Advanced intelligent water quality analysis system for detection of rare earth minerals and elements

Abstract

A computer-implemented system can be programmed for detecting rare earth metals and elements (REMEs) in a fluid flow. The system may include a suite of sensors configured to generate signals indicative of different attributes of the fluid flow. A data analysis module of the system is programmed for receiving and processing signal data from the sensor. In addition, a machine learning module of the system is programmed for executing machine learning algorithms for predicting a presence of at least one REME in response to the determined attribute of the fluid flow.

Description

ADVANCED INTELLIGENT WATER QUALITY ANALYSIS SYSTEM (IWQAS) FOR DETECTION OF RARE EARTH MINERALS AND ELEMENTS CROSS-REFERENCE TO RELATED APPLICATION / PRIORITY CLAIM

[0001] The present application claims priority to United States Provisional Patent Application Serial No.63 / 562,740, filed on March 8, 2024, the entirety of which is incorporated by reference herein. FIELD OF THE INVENTION

[0002] Various embodiments of the present invention generally relate to tools, techniques, and systems for detecting the presence of rare earth metals and elements in different types of water sources. BRIEF DESCRIPTION OF THE FIGURES

[0003] FIG.1 illustrates one example of a an intelligent water quality analysis system (IQWAS) structured in accordance with certain embodiments of the present invention.

[0004] FIG.2 includes one example of data output obtained while evaluating a fluid flow using certain components of the IQWAS system.

[0005] FIG.3 includes another example of data output obtained while evaluating a fluid flow using certain components of the IQWAS system. DESCRIPTION

[0006] In various embodiments, the present disclosure describes enhanced tools, techniques, and computer systems for detection of rare earth metals in groundwater and production fluid water by leveraging machine learning algorithms in conjunction with water quality analytics. By analyzing data for parameters such as pH level, conductivity, turbidity, condensate, total dissolved solids (TDS), pressure, flow rate, and / or other chemical markers, the system can be programmed to predict the presence of rare earth metals with high accuracy.

[0007] FIG.1 illustrates one example of an intelligent water quality analysis system (IQWAS) 102 structured in accordance with certain embodiments of the present invention. The system 102 can include an innovative machine learning module 104, which is trained on extensive datasets to identify patterns and correlations between the presence of rare earth metals or elements and specific water quality indicators and / or other water characteristics or attributesas detected by a variety of sensors 106, as shown. This system 102 with its associated processing offers a significant improvement over traditional detection techniques, which are often labor intensive, costly, and less efficient. One or more computer processors 108 (e.g., computer servers) can be configured or programmed to direct or execute various tasks performed within the system 102. Additionally, the ability of the system 102 to rapidly process and analyze data, such as by the processing of a data analysis module 110, can enable real-time monitoring and detection, thereby facilitating more effective management and extraction of valuable resources detected in a flow 112 of fluid or liquid. This technology can be used for enhancing the exploration and extraction processes of rare earth metals, contributing to more sustainable and efficient practices in the mining industry, for example, and in other industries.

[0008] In this example, the flow 112 comprises liquid or other fluids which can be exchanged between a storage vehicle 114 and a storage tank 116, such as by means of appropriate valves, piping, and / or other fluid communication mechanisms. Examples of suitable valves, piping, and other fluid communication mechanisms can be found in United States Patent Application Serial No.18 / 642,548, filed on April 22, 2024, entitled, “Enhanced Pipe Assembly,” and which is incorporated herein by reference. Alternatively, the flow 112 may be analyzed by the system 102 either as it moves in the direction from the storage tank 116 to the storage vehicle 114, or vice versa, or possibly in both directions.

[0009] The IWQAS system 102 is an innovative solution designed to harness the capabilities of machine learning (ML) in tandem with advanced water quality instrumentation. One objective of IWQAS is to revolutionize the detection and analysis of rare earth minerals and elements (REMEs) in water bodies. By offering a method that surpasses the efficiency, accuracy, and scalability of traditional detection techniques, IWQAS 102 aims to fulfill the growing demand for these critical resources in various high-technology applications while also addressing environmental and exploration challenges.

[0010] The inventor has appreciated that rare earth minerals and elements are indispensable in the manufacture of a myriad of high-tech devices, from renewable energy systems to electronic devices and beyond. The exploration and environmental monitoring of these elements, however, pose significant challenges due to their dispersed nature and the complexities involved in their detection. Traditional methods, while effective to a degree, suffer from limitations such as high costs, lengthy processing times, and the need for specialized personnel. Many current methods for testing for REME’s require extensive lab and / or destructive testing. The IWQAS 102 seeks to mitigate these issues by integrating ML algorithms with state-of-the-art sensor technology to provide a more refined, automated, and precisedetection system.

[0011] The IWQAS system 102 couples the precision of advanced water quality sensors with the analytical power of machine learning algorithms. This synergy facilitates the accurate identification and quantification of REMEs in water samples, offering a significant improvement over conventional methods. The system 102 may be comprised of primary components, such advanced water quality instrumentation including the sensors 106, the machine learning module 104, a data analysis module 110, and a user interface module 118

[0012] Advanced Water Quality Instrumentation. This component comprises a comprehensive suite of sensors 106, each optimized for the detection of specific water quality parameters that are indicative of the presence of REMEs.

[0013] Machine Learning Module. At the core of the IWQAS system 102 is a robust machine learning (ML) framework that processes and analyzes sensor data to detect the unique signatures of REMEs, employing both supervised and unsupervised learning techniques for continual improvement.

[0014] Data Analysis and Reporting Interface. A sophisticated interface that provides intuitive access to data analysis results and output of the system 102, featuring advanced visualization tools and detailed reporting capabilities for various stakeholders.

[0015] The sensor 106 suite at the heart of IWQAS 102 is designed to capture a wide range of chemical and physical parameters. These include, but are not limited to, pH levels, conductivity, turbidity, and / or specific absorbance values that may indicate the presence of REMEs. The selection of sensors 106 can be based on promoting sensitivity to low concentrations of target elements, allowing for the detection of REMEs even in dilute solutions. Furthermore, the IWQAS 102 can incorporate cutting-edge technologies such as nano-material based sensors and spectroscopy for enhanced detection capabilities. This modular sensor 106 array can be customized based on specific exploration or monitoring needs, facilitating the addition of sensors 106 for new elements as required.

[0016] The ML module 104 is designed to intelligently analyze the complex datasets generated by the sensor 106 suite. Initially trained on a vast array of historical and synthetic water quality data, the module 104 employs a variety of algorithms, including neural networks, decision trees, and clustering techniques, to identify patterns and signatures characteristic of REMEs. This training enables the system to distinguish between natural background levels and potential REME anomalies with high precision. The module 104 can leverage its adaptive learning capability, which allows it to refine its algorithms based on new data, improving its accuracy and reliability over time. This self-improving mechanism ensures that IWQAS 102remains at the forefront of detection technology, capable of adapting to new challenges and discoveries in the field of REME exploration. One or more data storage media 120 of the system 102 can be configured to store input data, analysis output report data, training set data, and / or a variety of other types of data that may be processed by the system 102.

[0017] The user interface of IWQAS 102 is designed to be both powerful and user- friendly, catering to experts and non-specialists alike. It provides real-time access to data analysis, highlighting key findings such as the presence and concentration of REMEs. The interface offers a range of visualization tools, including geographic information system (GIS) mapping for spatial analysis, trend graphs for temporal changes, and heat maps for concentration visualization. The system 102 can also include a report module 122 equipped with customizable reporting tools that can generate detailed reports tailored to the needs of different users, such as environmental agencies, mining companies, and research institutions, for example. These reports can include comprehensive data analysis, methodological explanations, and recommendations for further action, for example, among other types of reports.

[0018] The IWQAS system 102 provides a basis for detection and analysis of rare earth minerals and elements in water and other fluids, integrating advanced water quality sensors with machine learning algorithms for unparalleled accuracy and efficiency. It can employ a multi- sensor array, including nano-material based sensors and spectroscopic technology, specifically tailored for the detection of REMEs. Its adaptive machine learning framework is capable of processing complex sensor data, employing a combination of algorithmic approaches to continually refine detection accuracy. The system 102 also possesses comprehensive data analysis module and a reporting interface offering advanced visualization tools, GIS mapping, and customizable reporting for diverse user needs.

[0019] FIGS.2 and 3 include examples of experimental data obtained while testing flows using in-line water quality equipment in connection with the system 102. The examples include data derived from live analytics associated with analyzing production brine off-loaded from oil and gas water trucks, for example. Data may be derived while offloading or onboarding fluids, such as between a vehicle 114 and a storage tank 116, as described above. Various types of data indicative of signals generated by the sensors 106 can be captured and displayed, as shown. In certain embodiments, alerts may be generated by an alert module 124 and communicated to a user in response to a level of a characteristic or criterion meeting, exceeding, or not exceeding a predetermined threshold level. Such alerts may be useful for indicting the presence (or absence) of various types of REMEs in a fluid flow 112 analyzed by the system 102.

[0020] The IWQAS 102 represents a groundbreaking advancement in the detection ofrare earth minerals and elements in water, providing a highly accurate, efficient, and scalable solution that addresses the limitations of traditional methods. Through its innovative integration of machine learning and advanced sensor technology, IWQAS 102 offers significant benefits for environmental monitoring, resource exploration, and the broader field of water quality analysis. This invention sets a new standard for REME detection, promising to accelerate discovery and protect environmental resources with unprecedented precision. The integration of water quality instrumentation with machine learning for the detection of REMEs in water and other types of fluids is a cutting-edge area of research that combines analytical chemistry, environmental science, and computational analysis.

[0021] The examples presented herein are intended to illustrate potential and specific implementations of the present invention. It can be appreciated that the examples are intended primarily for purposes of illustration of the invention for those skilled in the art. No particular aspect or aspects of the examples are necessarily intended to limit the scope of the present invention. For example, no particular aspect or aspects of the examples of system architectures, configurations, data definitions, or process flows described herein are necessarily intended to limit the scope of the invention, unless such aspects are specifically claimed as such.

[0022] Any element expressed herein as a means for performing a specified function is intended to encompass any way of performing that function including, for example, a combination of elements that performs that function. Furthermore, the invention, as may be defined by such means-plus-function claims, resides in the fact that the functionalities provided by the various recited means are combined and brought together in a manner as defined by the appended claims. Therefore, any means that can provide such functionalities may be considered equivalents to the means shown herein.

[0023] The processes associated with the present embodiments may be executed by programmable equipment, such as computers. Software or other sets of instructions that may be employed to cause programmable equipment to execute the processes may be stored in any storage device, such as a computer system (non-volatile) memory. Furthermore, some of the processes may be programmed when the computer system is manufactured or via a computer- readable memory storage medium.

[0024] It can also be appreciated that certain process aspects described herein may be performed using instructions stored on a computer-readable memory medium or media that direct a computer or computer system to perform process steps. A computer-readable medium may include, for example, memory devices such as diskettes, compact discs of both read-only and read / write varieties, optical disk drives, and hard disk drives. A computer-readable mediummay also include memory storage that may be physical, virtual, permanent, temporary, semi- permanent and / or semi-temporary. Memory and / or storage components may be implemented using any computer-readable media capable of storing data such as volatile or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. Examples of computer-readable storage media may include, without limitation, digital video recorders (DVR), RAM, dynamic RAM (DRAM), Double-Data- Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), read-only memory (ROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase-change memory, ovonic memory, ferroelectric memory, silicon-oxide-nitride- oxide-silicon (SONOS) memory, magnetic or optical cards, or any other type of media suitable for storing information.

[0025] A “computer,” “computer system,” “computing apparatus,” “component,” or “computer processor” may be, for example and without limitation, a processor, microcomputer, minicomputer, server, mainframe, laptop, personal data assistant (PDA), wireless e-mail device, smart phone, mobile phone, electronic tablet, cellular phone, pager, fax machine, scanner, or any other programmable device or computer apparatus configured to transmit, process, and / or receive data. Computer systems and computer-based devices disclosed herein may include memory and / or storage components for storing certain software applications used in obtaining, processing, and communicating information. It can be appreciated that such memory may be internal or external with respect to operation of the disclosed embodiments. In various embodiments, a “host,” “engine,” “loader,” “filter,” “platform,” or “component” may include various computers or computer systems, or may include a reasonable combination of software, firmware, and / or hardware. In certain embodiments, a “module” may include software, firmware, hardware, or any reasonable combination thereof.

[0026] In various embodiments of the present invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to perform a given function or functions.

[0027] Various embodiments of the systems and methods described herein may employ one or more electronic computer networks to promote communication among different components, transfer data, or to share resources and information. Such computer networks can be classified according to the hardware and software technology that is used to interconnect the devices in the network, such as optical fiber, Ethernet, wireless LAN, HomePNA, power linecommunication or G.hn. Wireless communications described herein may be conducted with Wi- Fi and Bluetooth enabled networks and devices, among other types of suitable wireless communication protocols. For vehicle systems, networks such as CAN or J1939 may be employed, for example. For V2I (vehicle to infrastructure), or V2X (vehicle to everything) communications, technology such as DSRC or 3GPP may be used, for example. The Controller Area Network (CAN) bus is a serial bus protocol to connect individual systems and sensors as an alternative to conventional multi-wire looms. In certain cases, the CAN bus protocol allows vehicle components to communicate on a single or dual-wire networked data bus. The computer networks may also be embodied as one or more of the following types of networks: local area network (LAN); metropolitan area network (MAN); wide area network (WAN); virtual private network (VPN); storage area network (SAN); or global area network (GAN), among other network varieties.

[0028] For example, a WAN computer network may cover a broad area by linking communications across metropolitan, regional, or national boundaries. The network may use routers and / or public communication links. One type of data communication network may cover a relatively broad geographic area (e.g., city-to-city or country-to-country) which uses transmission facilities provided by common carriers, such as telephone service providers. In another example, a GAN computer network may support mobile communications across multiple wireless LANs or satellite networks. In another example, a VPN computer network may include links between nodes carried by open connections or virtual circuits in another network (e.g., the Internet) instead of by physical wires. The link-layer protocols of the VPN can be tunneled through the other network. One VPN application can promote secure communications through the Internet. The VPN can also be used to separately and securely conduct the traffic of different user communities over an underlying network. The VPN may provide users with the virtual experience of accessing the network through an IP address location other than the actual IP address which connects the wireless device to the network. The computer network may be characterized based on functional relationships among the elements or components of the network, such as active networking, client-server, or peer-to-peer functional architecture. The computer network may be classified according to network topology, such as bus network, star network, ring network, mesh network, star-bus network, or hierarchical topology network, for example. The computer network may also be classified based on the method employed for data communication, such as digital and analog networks.

[0029] Embodiments of the methods and systems described herein may employ internetworking for connecting two or more distinct electronic computer networks or networksegments through a common routing technology. The type of internetwork employed may depend on administration and / or participation in the internetwork. Non-limiting examples of internetworks include intranet, extranet, and Internet. Intranets and extranets may or may not have connections to the Internet. If connected to the Internet, the intranet or extranet may be protected with appropriate authentication technology or other security measures. As applied herein, an intranet can be a group of networks which employ Internet Protocol, web browsers and / or file transfer applications, under common control by an administrative entity. Such an administrative entity could restrict access to the intranet to only authorized users, for example, or another internal network of an organization or commercial entity. As applied herein, an extranet may include a network or internetwork generally limited to a primary organization or entity, but which also has limited connections to the networks of one or more other trusted organizations or entities (e.g., customers of an entity may be given access an intranet of the entity thereby creating an extranet).

[0030] Computer networks may include hardware elements to interconnect network nodes, such as network interface cards (NICs) or Ethernet cards, repeaters, bridges, hubs, switches, routers, and other like components. Such elements may be physically wired for communication and / or data connections may be provided with microwave links (e.g., IEEE 802.12) or fiber optics, for example. A network card, network adapter or NIC can be designed to allow computers to communicate over the computer network by providing physical access to a network and an addressing system through the use of MAC addresses, for example. A repeater can be embodied as an electronic device that receives and retransmits a communicated signal at a boosted power level to allow the signal to cover a telecommunication distance with reduced degradation. A network bridge can be configured to connect multiple network segments at the data link layer of a computer network while learning which addresses can be reached through which specific ports of the network. In the network, the bridge may associate a port with an address and then send traffic for that address only to that port. In various embodiments, local bridges may be employed to directly connect local area networks (LANs); remote bridges can be used to create a wide area network (WAN) link between LANs; and / or, wireless bridges can be used to connect LANs and / or to connect remote stations to LANs.

[0031] Embodiments of the methods and systems described herein may divide functions between separate CPUs, creating a multiprocessing configuration. For example, multiprocessor and multi-core (multiple CPUs on a single integrated circuit) computer systems with co- processing capabilities may be employed. Also, multitasking may be employed as a computer processing technique to handle simultaneous execution of multiple computer programs.

[0032] Although some embodiments may be illustrated and described as comprising functional components, software, engines, and / or modules performing various operations, it can be appreciated that such components or modules may be implemented by one or more hardware components, software components, and / or combination thereof. The functional components, software, engines, and / or modules may be implemented, for example, by logic (e.g., instructions, data, and / or code) to be executed by a logic device (e.g., processor). Such logic may be stored internally or externally to a logic device on one or more types of computer-readable storage media. In other embodiments, the functional components such as software, engines, and / or modules may be implemented by hardware elements that may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth.

[0033] Examples of software, engines, and / or modules may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and / or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[0034] Additionally, it is to be appreciated that the embodiments described herein illustrate example implementations, and that the functional elements, logical blocks, modules, and circuits elements may be implemented in various other ways which are consistent with the described embodiments. Furthermore, the operations performed by such functional elements, logical blocks, modules, and circuits elements may be combined and / or separated for a given implementation and may be performed by a greater number or fewer number of components or modules. Discrete components and features may be readily separated from or combined with the features of any of the other several aspects without departing from the scope of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0035] Unless specifically stated otherwise, it may be appreciated that terms such as“processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, a DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein that manipulates and / or transforms data represented as physical quantities (e.g., electronic) within registers and / or memories into other data similarly represented as physical quantities within the memories, registers or other such information storage, transmission or display devices.

[0036] Certain embodiments may be described using the expression “coupled” and “connected” along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments may be described using the terms “connected” and / or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, also may mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. With respect to software elements, for example, the term “coupled” may refer to interfaces, message interfaces, application program interface (API), exchanging messages, and so forth.

[0037] It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the present disclosure and are comprised within the scope thereof. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles described in the present disclosure and the concepts contributed to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents comprise both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present disclosure, therefore, is not intended to be limited to the exemplary aspects and aspects shown and described herein.

[0038] The flow charts and methods described herein show the functionality and operation of various implementations. If embodied in software, each block, step, or action may represent a module, segment, or portion of code that comprises program instructions to implement the specified logical functions. The program instructions may be embodied in the form of source code that comprises human-readable statements written in a programminglanguage or machine code that comprises numerical instructions recognizable by a suitable execution system such as a processing component in a computer system. If embodied in hardware, each block may represent a circuit or a number of interconnected circuits to implement the specified logical functions.

[0039] Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is comprised in at least one embodiment. The appearances of the phrase “in one embodiment” or “in one aspect” in the specification are not necessarily all referring to the same embodiment. The terms “a” and “an” and “the” and similar referents used in the context of the present disclosure (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as” or “for example”) provided herein is intended merely to better illuminate the disclosed embodiments and does not pose a limitation on the scope otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the claimed subject matter. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as solely, only and the like in connection with the recitation of claim elements, or use of a negative limitation.

[0040] Groupings of alternative elements or embodiments disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. It is anticipated that one or more members of a group may be comprised in, or deleted from, a group for reasons of convenience and / or patentability.

[0041] In various embodiments of the present invention, different types of artificial intelligence tools and techniques can be incorporated and implemented. Search and optimization tools including search algorithms, mathematical optimization, and evolutionary computation methods can be used for intelligently searching through many possible solutions. For example, logical operations can involve searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms can search throughtrees of goals and subgoals, attempting to find a path to a target goal, in a process called means- ends analysis.

[0042] Heuristics can be used to prioritize choices in favor of those more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to eliminate some choices unlikely to lead to a goal. Heuristics can supply a computer system with a best estimate for the path on which the solution lies. Heuristics can limit the search for solutions into a smaller sample size, thereby increasing overall computer system processing efficiency.

[0043] Propositional logic can be used which involves truth functions such as "or" and "not" search terms, and first-order logic can add quantifiers and predicates, and can express facts about objects, their properties, and their relationships with each other. Fuzzy logic assigns a degree of truth (e.g., between 0 and 1) to vague statements which may be too linguistically imprecise to be completely true or false. Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic can be used to address specific domains of knowledge, such as description logics, situation calculus, event calculus and fluent calculus (for representing events and time), causal calculus, belief calculus (belief revision); and modal logics. Logic for modeling contradictory or inconsistent statements arising in multi-agent systems can also be used, such as paraconsistent logics.

[0044] Probabilistic methods can be applied for uncertain reasoning, such as Bayesian networks, hidden Markov models, Kalman filters, particle filters, decision theory, and utility theory. These tools and techniques help the system execute algorithms with incomplete or uncertain information. Bayesian networks are tools that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation- maximization algorithm), planning (using decision networks), and perception (using dynamic Bayesian networks). Probabilistic algorithms can be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Artificial intelligence can use the concept of utility as a measure of how valuable something is to an intelligent agent. Mathematical tools can analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.

[0045] The artificial intelligence techniques applied to embodiments of the invention may leverage classifiers and controllers. Classifiers are functions that use pattern matching todetermine a closest match. They can be tuned according to examples known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class which represents a decision to be made. All of the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is one kind of symbolic machine learning algorithm. The naive Bayes classifier is one kind of classifier useful for its scalability, in particular. Neural networks can also be used for classification. Classifier performance depends in part on the characteristics of the data to be classified, such as the data set size, distribution of samples across classes, dimensionality, and the level of noise. Model-based classifiers perform optimally when the assumed model is an optimized fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is a primary concern, then discriminative classifiers (e.g., SVM) can be used to enhance accuracy.

[0046] A neural network is an interconnected group of nodes which can be used in connection with various embodiments of the invention, such as execution of various methods, processes, or algorithms disclosed herein. Each neuron of the neural network can accept inputs from other neurons, each of which when activated casts a weighted vote for or against whether the first neuron should activate. Learning achieved by the network involves using an algorithm to adjust these weights based on the training data. For example, one algorithm increases the weight between two connected neurons when the activation of one triggers the successful activation of another. Neurons have a continuous spectrum of activation, and neurons can process inputs in a non-linear way rather than weighing straightforward votes. Neural networks can model complex relationships between inputs and outputs or find patterns in data. They can learn continuous functions and even digital logical operations. Neural networks can be viewed as a type of mathematical optimization which performs a gradient descent on a multi-dimensional topology that was created by training the network. Another type of algorithm is a backpropagation algorithm. Other examples of learning techniques for neural networks include Hebbian learning, group method of data handling (GMDH), or competitive learning. The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction), and recurrent neural networks (which allow feedback and short-term memories of previous input events). Examples of feedforward networks include perceptrons, multi-layer perceptrons, and radial basis networks.

[0047] Deep learning techniques applied to various embodiments of the invention can use several layers of neurons between the network's inputs and outputs. The multiple layers canprogressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning may involve convolutional neural networks for many or all of its layers. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. This can substantially reduce the number of weighted connections between neurons. In a recurrent neural network, the signal will propagate through a layer more than once. A recurrent neural network (RNN) is another example of a deep learning technique which can be trained by gradient descent, for example.

[0048] While various embodiments of the invention have been described herein, it should be apparent, however, that various modifications, alterations and adaptations to those embodiments may occur to persons skilled in the art with the attainment of some or all of the advantages of the present invention. The disclosed embodiments are therefore intended to include all such modifications, alterations and adaptations without departing from the scope and spirit of the present invention as described herein.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A computer-implemented system programmed for detecting rare earth metals and elements (REMEs) in a fluid flow, the system comprising: a computer processor programmed to execute at least one function of at least one computer-implemented module of the system; at least one sensor configured to generate a signal indicative of an attribute of the fluid flow; a data analysis module programmed for receiving and processing signal data from the sensor; and a machine learning module programmed for executing at least one machine learning algorithm for predicting a presence of at least one REME in response to the processing of the data analysis module to determine the attribute of the fluid flow.

2. The system of Claim 1, wherein the attribute comprises at least one of pH level, conductivity, turbidity, condensate content, total dissolved solids (TDS) content, pressure, and / or flow rate.

3. The system of Claim 1, wherein the attribute comprises a chemical marker.

4. The system of Claim 1, further comprising wherein the machine learning module is trained on a training data set and programmed to identify a correlation between a presence or absence of at least one REME and at least one processed attribute.

5. The system of Claim 1, wherein the fluid flow comprises a water flow and at least one attribute comprises a water quality indicator.

6. The system of Claim 1, wherein the fluid flow comprises a flow between a storage vehicle 114 and a storage tank.

7. The system of Claim 6, further comprising analyzing the fluid flow in a direction flowing from the storage vehicle to the storage tank.

8. The system of Claim 6, further comprising analyzing the fluid flow in a direction flowing from the storage tank to the storage vehicle.

9. The system of Claim 1, further comprising a suite of sensors each optimized for the detection of a specific water quality parameter indicative of an REME.

10. The system of Claim 1, further comprising the machine learning module programmed for analyzing data received from the sensor to detect a unique REME signature.

11. The system of Claim 1, further comprising the machine learning module programmed for both supervised and unsupervised learning techniques.

12. The system of Claim 1, wherein at least one sensor comprises a nanomaterial based sensor.

13. The system of Claim 1, wherein at least one sensor comprises a spectroscopy based sensor.

14. The system of Claim 1, further comprising the machine learning module trained for distinguishing between a natural background level of REME and a comparatively higher level of REME.

15. The system of Claim 1, further comprising a report module programmed for generated a report in response to a user type.

16. The system of Claim 15, where the user type comprises an environmental agency, a mining company, or a research institution.

17. The system of Claim 1, further comprising a an alert module programmed for generating an alert communication in response to a level of an attribute meeting, exceeding, or not exceeding a predetermined threshold level.

18. The system of Claim 17, wherein the generated alert is generated in response to the presence or absence of a type of REME detected in the fluid flow.

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