is configured to alter the chemical characteristics of the material.
5 . The system according to claim 1 , wherein the one or more data analysis systems configured to receive and analyze the output of the material upon processing of the material comprises one or more of a monomer extraction unit configured to determine monomer extraction efficiency and an analysis unit configured to chemically or spectroscopically determine the purity and characteristics of the monomers in the output.
6 . The system according to claim 1 , wherein the materials information comprises a spectrum of the material in response to an electromagnetic interaction of the material in a specified frequency range.
7 . The system according to claim 1 , wherein the materials information comprises a composition of the material.
8 . The system according to claim 1 , wherein the materials information comprises one or more physical characteristics of the material.
9 . The system according to claim 1 , further comprising one or more apparatuses arranged upstream of the one or more processors, the one or more apparatuses being configured to pre-treat the material before inputting the material into the one or more processors.
10 . The system according to claim 1 , wherein the features that define the processing protocol comprises conditions for depolymerizing the material in the one or more processors to obtain a depolymerized material.
11 . A system for predicting a processing protocol for treating a heterogeneous mixture of materials, comprising:
one or more sensor systems configured to analyze a material;
one or more processors configured to process the material;
one or more data analysis systems configured to receive and analyze an output of the material upon processing of the material;
a computer system operatively connected to the one or more sensor systems, the one or more data analysis systems, and the one or more reactors, the computer system configured to perform the steps of:
receiving, from the one or more sensor systems, an input for a desired material for treating;
creating, by a machine-learning model, a new processing protocol and grouping strategy for treating the desired material based on the input, wherein the machine-learning model is created by training a machine-learning algorithm using a materials information, a processing protocol, and training results obtained from analyzing an output of a treatment from a plurality of experiments performed on a control set of materials;
communicating the new processing protocol to the one or more processors to control conditions for treating the desired material, wherein the one or more processors treat the desired material with the new processing protocol;
receiving, from the one or more data analysis systems, further training results from analysis of the output, wherein the output is produced by treating the material in the one or more processors using a processing protocol from a plurality of processing protocols that identify conditions for processing materials; and
updating the machine-learning model by further training the machine-learning algorithm by inputting the input, the new processing protocol, and the further training results.
12 . The system according to claim 11 wherein the one or more sensor systems comprises an infrared sensor.
13 . The system according to claim 11 , wherein the one or more processors comprise one or more reactors and wherein the one or more reactors is configured to depolymerize the material.
14 . The system according to claim 13 , wherein the one or more reactors are configured to alter the chemical characteristics of the material.
15 . The system according to claim 11 , wherein the one or more data analysis systems comprises one or more of a monomer extraction unit configured to determine monomer extraction efficiency and an analysis unit configured to chemically or spectroscopically determine the purity and characteristics of the monomers in the output.
16 . The system according to claim 11 , wherein the materials information comprises a spectrum of the material in response to an electromagnetic interaction of the material in a specified frequency range.
17 . The system according to claim 11 , wherein the materials information comprises a composition of the material.
18 . The system according to claim 11 , wherein the materials information comprises one or more physical characteristics of the material.
19 . The system according to claim 11 , further comprising one or more apparatuses arranged upstream of the one or more processors, the one or more apparatuses being configured to pre-treat the material before inputting the material into the one or more processors.
20 . The system according to claim 11 , wherein the features that define the processing protocol comprises conditions for depolymerizing the material in the one or more processors to obtain a depolymerized material.