Patent Description:
<CIT> discloses a system comprising: a sludge holding unit that holds a sludge; an illumination unit that shines light in the near-infrared region on the sludge in the sludge holding unit; a light-receiving unit that receives light reflected from or transmitted through the sludge; and a control unit. Said control unit uses a precomputed regression equation together with the absorbance determined from the light received by the light-receiving unit to compute at least one of the following sludge properties to be analyzed: gross calorific value; net calorific value; moisture content; ash content; sulfur content; hydrogen content; and carbon content. The regression equation is for wavelengths directly ascribable to the property being measured, as per multiple-regression analysis of the second derivative of the spectral absorbance determined from reflected or transmitted light received from a sample sludge for which the property being measured is known, said sample sludge being illuminated by near-infrared light. This makes it possible to directly measure a property being analyzed for a sludge being measured, such as calorific value.

<CIT> discloses methods for measuring the composition of plant biomass and predicting the efficiency of conversion of such biomass to various end products under various processing conditions. For example, methods and materials for identifying plant material having higher levels of accessible carbohydrate, as well as materials and methods for processing plant material having higher levels of accessible carbohydrate are disclosed. Also disclosed are computer-implemented methods that provide improved biorefineries.

The article of <NPL>, discloses using NIRS techniques, together with advanced data analysis methods to determine biomass feedstock/product properties and directly monitor process reactors. It focuses on the introduction of the NIRS method and its applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production.

The present invention is defined in claims <NUM>, <NUM> and <NUM>. A sludge analysis system processes data that indicates a molecular content of a batch of sludge. The sludge may be a semi-solid slurry that is created from waste material, e.g., wastewater or the output of a product creation process. The sludge analysis system uses the data to predict a value of a property of the batch of sludge. For instance, the sludge analysis system may use the predicted value to customize a treatment process for the batch of sludge, predict a destination or use for the batch of sludge, determine a recommended destination or use, or predict an amount of usable material included in the batch of sludge that can be used for another process.

A predicted use for a batch of sludge may indicate another process, such as farming or bio-gas production, that is most likely to receive the greatest benefit from the batch of sludge given the molecular content of the batch of sludge. For instance, the sludge analysis system may determine a predicted use of "farming" for a batch of sludge that includes a high percentage of organic materials and a medium to low percentage of dry matter, which indicate good fertilization properties. The sludge analysis system may determine a predicted use of a "waste to energy" or "biogas processing" for a batch of sludge that includes organic materials and a high percentage of dry matter. A predicted or recommended destination can be a particular entity that uses the batch of sludge, such as a particular farm or a particular waste to energy plant.

In some implementations, the sludge analysis system may determine a predicted value for a property that represents a quality or another property for a commodity trading platform. For instance, the sludge analysis system may determine a customized treatment process for a batch of sludge based on a predicted destination of the batch of sludge given a type of entity, e.g., a farmer, that can acquire the batch of sludge using the commodity trading platform. The sludge analysis system according to the claimed invention uses <NUM>. the molecular content of the batch of sludge, and may additionally use the potential destination and/or a source of the batch of sludge to determine the predicted value.

In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving molecular content data that indicates a molecular content of a portion of a batch of sludge; determining, by a machine learning module included in the system using the molecular content data and multiple parameters, a predicted value for a property of the portion of the batch of sludge; and providing the predicted value for the property of the portion of the batch of sludge. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

The foregoing and other embodiments can each optionally include one or more of the following features, alone or in combination. The method may include receiving an actual value for the property of the batch of sludge; and updating, by the machine learning module, at least one of the parameters using the actual value for the property of the batch of sludge. According to the claimed invention receiving the molecular content data that indicates the molecular content of the batch of sludge includes receiving, from a near infrared spectrometer, near-infrared spectroscopy data. A system may include a near infrared spectrometer. The method may include scanning, by the near infrared spectrometer, the batch of sludge to determine the molecular content data.

In some implementations, determining, by the machine learning module included in the system using the molecular content data and the multiple parameters, the predicted value for the property of the batch of sludge may include determining, in parallel by the machine learning module for each of two or more properties of the batch of sludge, a predicted value for the respective property. Determining, in parallel by the machine learning module for each of the two or more properties of the batch of sludge, the predicted value for the respective property may include determining, for each of the two or more properties of the batch of sludge using regression analysis, the predicted value for the respective property. Determining, in parallel by the machine learning module for each of the two or more properties of the batch of sludge, the predicted value for the respective property may include determining, for each of the two or more properties of the batch of sludge using non-linear classification, the predicted value for the respective property. According to the claimed invention determining, by the machine learning module included in the system using the molecular content data and the multiple parameters, the predicted value for the property of the batch of sludge includes determining the predicted value for the property of the batch of sludge using the molecular content data, the multiple parameters, and one or more potential uses for the batch of sludge.

In some implementations, providing the predicted value for the property of the batch of sludge may include providing, to a treatment module, the predicted value for the property of the batch of sludge to cause the treatment module to determine a recommended treatment process for the batch of sludge given the predicted value for the property of the batch of sludge and one or more potential uses for the batch of sludge. Receiving the actual value for the property of the batch of sludge may include receiving the actual value for the property of the batch of sludge after a treatment system processes the batch of sludge using the recommended treatment process. The method may include determining, using the molecular content data, a recommended potential use for the batch of sludge. Providing, to the treatment module, the predicted value for the property of the batch of sludge may include providing, to the treatment module, the recommended potential use and the predicted value for the property of the batch of sludge to cause the treatment module to determine a recommended treatment process for the batch of sludge given the predicted value for the property of the batch of sludge and one or more potential uses for the batch of sludge. According to the claimed invention determining, using the predicted value, the recommended potential use for the batch of sludge includes determining, using the predicted value for the property of the batch of sludge and current weather conditions at at least one potential destination and/or predicted weather conditions at the at least one potential destination, the recommended potential use for the batch of sludge. The method may include determining, by the treatment module, the recommended treatment process for the batch of sludge given the predicted value for the property of the batch of sludge and the one or more potential uses for the batch of sludge.

In some implementations, determining, by the machine learning module included in the system using the molecular content data and the multiple parameters, the predicted value for the property of the batch of sludge may include determining, by the machine learning module, the predicted value using, as input to the machine learning module, characteristics of a potential purchaser, environmental parameters, or both. The environmental parameters may include at least one of: environmental parameters of a treatment system; environmental parameters for a storage facility at which the batch of waste material is located; environmental parameters for a storage facility at which the batch of waste material will be located prior to delivery to a potential destination; or environmental parameters during transportation. The characteristics of a potential purchaser may include at least one of: environmental parameters for the potential purchaser; characteristics of how a batch of waste material will be used by the potential purchaser; or desired waste material properties for the potential purchaser. Determining, by the machine learning module included in the system using the molecular content data and the multiple parameters, the predicted value for the property of the batch of sludge may include determining at least one of: a predicted value for a percentage of dry matter of the batch of sludge; a predicted value for a percentage of a primary material included in the batch of sludge; a predicted value for a percentage of volatile solids included in the batch of sludge; a predicted value for a percentage of biogas material included in the batch of sludge; a predicted value for a percentage of organic matter included in the batch of sludge; a predicted value for a percentage of phosphorus included in the batch of sludge; a predicted value for a percentage of zinc included in the batch of sludge; or a predicted value for a percentage of material included in the batch of sludge that can be reused.

In some implementations, providing the predicted value for the property of the batch of sludge may include generating instructions for presentation of the predicted value for the property of the batch of sludge in a user interface. Receiving the actual value for the property of the batch of sludge may include receiving data indicating user input of the actual value for the property of the batch of sludge. Generating the instructions for presentation of the predicted value for the property of the batch of sludge in a user interface may include generating the instructions for presentation of a user interface that includes a filter option to enable a user to view details about multiple different batches of sludge, including the batch of sludge, and to filter details about batches of sludge using the predicted values for the property of the respective batch of sludge. Generating instructions for presentation of the predicted value for the property of the batch of sludge in a user interface may include enabling a user to select the batch of sludge for purchase. Generating instructions for presentation of the predicted value for the property of the batch of sludge in a user interface may include enabling a user to select the batch of sludge for purchase using a blockchain smart contract.

The subject matter described in this specification can be implemented in various embodiments and may result in one or more of the following advantages. In some implementations, determining a recommended treatment process using a predicted value of a property for a batch of waste material can reduce treatment processing time, reduce materials used during treatment processes, optimize treatment processing, e.g., for a recommended use or a recommended destination, or a combination of two or more of these. In some implementations, the systems and methods described below can reduce treatment costs, optimize waste material delivery and trading, reduce a quantity of waste material delivered to landfills, or a combination of two or more of these.

<FIG> depicts an example of an environment <NUM> that includes a waste material processing system <NUM> that analyzes waste material, such as sludge. For example, the waste material processing system <NUM> may receive the waste material from a home <NUM>, a factory, e.g., as waste from products that are being built, or a restaurant. The waste material processing system <NUM> may use one or more of a separation system <NUM>, a waste material analysis system <NUM>, and a treatment system <NUM> to process the waste material for reuse at a destination <NUM>.

For example, the waste material analysis system <NUM> may process data about batches of waste material received from the separation system <NUM> to determine properties of the material included in the batches of waste material. The waste material analysis system <NUM> may determine recommended treatment processes for each of the batches and provide the treatment system <NUM> data about those treatment processes, e.g., instructions to cause the treatment system <NUM> to execute those treatment processes.

After processing a batch of waste material, the waste material processing system <NUM> provides the processed waste material to a transport system <NUM> that delivers the processed waste material to a destination <NUM>. The transport system <NUM> may be any appropriate type of transport system, e.g., using vehicles, ships, planes, or a combination of two or more of these. The destinations <NUM> may include a waste to energy system 114a, agriculture 114b, e.g., a farm, or a landfill 114c.

The waste material analysis system <NUM> may include a waste material intake <NUM>, one or more sensors, e.g., one or more near infrared spectrometers <NUM>, or both. For instance, the waste material intake <NUM> may receive material from the separation system <NUM>. The material may be part of a batch of waste material or an entire batch of waste material.

The near infrared spectrometers <NUM> captures molecular content data for the material received in the waste material intake <NUM>. For example, the near infrared spectrometers <NUM> may capture near-infrared spectroscopy data that indicates molecular content properties of the material. In some implementations, the waste material analysis system <NUM> may include other types of sensors that can capture molecular content data for the material received in the waste material intake <NUM>. The molecular content data may indicate average property values, minimum property values, maximum property values, or a combination of two or more of these, for an entire batch of waste material or a portion of a batch of waste material. The waste material may be sludge.

A machine learning module <NUM>, included in the waste material analysis system <NUM>, receives the molecular content data. The machine learning module <NUM> processes the molecular content data to determine one or more predicted values for properties of the batch of waste material. The predicted values may be predicted minimum values for a batch of waste material, predicted average values for a batch of waste material, predicted maximum values for a batch of waste material, or a combination of two or more of these.

For instance, a property analysis module <NUM> may use historical data <NUM> and the molecular content data to determine a predicted value for at least one of the properties of the batch of waste material. The predicted value may be a predicted value after treatment of the batch of waste material, a predicted value for the batch of waste material before delivery of the batch of waste material to one of the destinations <NUM>, a predicted value for the batch of waste material upon delivery of the batch of waste material to one of the destinations <NUM>, a predicted sale value for the batch of waste material, a predicted value for the batch of waste material after one of the destinations <NUM> uses the batch of waste material, or a combination of two or more of these. In some examples, the machine learning module <NUM> may determine multiple predicted values, e.g., for different stages of the batch of waste material. The predicted value may be a maximum value, a minimum value, or an average value for the batch of waste material.

When the predicted value is a predicted value after treatment of the batch of waste material, the property analysis module <NUM> predicts how a treatment process may change one or more properties of the batch of waste material. The property analysis module <NUM> may use a potential use of the batch of waste material at one of the destinations <NUM> to predict the value of a property that affects the potential use, e.g., for a property that indicates a likelihood that the batch of waste material can be used for the potential use. For example, the property analysis module <NUM> may predict a percentage of organic material that will be included in a batch of waste material when an intended or recommended use is farming. The property analysis module <NUM> may predict a percentage of biogas material that will be included in a batch of waste material when an intended or recommended use is for a waste to energy process.

The machine learning module <NUM> or a recommendation module <NUM> may determine a recommended use, recommended destination, or both, for a batch of waste material, e.g., in addition to or separate from the predicted value of the property. The machine learning module <NUM> may use the recommended use, recommended destination, or both, to determine the predicted value of the property. For example, when a batch of waste material has a high average amount of organic material, the machine learning module <NUM> may determine a recommended use of "farming" for an "agricultural" destination 114b. The machine learning module <NUM> may determine a current value of organic material included in the batch of waste material, e.g., as a percentage or volume.

The machine learning module <NUM> may predict a value for a property of the batch of waste material before the batch of waste material is delivered to one of the destinations <NUM>. The predicted value before delivery may be a predicted value before the batch of waste material is transported <NUM> to a destination <NUM>, before the batch of waste material is placed on a transport vehicle, or another time before transport <NUM>. The predicted value may be a value for the batch of waste material after treatment processing and before delivery. The predicted value may be a value for a batch of waste material that will not have treatment processing or that will not have any additional treatment processing, e.g., after the waste material analysis system <NUM> receives the molecular content data and determines the predicted value. For instance, the waste material analysis system <NUM> may perform multiple predictions of values for properties of a batch of waste material at different stages of processing: before treatment, e.g., to determine a recommended treatment process, during treatment, after treatment, after a batch of waste material has been at a waste material processing system <NUM> without any processing, e.g., and is in a storage facility, immediately before a batch of waste material is loaded for transport, or a combination of two or more of these.

The machine learning module <NUM> may predict the value of a property, e.g., organic material, using the current value and the recommended use. The predicted value may be a predicted value after treatment. In some examples, the machine learning module <NUM> may use both the property analysis module <NUM> and a treatment module <NUM> when determining the predicted value for the property. For instance, the property analysis module <NUM>, or another module in the machine learning module <NUM>, may determine a recommended use for the batch of waste material using the molecular content data as input to the property analysis module. The treatment module <NUM> may use the recommend use and molecular content data as input to determine a recommended waste material treatment to process the batch of waste material. The treatment module <NUM> may determine a recommended waste material treatment that will increase, decrease, or both, one or more properties of the waste material to improve the waste material for the recommended use, for use at the recommended destination, or both. The treatment module <NUM> or the property analysis module <NUM> may determine the predicted value for the property. For instance, the treatment module <NUM> may determine the predicted property when determining the recommended waste material treatment. In some examples, the property analysis module <NUM> may use the recommended waste material treatment, e.g., as input, to determine the predicted value.

The machine learning module <NUM> may predict a value for a property of the batch of waste material upon delivery of the batch of waste material to one of the destinations <NUM>. For instance, the value for the property may account for any changes in the batch of waste material during processing, transportation, other stages through which the batch of waste material proceeds until the batch of waste material is delivered to one of the destinations <NUM>, or a combination of two or more of these. In some examples, the property may be a predicted percentage of dry matter. The machine learning module <NUM> may use current weather conditions or predicted weather conditions to determine the predicted value for the dry matter percentage. In some examples, the machine learning module <NUM> may use a humidity level, a predicted chance of rain, or both, to determine the predicted percentage of dry matter for the batch of waste material. In some implementations, the property may be a cost for the batch of waste material such that the predicted value indicates a predicted sale value. In some implementations, the property may be a percentage of volatile solids or biogas material that will be included in the batch of waste material upon delivery.

Some example properties for a predicted value for a property of the batch of waste material after delivery of the batch of waste material to one of the destinations <NUM> include volume, volatile solids, biogas materials, organic matter, dry matter, or a combination of two or more of these. The value of the property may be after the batch of waste material is used at a destination <NUM>, while the batch of waste material is being used at a destination <NUM>, or another time after the batch of waste material is delivered. A volume may change when the batch of waste material absorbs or releases moisture, gas, or another material. A percentage of volatile solids may change as the materials in the batch of waste material change in temperature, emit moisture or other material, or otherwise change during processing, transportation, delivery, or a combination of two or more of these. A percentage of organic matter may change as oxygen or another element reacts with the material included in the batch of waste material. In some implementations, the predicted value may be a predicted sale value or a predicted cost for the batch of waste material upon delivery of the batch of waste material. One or more of these properties may change during processing, transportation, delivery, or a combination of two or more of these. In some examples, the machine learning module <NUM> may determine multiple predicted values for different properties. When the waste material analysis system <NUM> determines multiple different predicted values, the corresponding actual values may be measured at the same time, different times, or a combination of both.

The machine learning module <NUM> may use the molecular content data, the historical data <NUM>, or both, as input. For example, the machine learning module <NUM> may initially perform a training process using the historical data <NUM> and update one or more parameters of the machine learning module <NUM> during training. The historical data <NUM> may include predicted values and actual values for sets of molecular content data. The machine learning module <NUM> may use a supervised learning process to update the parameters for the machine learning module <NUM> using the historical data <NUM>. In some implementations, the historical data <NUM> may include treatment process data that indicates a treatment process used for a batch of waste material and corresponding predicted and actual values for the batch of waste material. In these implementations, the machine learning module <NUM> may use the historical data <NUM> to adjust the parameters to cause the machine learning module <NUM> to predict a recommended treatment process for a batch of waste material. The machine learning module <NUM> may later, e.g., during run-time, use the molecular content data to determine a predicted value.

In some implementations, the machine learning module <NUM> may use only the molecular content data as input and the historical data <NUM> may represent parameters determined by the machine learning module <NUM> to represent a machine learning algorithm used to analyze the input molecular content data. For instance, the machine learning module <NUM> may update the historical data <NUM> using a predicted value for a property and an actual value for a property, e.g., as part of a feedback process. The machine learning module <NUM> may receive the actual value for the property after a treatment process, e.g., from the near infrared spectrometers <NUM> or other sensors that measures the value, during transport <NUM>, or after delivery to one of the destinations <NUM>. The machine learning module <NUM> may use the predicted value and the actual value to update the parameters, e.g., the historical data, during a training process.

The machine learning module <NUM> may use parameters for any appropriate artificial intelligence process to analyze the molecular content data and determine the predicted value or the predicted values. In some implementations, the machine learning module <NUM> may use parameters for a non-linear classification of the molecular content data to determine the predicted value. The machine learning module <NUM> may use parameters for regression analysis of the molecular content data to determine the predicted value. Some examples of models that include the parameters include support vector machines, linear regression models, and random forest modelling. A type of model used for the parameters of the machine learning module <NUM> may be selected to balance analysis speed of the machine learning module <NUM>, e.g., the time required by the machine learning module <NUM> to determine a predicted value after receiving molecular content data, and accuracy of the machine learning module <NUM>. For instance, the machine learning module <NUM> may use parameters that represent a support vector machine or a random forest model to improve an accuracy of the predicted value. In some implementations, the machine learning module <NUM> may use parameters for random forest modelling to allow the machine learning module <NUM> to analyze multiple decision trees in parallel.

In some implementations, the property analysis module <NUM> and the treatment module <NUM> are the same model. For instance, a single model may use the parameters to determine at least two of a recommended treatment process, a recommended use, a recommended destination, and one or more predicted values for properties of a batch of waste material.

In some implementations, the recommendation module <NUM> can be part of or included in the machine learning module <NUM>. For instance, a single module may determine the predicted value, the recommended treatment process, and the recommended use. In some implementations, a single module may determine the recommended treatment process and the recommended use.

The recommendation module <NUM> may use the predicted value for the property of the batch of sludge, the molecular content data, or both, to determine a recommended use, a recommended destination, or both, for the batch of waste material. For instance, the property analysis module <NUM> may determine the predicted value for the batch of waste material using the molecular content data as input. The recommendation module <NUM> receives the predicted value and uses the predicted value as input, potentially with the molecular content data, to determine the recommended use, the recommended destination, or both.

A recommended use may be a particular type of process or system in which the batch of waste material can be used. For instance, recommended uses may include waste to energy processes, agriculture, or manufacturing.

A recommended destination may be a particular system that uses a process that can include the batch of waste material. For example, while a recommended use may be agriculture, a recommended destination may be farm A or farm B.

In some implementations, the recommendation module may determine a ranked list of recommended uses, a ranked list of recommended destinations, or both. The ranked list of recommended destinations can include a first destination that has a first use, followed by a second destination that has a second use, and then a third destination that also has the first use. For example, the ranked list of destinations may include farm A followed by energy plant A and then farm B. A ranked list of recommended uses may include agriculture and then waste to energy.

The waste material analysis system <NUM> may use the ranked list of recommended uses, the ranked list of recommended destinations, or both, to determine the recommended waste material treatment. In some examples, the waste material analysis system <NUM> may generate instructions for presentation of one or both ranked lists in a user interface.

The recommendation module <NUM> may use data that indicates properties of a potential destination as input. For instance, the recommendation module <NUM> may use data that indicates properties for the destinations <NUM> when determining a single recommended destination or a ranked list of recommended destinations. In some examples, the recommendation module <NUM> may use the data that indicates properties for the potential destination during a training process during which parameters of the recommendation module <NUM> are updated based on molecular content data as input and output that identifies particular recommended destinations. Use of molecular content data, e.g., a predicted value or a set of molecular content data for multiple properties of a batch of waste material, as input along with properties for the destinations, e.g., properties that indicate how waste material will be used or desired waste material properties for the destination, may allow the recommendation module <NUM> to be more robust given potential changes to the properties for the destinations over time, e.g., without necessarily requiring additional training of the recommendation module <NUM>.

In some implementations, the treatment module <NUM> uses data from the recommendation module <NUM> to determine a recommended treatment process. For instance, the treatment module <NUM> may determine a treatment process with a highest likelihood of optimizing a batch of waste material for a particular use, a particular destination, or both.

In some implementations, the property analysis module <NUM> may determine a particular predicted value based on a recommended use, a recommended destination, or both. For example, the property analysis module <NUM> can use, as input, the recommended use, the recommended destination, or both, to determine the predicted value. When the recommended use is agriculture, the property analysis module <NUM> may determine a minimum amount of organic material, a maximum amount of heavy metals, or both, that will likely be included in a batch of waste material. In some examples, when the recommended use is waste to energy, the property analysis module may determine an average amount of biogas material, primary material included in the waste material, a maximum amount of heavy metals, or a combination of two or more of these.

The waste material analysis system <NUM> can include a blockchain enabler <NUM> that provides third party systems with information about a batch of waste material, e.g., the predicted value or molecular content data or both, create contracts with third party systems for delivery of a batch of waste material, or both. For example, the blockchain enabler <NUM> may store, in a distributed blockchain database, the information about a batch of waste material. The information about a batch of waste material may include the predicted value, the molecular content of a batch of waste material, e.g., before or after treatment processing or both, a source of the batch of waste material, or other information about the batch of waste material.

When the recommendation module <NUM> determines a recommended use, a recommended destination, or both, the blockchain enabler <NUM> can store data about the recommended use, the recommended destination, or both. This may allow the blockchain enabler <NUM> to provide potential destinations, e.g., recommended destinations, with information about a batch of waste material. For instance, the blockchain enabler <NUM> may determine that given the recommended use, the recommended destination, or both, a particular destination can likely use a corresponding batch of waste material. The blockchain enabler <NUM> may provide the particular destination, e.g., a computer for the particular destination, with the information about the batch of waste material to allow the particular destination to confirm whether the corresponding batch of waste material should be delivered to the particular destination.

Upon receipt of confirmation data indicating that the corresponding batch of waste material should be delivered to the particular destination, the blockchain enabler <NUM> stores, in the distributed blockchain database, data that indicates that the corresponding batch of waste material should be delivered to the particular destination. The data may indicate transportation type for the delivery and other delivery details.

The waste material analysis system <NUM> may use any appropriate system to provide a third party systems with information about a batch of waste material. In some implementations, the waste material analysis system may use a web interface <NUM> to provide information about a batch of waste material. The web interface <NUM> may provide, to a third party system, instructions that cause presentation of an interface that includes the information about the batch of waste material. The third party system can present the interface in a web browser or another application.

In some implementations, the waste material analysis system <NUM> may receive the molecular content data that indicates a molecular content of a batch of waste material, or a portion of a batch of waste material, from a device that is not included in the waste material analysis system <NUM>. For instance, the waste material analysis system <NUM> may be physically located separate from a physical location for the waste material processing system <NUM>. The waste material analysis system <NUM> may receive the molecular content data from a sensor, e.g., one of the near infrared spectrometers <NUM>, located at the separate waste material processing system <NUM> or at another physical location at which the batch of waste material is located.

The waste material analysis system <NUM> may receive the molecular content data from a mobile application, e.g., installed on a mobile device. For example, the mobile device may receive the molecular content data from a sensor and provide the molecular content data to the waste material analysis system <NUM>. In these implementations, the waste material analysis system <NUM> may be a cloud service that analyzes the molecular content of the waste material. The waste material analysis system <NUM> may provide the mobile device with a recommended treatment process, data about the molecular content of the waste material, or other data, e.g., a recommended sale price for the waste material.

When the near infrared spectrometers <NUM> are not included in the waste material analysis system <NUM>, the machine learning module <NUM>, and the waste material analysis system <NUM>, may receive the molecular content data from the near infrared spectrometers <NUM> using a network interface. The network interface may be part of or used by the web interface <NUM>. For instance, the waste material analysis system <NUM> may include a network interface that receives the molecular content data from the near infrared spectrometers <NUM>, or other sensors, using a network protocol.

In some implementations, the waste material analysis system <NUM> may include an application programming interface (API) that receives the molecular content data from an external system, e.g., that includes the near infrared spectrometers <NUM> or other sensors. The API can allow the waste material analysis system <NUM> to communicate with other systems, e.g., to receive molecular content data, send treatment recommendations, send predicted values for properties, or a combination of two or more of these.

The waste material analysis system <NUM> is an example of a system implemented, at least in part, as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described in this document are implemented. The mobile devices may include personal computers, mobile communication devices, and other devices that can send and receive data over a network. The network (not shown), such as a local area network (LAN), wide area network (WAN), the Internet, or a combination thereof, connects the mobile devices, and the waste material analysis system <NUM>. The waste material analysis system <NUM> may use a single server computer or multiple server computers operating in conjunction with one another, including, for example, a set of remote computers deployed as a cloud computing service.

<FIG> is a flow diagram of a process <NUM> for updating parameters used in sludge analysis. For example, the process <NUM> can be used by the waste material analysis system <NUM> from the environment <NUM>. Although the example described with reference to <FIG> refers to sludge, a system may use one or more steps of the process <NUM> for updating parameters for analysis of other types of waste material.

A waste material analysis system receives molecular content data that indicates a molecular content of a batch of sludge (<NUM>). For instance, the waste material analysis system receives the molecular content data from one or more sensors included in the waste analysis system or from another system, e.g., that includes one or more sensors. The sensors can scan the batch of sludge or a portion of the batch of sludge to determine the molecular content data for the scanned portion of the batch of sludge.

The waste material analysis system determines, using the molecular content data and multiple parameters, a predicted value for a property of the batch of sludge (<NUM>). The predicted value for the property may be for any property of the batch of sludge. For instance, the property may be a percentage of dry matter of the batch of sludge; a primary material included in the batch of sludge; a percentage of volatile solids included in the batch of sludge; a percentage of biogas material included in the batch of sludge; a percentage of organic matter included in the batch of sludge; a percentage of phosphorus included in the batch of sludge; a percentage of nitrogen included in a batch of sludge; a percentage of zinc included in the batch of sludge; a percentage of material included in the batch of sludge that can be reused; a predicted amount of usable material included in a batch of sludge; or a property that represents a combination of two or more of these. In some examples, a percentage of biogas may be represented by a percentage of methane included in a batch of sludge. The waste analysis system may include methane percentage as a property. A percentage of zinc may represent a percentage of heavy metals included in a batch of sludge. The waste analysis system may include heavy metals as a property. The predicted amount of usable material included in a batch of sludge may be determined based on a recommended use, a recommended destination, or both. For example, the predicted amount of usable material may indicate an amount of material included in a batch of sludge that can be used for a recommended use, a process at a recommended destination, or both. The predicted amount of usable material may indicate a predicted amount of material that can be extracted from a batch of sludge and used at a destination.

In some implementations, the waste material analysis system may determine chemical components included in a scanned portion of the batch of sludge using the molecular content data. The waste material analysis system can then use data about the chemical components to determine the predicted value for the property of the batch of sludge, e.g., as input to a model that determines the predicted value.

The waste material analysis system may determine predicted values for each of two or more properties for a batch of sludge. When determining predicted values for multiple properties, the waste material analysis system may determine multiple predicted values in parallel. For example, the waste material analysis system may use regression analysis, non-linear classification, or both, to determine the multiple predicted values in parallel. The waste material analysis system may determine all of the multiple predicted values concurrently. In some examples, the waste material analysis system may determine two of the multiple predicted values concurrently and then determine additional predicted values after determining at least one of the two predicted values.

The waste material analysis system determines, using the molecular content data, a recommended potential use for the batch of sludge (<NUM>). The waste material analysis system may use the predicted value for the property of the batch of sludge as input, in addition to or instead of, the molecular content data. In some implementations, the waste material analysis system may determine a recommended potential destination.

The waste material analysis system may use current weather conditions, predicted weather conditions, or both, to determine the recommended potential use for the batch of sludge. For instance, the waste material analysis system may determine multiple potential destinations for the batch of sludge. The waste material analysis system may use physical location information for each of the potential destinations to determine current weather conditions, predicted weather conditions, or both, for each of the multiple potential destinations. In some examples, when two of the potential destinations are physically located close to each other, e.g., in the same city, the waste material analysis system determines the current weather conditions, the predicted weather conditions, or both, for the two potential destinations at the same time, e.g., once.

The waste material analysis system can use the current weather conditions, the predicted weather conditions, or both, to determine a recommended potential use, a recommended potential destination, or both. For instance, when predicted weather conditions indicate that farm A will be dry and farm B will be wet, e.g., it will rain at farm B in two weeks but there will not be any rain at farm A in the next two weeks, the waste material analysis system may determine that farm A is a recommended destination for a batch of sludge with a high percentage of organic material and a low percentage of dry matter.

The waste material analysis system provides, to a treatment module, the recommended potential use, the predicted value for the property of the batch of sludge, or both (<NUM>). The waste material analysis system determines whether to generate a recommended treatment process for the batch of sludge (<NUM>). For example, the treatment module, or another module of the waste material analysis system, receives the predicted value, the recommended potential use, or both, from the waste material analysis system. The waste material analysis system may provide the recommended potential destination to the treatment module. In some examples, the waste material analysis system may provide the molecular content data to the treatment module.

The treatment module can use the received data to determine whether the batch of sludge should receive treatment processing. The treatment module may select a treatment process that has at least a threshold likelihood of improving a value for a property of the batch of sludge. The property may be the property to which the predicted value corresponds.

In response to determining to generate a recommended treatment process, the waste material analysis system generates a recommended treatment process for the batch of sludge given the predicted value for the property of the batch of sludge, the one or more potential uses for the batch of sludge, or both (<NUM>). For instance, the treatment module may use the received data as input to an artificial intelligence process to determine the recommended treatment process for the batch of sludge. In some implementations, the waste material analysis system may use current weather conditions, predicted weather conditions, or both, as input to the artificial intelligence process to determine the recommended treatment process for the batch of sludge.

The waste material analysis system receives an actual value for the property of the batch of sludge after a treatment system processes the batch of sludge using the recommended treatment process (<NUM>). For example, the waste material analysis system receives, from a treatment processing system, the actual value for the property. The treatment processing system may include one or more sensors used to capture the actual value for the property, e.g., as part of updated molecular content data for the batch of sludge after treatment.

The waste material analysis system generates instructions for presentation of a predicted value for a property of the batch of sludge in a user interface (<NUM>). The waste material analysis system may generate the instructions in response to determining not to generate a recommended treatment process. In some examples, the waste material analysis system may generate the instructions after receiving the actual value for the property of the batch of sludge, e.g., after performing step <NUM>. The instructions may cause a device, e.g., a user device such as a mobile device, to present the predicted value in a user interface. The instructions may be for a web browser or another application. The waste material analysis system may generate the instructions and provide the instructions to a device to provide the predicted value for the property of the portion of the batch of sludge.

In some implementations, the user interface can include information about multiple different batches of sludge, including the batch of sludge for which the predicted value of the property was determined. Each of the multiple different batches of sludge may have a corresponding predicted value for the property. The user interface can include a filter option to enable a user, e.g., interacting with the user interface, to filter details about batches of sludge given values of the properties of the batches of sludge. For instance, the filter may enable the user to filter the presented details based on the property and the corresponding predicted values for the property of the respective batch of sludge. The user interface can enable a user to select a batch of sludge for delivery to a destination.

In some implementations, the waste material analysis system may provide the instructions to a system for the recommended potential destination. For instance, the waste material analysis system may provide the instructions to a computer operated on behalf of or for the recommended potential destination.

In some implementations, the waste material analysis system may provide other data, instead of instructions, to the system for the recommended potential destination. The data may be part of an automated transaction process used to determine whether to send the batch of sludge to a physical location for the recommend potential destination.

The waste material analysis system receives data indicating user input of an actual value for a property of the batch of sludge (<NUM>). For example, the data indicating the user input may indicate the actual value for the property upon request that the batch of sludge be delivered to a destination, upon delivery, after the batch of sludge was used at a destination, or a combination of two or more of these. In some implementations, when the property is a sale value, the predicted value is a predicted sale value and the actual value is the actual sale value for a batch of sludge. In some implementations, when the waste material analysis system generates a recommended treatment process and generates instructions for presentation of a predicted value, the predicted value used to determine the treatment process may be the same value as the predicted value indicated by the instructions, e.g., the waste material analysis system may determine a single predicted value. In some implementations, when the waste material analysis system generates a recommended treatment process and generates instructions for presentation of a predicted value, the predicted value used to determine the treatment process may be a different predicted value than the predicted value indicated by the instructions. The different predicted values may be for the same property, e.g., a percentage of organic matter, or for different properties.

The waste material analysis system updates at least one of the multiple parameters using the actual value for the property of the batch of sludge (<NUM>). The waste material analysis system, e.g., a machine learning module, may update at least one of the multiple parameters using the actual value for a first property received after the treatment system processes the batch of sludge, using the data indicating the user input of the actual value for a second property, or both. For example, the waste material analysis system may update at least one of the multiple parameters as part of a reinforcement learning process.

In response to input indicating selection of the batch of sludge, the waste material analysis system creates a smart contract with an entity, transfers the batch of sludge to a physical location for the entity, or both (<NUM>). The input may indicate user input of a selection of a batch of sludge, e.g., on a trading platform. The entity may employ the user. The input may indicate an automated system, e.g., used by the entity, determining that the batch of sludge should be delivered to a physical location for the entity, e.g., a destination at which the entity can use the batch of sludge.

The smart contract may be any appropriate type of smart contract and may indicate an intent to transfer the batch of sludge from a first physical location, e.g., a waste processing facility, to a second location, e.g., for the entity. The smart contract may be a blockchain contract.

Transfer of the batch of sludge to the physical location may include physically placing the batch of sludge on a transport mechanism, e.g., a vehicle, to deliver the batch of sludge to the physical location. The batch of sludge may be transferred using any appropriate methods. The entity may be an entity purchasing the batch of sludge.

In some implementations, the input indicating selection of the batch of sludge may be the data indicating the user input of the actual value for the property of the batch of sludge. For instance, when the actual value is an actual price paid offered by the entity to purchase the batch of sludge, the waste material analysis system may, in response to receipt of the actual value, create the smart contract, initiate transfer of the batch of sludge, or both.

Initiation of transfer of the batch of sludge may include the waste material analysis system sending instructions to a transportation system to initiate the transfer of the batch of sludge. The transportation system may receive the instructions and use the instructions to retrieve the batch of sludge and deliver the batch of sludge to the physical location for the entity.

The order of steps in the process <NUM> described above is illustrative only, and updating the parameters used in the sludge analysis can be performed in different orders. For example, the waste material analysis system may determine the recommended potential use and then determine the predicted value for the property of the batch of sludge. In some examples, the waste material analysis system may determine, concurrently, the recommended potential use and the predicted value for the property of the batch of sludge. For instance, the waste material analysis system may use a single module that receives the molecular content data as input and determines both the recommended potential use and the predicted value. Instead of or in addition to determining the recommended potential use, the waste material analysis system may determine a recommended potential destination. In some implementations, the waste material analysis system may receive the data indicating the user input of the actual value after or concurrently with creation of the smart contract, transfer of the batch of sludge to the physical location, or both.

In some implementations, the process <NUM> can include additional steps, fewer steps, or some of the steps can be divided into multiple steps. For example, the waste material analysis system may receive the molecular content data, determine the predicted value, and provide the predicted value, e.g., to a treatment module, without performing the other steps of the process <NUM>. In some implementations, the waste material analysis system may perform these steps and then receive the actual value, and update at least one of the multiple parameters.

In some implementations, a waste material processing facility may include one or more sensors, e.g., near infrared spectrometers. For example, a sewerage treatment works may include the one or more sensors. The sensors at the waste material processing facility may scan waste material, e.g., at regular intervals, to determine molecular content data for the waste material. The waste material processing facility may determine a batch of waste material, e.g., that is physical grouped together. Each batch may have a predetermined size, e.g., the same size, or at most a predetermined size. One set of molecular content data captured during one of the intervals may be for a single batch or multiple batches of waste material.

The waste material processing facility may send, to a cloud service that includes a waste material analysis system, the molecular content data. The waste material processing facility may use an API to send the molecular content data to the cloud service. In some examples, the waste material processing facility, e.g., a user at the waste material processing facility, may use an application installed on a device, e.g., a mobile device, to send the molecular content data to the cloud service. The mobile device can receive the molecular content data from the sensors.

The cloud service can use the molecular content data as input to a module to determine the chemical components of the scanned waste material. The cloud service can use data for the chemical components, the molecular content data, or both, as input to one or more modules to determine a predicted value for a property of the batch of waste material, a recommended treatment process, a recommend use for the batch of waste material, a recommended destination for the batch of waste material, or a combination of two or more of these. In some examples, the predicted value can be a recommend sale price, or a minimum initial bidding price for purchase of the batch of waste material. The cloud service may determine a recommended treatment process specific to the waste material processing facility and the treatment processes available at the waste material processing facility.

The cloud service may determine a recommended treatment process based on a likelihood that the recommended treatment process will maximize a quantity of target chemicals, e.g., target materials, included in the batch of waste material, maximize a quality of target chemicals included in the batch of waste material, or both. For example, the cloud service may analyze multiple potential treatment processes and the molecular content data to determine a recommended treatment process with a highest likelihood of maximizing a quantity, quality, or both, of material in the batch of waste material. The cloud service may use a recommended use, a recommended destination, or both, to determine particular material in the batch of waste material for which the treatment process should maximize the quantity, quality, or both.

In some implementations, the cloud service may use market data, treatment site data, or both, to determine the predicted value, the treatment process, or both. For instance, the cloud service may use a demand, e.g., a local demand, current prices or price trends, treatment process available at the waste material processing facility, or a combination of two or more of these, to determine the predicted value, the recommended treatment process, the recommended use, the recommended destination, or a combination of two or more of these.

The waste material processing facility receives, from the cloud service, data for the predicted value for the property, the recommended treatment process, the recommended use, the recommended destination, or a combination of two or more of these. The waste material processing facility may present, in a user interface, some or all of the received data. For example, a device for the waste material processing facility, e.g., physically at a physical location of the waste material processing facility or at another physical location, may present the predicted value for the property, the recommended treatment process, the recommended use, the recommended destination, or a combination of two or more of these. The device that presents some or all of the received data may be the same device that provided the molecular content data to the cloud service or a different device.

In some implementations, the waste material processing facility may provide at least some of the data to a trading platform to allow a third party, e.g., associated with a potential destination such as a recommended destination, to purchase a batch of waste material. The trading platform may be a bidding platform, e.g., based on a minimum bid price, a sales platform, e.g., based on a listed sale price, or a combination of both.

The waste material processing facility may automatically provide data for a batch of waste material to the trading platform, may automatically create a contract for a sale of a batch of waste material, may automatically facilitate transportation of a batch of waste material to a destination, or a combination of two or more of these.

The waste material processing facility or another system may create a smart contract using a blockchain process. Use of a blockchain process may improve security of sales transactions.

Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier for execution by, or to control the operation of, data processing apparatus.

Computers suitable for the execution of a computer program include, by way of example, general or special purpose microprocessors or both, or any other kind of central processing unit. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a smart phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, e.g., a universal serial bus (USB) flash drive, to name just a few.

To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., LCD (liquid crystal display), OLED (organic light emitting diode) or other monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.

In some embodiments, a server transmits data, e.g., an HyperText Markup Language (HTML) page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the user device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received from the user device at the server.

An example of one such type of computer is shown in <FIG>, which shows a schematic diagram of a generic computer system <NUM>. The system <NUM> can be used for the operations described in association with any of the computer-implemented methods described previously, according to one implementation. The system <NUM> includes a processor <NUM>, a memory <NUM>, a storage device <NUM>, and an input/output device <NUM>. Each of the components <NUM>, <NUM>, <NUM>, and <NUM> are interconnected using a system bus <NUM>. The processor <NUM> is capable of processing instructions for execution within the system <NUM>. In one implementation, the processor <NUM> is a single-threaded processor. In another implementation, the processor <NUM> is a multi-threaded processor. The processor <NUM> is capable of processing instructions stored in the memory <NUM> or on the storage device <NUM> to display graphical information for a user interface on the input/output device <NUM>.

Claim 1:
A system (<NUM>) comprising one or more computers (<NUM>) and one or more storage devices (<NUM>) on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving, from a near infrared spectrometer (<NUM>), molecular content data that indicates a molecular content of a portion of a batch of sludge;
determining, by a machine learning module (<NUM>) included in the system using the molecular content data and multiple parameters of the machine learning module, a predicted value for a property of the portion of the batch of sludge;
determining, using the predicted value and current weather conditions at at least one potential destination and/or predicted weather conditions at the at least one potential destination, a recommended potential use for the batch of sludge; and
providing the predicted value for the property of the portion of the batch of sludge.