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 method for predicting a processing protocol for treating a heterogeneous mixture of desired materials, comprising:
performing a plurality of experiments on a control set of materials, each one of the plurality of experiments comprising:
acquiring a materials information about the control set of material by one or more sensors;
treating the material using a processing protocol from a plurality of processing protocols that identifies conditions for processing materials;
obtaining an output of the treatment; and
analyzing the output to obtain training results of the experiments;
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 the plurality of experiments;
receiving an input for a desired material for treating wherein the input comprises a materials information about the desired material obtained by the one or more sensors;
creating, by the machine-learning model, a processing protocol for treating the desired material based on the input, wherein the creating of the processing protocol for treating the desired material comprises dynamically mapping the desired material to a newly created category to predict the processing protocol for treating the desired material;
treating the desired material using the processing protocol for treating the desired material;
obtaining an output from treating the desired material;
analyzing the output to obtain further training results; and
updating the machine-learning model by further training the machine-learning algorithm using the input, the processing protocol for treating the desired material, and the further training results.
2 . The method 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.
3 . The method according to claim 2 , wherein the specified frequency range is an infrared frequency range.
4 . The method according to claim 1 , wherein the materials information comprises a composition of the material.
5 . The method according to claim 1 , wherein the materials information comprises one or more physical characteristics of the material.
6 . The method according to claim 1 , wherein the plurality of experiments comprises in a range of from about 10 to about 1000 experiments.
7 . The method according to claim 1 , wherein the plurality of experiments comprises less than 500 experiments.
8 . The method according to claim 1 , wherein the treating of the material comprises depolymerizing the material in a reactor.
9 . The method according to claim 8 , wherein one or more features that define the processing protocol comprise conditions for depolymerizing the material in the reactor to obtain a depolymerized material.
10 . The method according to claim 9 , wherein the treating of the material comprises chemically and/or physically treating the depolymerized material.
11 . The method according to claim 10 , wherein the one or more features that define the processing protocol comprise conditions for chemically and/or physically treating the depolymerized material.
12 . The method according to claim 1 , wherein the treating of the material comprises chemically and/or physically treating the material before inputting the material into a reactor.
13 . The method according to claim 12 , wherein one or more features that define the processing protocol comprises conditions for chemically and/or physically treating the material before inputting the material into the reactor.
14 . The method according to claim 1 , wherein at least some of the materials information of the desired material for treating is different from the materials information of the materials in the control set of materials.
15 . A method for predicting a processing protocol for treating a heterogeneous mixture of desired materials, comprising:
receiving an input for a desired material for treating, wherein the input comprises a materials information about the desired material obtained by one or more sensors;
creating, by a machine-learning model, a processing protocol for treating the desired material based on the input,
wherein the creating of the processing protocol for treating the desired material comprises dynamically mapping the desired material to a newly created category to predict the processing protocol for treating the desired material,
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;
treating the desired material using the processing protocol for treating the desired material;
obtaining an output from treating the desired material;
analyzing the output to obtain further training results; and
updating the machine-learning model by further training the machine-learning algorithm using the input, the processing protocol for treating the desired material, and the further training results.
16 . The method according to claim 15 , wherein the materials information comprises a spectrum of the desired material in response to an electromagnetic interaction of the desired material in a specified frequency range.
17 . The method according to claim 16 , wherein the specified frequency range is an infrared frequency range.
18 . The method according to claim 15 , wherein the materials information comprises a composition of the desired material.
19 . The method according to claim 15 , wherein the materials information comprises one or more physical characteristics of the desired material.
20 . The method according to claim 15 , wherein the plurality of experiments comprises in a range of from about 10 to about 1000 experiments.
21 . The method according to claim 15 , wherein the plurality of experiments comprises less than 500 experiments.
22 . The method according to claim 15 , wherein the treating of the desired material comprises depolymerizing the desired material in a reactor.
23 . The method according to claim 22 , wherein one or more features that define the processing protocol for treating the desired material comprise conditions for depolymerizing the desired material in the reactor to obtain a depolymerized material.
24 . The method according to claim 23 , wherein the treating of the desired material comprises chemically and/or physically treating the depolymerized material.
25 . The method according to claim 24 , wherein the one or more features that define the processing protocol for treating the desired material comprise conditions for chemically and/or physically treating the depolymerized material.
26 . The method according to claim 22 , wherein one or more features that define the processing protocol for treating the desired material comprise conditions for chemically and/or physically treating the desired material before inputting the desired material into the reactor.
27 . The method according to claim 15 , wherein the treating of the desired material comprises chemically and/or physically treating the desired material before inputting the desired material into a reactor.
28 . The method according to claim 15 , wherein at least some of the materials information of the desired material for treating is different from the materials information of the materials in the control set of materials.