Patent Description:
Human population growth and socio-demographic changes are placing increased pressure on natural resources to provide more and different types of food. Proteins are one of the key nutrients for the human diet. Animal-based proteins from meat are an increasingly popular and important source of protein for the human diet, however, the ecological impact of animal farming and meat production is also a significant and growing problem. Alternative protein-based meat substitutes, such as plant protein-based meat substitutes and insect protein-based meat substitutes, offer an alternative food source that seeks to provide or exceed the nutritional benefits of meat.

A significant challenge for alternative protein-based meat substitutes is providing desired aesthetic and physical characteristics (for example, taste, texture, toughness, appearance, and cooking behavior, which are both aesthetic and physical characteristics) that emulate or exceed those of animal-based meat. Controlling the aesthetic characteristics of alternative protein-based meat substitutes is a complex problem impacted by multiple variables including ingredient characteristics (for example, the chemical composition, physical composition and structure, purity, and other characteristics of the ingredients and additives introduced into the process) and process settings (for example, ingredient feed rate, process flow rates, process temperatures, and other control parameters).

<FIG> is a schematic diagram depicting certain aspects of a prior art system <NUM> for producing an alternative protein-based meat substitute product. In system <NUM>, one or more human operator(s) <NUM> generates a recipe <NUM> including ingredients <NUM> to be provided to a wet extrusion process machine <NUM> and process settings <NUM> which are provided to a machine controller <NUM> configured to control the operation of the wet extrusion process machine <NUM>. The wet extrusion process machine <NUM> includes one or more feeders <NUM> which supply one or more respective ingredients <NUM> to an extruder <NUM> which includes one or more rotating screws <NUM> which are disposed and rotatable within a stationary barrel or chamber <NUM> defining a length of a processing path.

The ingredients <NUM> may include, for example, one or more alternative protein powders (for example, plant-based flours and/or insect-based flours), water, and oil, and may also include additives, fillers, processing aids, and the like. The one or more feeders <NUM> introduce the ingredients <NUM> into the extruder <NUM> at various locations along the length of the processing path. The extruder <NUM> mixes and advances the ingredients <NUM> along the length of the processing path, controls the temperature at different locations along the length of the processing path, and extrudes a processed mixture through a die <NUM>. The extruded processed mixture may then undergo one or more post-processing operations <NUM> to configure the processed mixture output from die <NUM> into the final form of a meat substitute product <NUM>.

The process settings <NUM> may include quantities or rates of introduction of the ingredients <NUM>, rotation speed of the one or more screws <NUM>, temperature, pressure, and/or humidity settings at one or more locations along the length of the processing path, and other operational settings of wet extrusion process machine <NUM>. The system <NUM> relies on adjustment input <NUM> from the one or more human operator(s) <NUM> to adjust the ingredients <NUM>, processing settings <NUM>, and post-processing operations <NUM>. Adjustment input <NUM> is based on the operator(s) inspection and evaluation of a product under process at one or more points in the process (for example, product samples taken from the output of die <NUM> or at one or more points in post-processing operations <NUM>) in order to achieve the desired characteristics of the meat substitute product <NUM> and therefore depends on the operator(s) expertise and experience. A method and a system for producing protein-based meat analogues is known from <CIT>. Extrusion systems with in-line measurement systems for food products are known from <CIT>.

Heretofore, achieving desired aesthetic characteristics for alternative protein-based meat substitutes has required reliance on human expertise and experience achieved through costly trial-and-error repetition. This imposes several disadvantages and limitations. For example, because achieving the desired aesthetic characteristics depends on the experience of individual human experts, production is exposed to the risk of the experts' unavailability. Additionally, while machine intelligence control techniques are known to be useful in some contexts, they are highly process-specific and have not been developed to the point of general applicability to process control. Furthermore, such techniques have proven inefficient or ineffective in a number of applications. Even using general forms of such techniques, adapting a process towards optimized and customized food products is a time-consuming proposition and may require weeks and months of trial and error based experiments, with limited changes for and degrees of success. Lack of relevant information on key parameters has been another obstacle to both human expert control and machine intelligence control approaches. A further confounding variable is the presence of compositional complexity such as non-Newtonian and nonlinear fluid behaviors which are exhibited by alternative protein-based meat substitute product materials. Unknown variation in ingredient inputs is a yet another confounding hindrance. There remains a significant unmet need for the unique apparatuses, methods, systems, and techniques disclosed herein.

To address the foregoing and other shortcomings and problems faced in the art, the inventors have developed a number of unique technical solutions including the apparatuses, methods, systems, processes, and techniques disclosed herein. For the purposes of illustrating certain aspects of the same, reference shall now be made to the example embodiments illustrated in the accompanying drawings of the present disclosure.

With reference to <FIG>, there is illustrated a system <NUM> for producing an alternative protein-based meat substitute product <NUM>. System <NUM> includes a wet extrusion process machine <NUM> which may be the same as or similar to the wet extrusion process machine <NUM> of system <NUM>. For example, the wet extrusion process machine <NUM> includes one or more feeders <NUM> which supply one or more respective ingredients <NUM> to an extruder <NUM> which includes one or more rotating screws <NUM> which are disposed and rotatable within a stationary barrel or chamber <NUM> defining a length of a processing path. The ingredients <NUM> may include, for example, one or more alternative protein powders (for example, plant-based flours and/or insect-based flours), water, and oil, and may also include additives, fillers, processing aids, and the like.

The wet extrusion process machine <NUM> is one example of a wet extrusion process machine configured to receive, mix, and convey a plurality of ingredients to an extrusion die <NUM>, the plurality of ingredients including a protein powder, an oil, and water (including water in liquid, vapor, or solid-phase). For example, the one or more feeders <NUM> introduce the ingredients <NUM> into the extruder <NUM> at various locations along the length of the processing path. In certain embodiments, the one or more feeders <NUM> may include one or more powder feeder configured to introduce the protein powder to the extrusion passage, one or more water feeders configured to add the water to the extrusion passage, and one or more oil feeders configured to add the oil to the extrusion passage.

The extruder <NUM> mixes and advances the ingredients <NUM> along the length of the processing path. One or more heating systems and/or cooling systems may be coupled with the screw extruder and configured to selectably heat or cool one or more locations along the length of the extrusion passage effective to control the process temperature at different locations along the length of the processing path, and extrudes a processed mixture through the extrusion die <NUM>. It shall be appreciated that the wet extrusion process machine <NUM> is one but one example of a wet extrusion machine configured to receive, mix, and convey a plurality of ingredients to an extrusion die and that a number of alternatives and variations are contemplated as will occur to one of skill in the art with the benefit of the present disclosure. It shall be further appreciated that a variety of wet extrusion machines and wet extrusion processes may be utilized in embodiments according to the present disclosure.

A number of embodiments according to the present disclosure comprise various types of macro-scale wet extrusion machines and processes, for example, single-screw extruders, twin-screw extruders, higher-order multi-screw extruders, kneaders, kneading-extruders, counter-rotating extruders, co-rotating extruders, and other types of macro-scale extruder machines and processes. Additionally or alternatively, the wet extrusion machines and processes may comprise micro-scale extrusion, for example, via filament deposition, fused filament fabrication, fused filament modeling, or other 3D printing or micro-scale material extrusion techniques.

Wet extrusion machines and processes according to the present disclosure may respectively perform and comprise a number of acts. Such acts may include blending or mixing one or more dry ingredients and one or more liquid ingredients to form a blend or mixture (sometimes referred to as a dough), processing the dough to denature proteins and orient protein fibers, and fixation or setting of a fibrous structure. Processing the dough to denature proteins and orient protein fibers may comprise application of mechanical force to the dough, for example, by agitating, beating, confluence flowing, friction application, impingement, kneading, pressurizing, shaking, spinning, turbulence application, wave application, or combinations of these and/or other applications of mechanical force. Processing the dough to denature proteins and orient protein fibers may additionally or alternatively be performed by application of chemical reagents, radiant energy, electromagnetic energy, and/or thermal energy. The chemical reagents may include pH adjusting agents, kosmotropic agents, chaotropic agents, gypsum, salts, surfactants, emulsifiers, fatty acids, amino acids, enzymes, or combinations of these and/or other chemical components. Fixation or setting of a fibrous structure may comprise applying temperature changes, pressure changes, dehydration, redox reactions, chemical fixation, and/or other fixation operations.

System <NUM> further includes an electronic process control system (EPCS) <NUM> which is one example of an EPCS configured to control a wet extrusion machine using a plurality of process settings effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die. For example, the EPCS <NUM> is configured to control the wet extrusion machine <NUM> using a plurality of process settings <NUM> effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die <NUM>. The process settings <NUM> may include quantities or rates of introduction of the ingredients <NUM>, rotation speed of the one or more screws <NUM>, temperature settings at one or more locations along the length of the processing path, pressure settings at one or more locations along the length of the processing path, and other operational settings of wet extrusion process machine <NUM>.

The EPCS is further configured to control automated post-processing equipment <NUM> using a plurality of post-process settings <NUM>. The automated post-processing equipment <NUM> is configured to further process the die mixture which output from the extrusion die <NUM> into the final form of a meat substitute product <NUM>, for example, by cutting, shredding, tearing, ripping, rolling or other post-processing techniques.

The EPCS further includes one or more sensing subsystems <NUM> which sense and provide feedback parameters to a supervisory machine intelligence control system (SMICS). The feedback parameters may include parameters from sensors associated with the wet extrusion machine <NUM> and/or the automated post-processing equipment <NUM>. The sensors may be configured to sense and provide feedback parameters associated with operation of and/or the material being processed by the wet extrusion machine <NUM> and/or automated post-processing equipment <NUM>. In certain forms, sensing subsystems <NUM> may include one or more of the sensing subsystems and/or sensors described below in connection with <FIG>, a combination of two or more of the sensing subsystems and/or sensors described below in connection with <FIG>, and, additionally or alternatively, other forms and types of sensing subsystems and/or sensors.

The system <NUM> relies on one or more machine intelligence components of the SMICS <NUM> to determine, provide, and adjust or modify the ingredients <NUM>, process settings <NUM>, post-process setting <NUM> utilized by EPCS <NUM> in order to achieve the desired characteristics of the meat substitute product <NUM>. The ingredient settings <NUM> provided by SMICS <NUM> to EPCS <NUM> may include quantitative and qualitative ingredient specifications for a plurality of ingredients, for example, one or more alternative protein powders (for example, plant-based flours, proteins derived from microorganism fermentation, and/or insect-based flours), water, and oil, and may also include additives, fillers, processing aids, and the like.

The process settings <NUM> provided by SMICS <NUM> to EPCS <NUM> may include quantities or rates of introduction of the ingredients <NUM>, rotation speed of the one or more screws <NUM>, temperature, pressure, and/or humidity settings at one or more locations along the length of the processing path, and other operational settings of wet extrusion process machine <NUM>. The post-process settings <NUM> provided by SMICS <NUM> to EPCS <NUM> may include force, magnitude, frequency, and other control parameters associated with cutting, shredding, tearing, ripping, rolling or other post-processing techniques may be performed by automated post-processing equipment <NUM>.

A human operator <NUM> can provide input such as available ingredients and desired product data to SMICS <NUM>, although it is also contemplated that such inputs may be provided in an automated, or semi-automated manner. The desired product data input may include a number of parameters associated with a desired intermediate or final product, including, for example, digital images of known mixtures at or proximate the input of an extrusion die comparable to extrusion die <NUM>, at or proximate the output of an extrusion die comparable to extrusion die <NUM> or at one or more additional or alternate locations relative to such an extrusion die. The desired product data input may additionally or alternatively include a number of physical fibrosity parameters determined by processing digital images of such known mixtures, including, for example, physical fibrosity parameters such as a fiber size metric, a fiber orientation metric, a fiber alignment metric, a fiber entanglement metric, inter-fiber distance metric, a torsion force metric, a density metric, a fiber bubble metric or combinations thereof.

It shall be appreciated that the aforementioned physical fibrosity parameters may be defined in a number of manners. For example, the fiber size metric may include one or more of an average fiber diameter, an average fiber length, a fiber diameter distribution or variance, a fiber length distribution or variance, a quartile, quintile, decile or other range metrics of fiber diameter and/or length or various other size metrics as would occur to one of skill in the art with the benefit of the present disclosure. The fiber orientation metric may include, for example, an orientation uniformity index ranging from <NUM> (indicating a group of fibers with a random or pseudo-random orientation relative to an orientation reference) to <NUM> (indicating a group of fibers with orientations that are substantially or completely uniform relative to the orientation reference). The fiber alignment metric may include, for example, an orientation uniformity index ranging from <NUM> (indicating a group of fibers with random or pseudo-random alignments relative to one another) to <NUM> (indicating a group of fibers with alignments that are substantially or completely uniform relative to one another). The fiber entanglement metric may include, for example, a number of fiber crossings per unit area of a digital image. The inter-fiber distance metric may include an average distance between adjacent fibers. The density metric may be calculated or derived using one or more of the foregoing metrics, for example, using a ratio of fiber to inter-fiber distance, and one or more coefficients corresponding to a fiber size metric, a fiber orientation metric, a fiber alignment metric, a fiber entanglement metric, and/or a fiber bubble metric. It shall be further appreciated that the aforementioned averages may include mean averages, median averages, mode averages, weighted averages, or variations thereof. A fiber bubble metric may indicate the presence, degree, and characteristics of air or gas bubble formation within the fibers or in the inter-fiber matrix, for example, a bubble count per unit area or unit volume, and average bubble size (e.g., diameter, radius, volume, etc.), and/or a bubble frequency. It shall be appreciated that for some purposes, the fiber bubble metric may be considered a form of or may be correlated with a fiber density metric.

In general, the SMICS <NUM> is structured to perform certain operations and to receive and interpret signals from any component and/or sensor of the system <NUM> with which it is in operative communication, either directly or indirectly. It shall be appreciated that the SMICS <NUM> may be provided in a variety of forms and configurations including one or more computing devices forming a whole or a part of a processing subsystem having non-transitory memory storing computer-executable instructions, processing, and communication hardware. The SMICS <NUM> may be a single device or a distributed device, and the functions of the SMICS <NUM> may be performed by hardware or software. The SMICS <NUM> is in communication with any actuators, sensors, datalinks, computing devices, wireless connections, or other devices to be able to perform any described operations. The SMICS <NUM> may include one or more non-transitory memory devices configured to store instructions in memory which are readable and executable by the SMICS <NUM> to control operation of system <NUM> as described herein.

Certain operations described herein include operations to determine one or more described parameters. SMICS <NUM> may be configured to determine and may perform acts of determining in a number of manners, for example, by calculating or computing a value, using statistical techniques, obtaining a value from a lookup table or using a lookup operation, receiving values from a datalink or network communication, receiving an electronic signal indicative of the value, receiving a parameter indicative of the value, reading the value from a memory location on a computer-readable medium, receiving the value as a run-time parameter, and/or by receiving a value by which the interpreted parameter can be calculated, and/or by referencing a default value that is interpreted to be the parameter value.

The SMICS <NUM> includes one or more machine intelligence components <NUM> which may be configured to perform a number of machine intelligence techniques to automatically adjust or modify the ingredients <NUM>, process settings <NUM>, post-process setting <NUM> utilized by EPCS <NUM> in response to feedback information from the one or more sensing subsystems <NUM>. For example, the machine intelligence component <NUM> may be configured to utilized a machine learning technique such as one or more of the techniques described herein.

The SMICS <NUM> may utilize a deep learning or deep structured learning technique in which the one or more machine intelligence components <NUM> utilize an artificial neural network (ANN) with multiple layers between the input layer and the output layer. For example, the ANN may be configured with a multi-layer credit assignment path (CAP) which defines the neural network chain of transformations from the input layer to the output layer.

The SMICS <NUM> may utilize a supervised or semi-supervised learning technique in which the one or more machine intelligence components <NUM> are provided with example inputs and their desired outputs, and a defined goal of generating one or more rules that map inputs to outputs. The example inputs, desired outputs, and defined goal may be input by a user and/or at least in part acquired by the one or more machine intelligence components <NUM> during operation of the system <NUM>.

The SMICS <NUM> may utilize a reinforcement learning technique in which the one or more machine intelligence components <NUM> interacts with a dynamic process environment over time in which it must perform a defined goal, for example, producing or duplicating desired product data input to SMICS <NUM>. In such embodiment, as the one or more machine intelligence components <NUM> repeatedly navigates a problem space, it is provided with feedback from sensing subsystems and/or an operator or trainer which is utilized as a reward that the one or more machine intelligence components <NUM> seeks to maximizes.

In other embodiments, the SMICS <NUM> may additionally or alternatively utilize other machine learning techniques such as unsupervised learning wherein no labels are given to the one or more machine intelligence components <NUM>, leaving them on their own to find structure in its input. Further embodiments may utilize other machine learning techniques such as topic modeling, dimensionality reduction, or meta learning.

As further described herein, the SMICS <NUM> may utilize the foregoing machine learning techniques in a learning or training mode wherein the SMICS <NUM> generates, maintains, and/or updates one or more models to establish a correlation between one or more of the process feedback inputs disclosed herein and one or more of the process control parameters or settings disclosed herein. The SMICS <NUM> may additionally or alternatively utilize the foregoing machine learning techniques in a control or supervisory mode to control the aesthetic characteristics of a produced meat substitute product. The SMICS <NUM> may additionally or alternatively utilize the foregoing machine learning techniques in a product development mode to identify and characterize recipes for a produced meat substitute product including ingredient parameters and process setting parameters.

With reference to <FIG>, there is illustrated a schematic diagram depicting certain aspects of an example implementation <NUM> of the system <NUM> including certain aspects of the sensing subsystems <NUM> and the SMICS <NUM>. The implementation <NUM> includes a direct fibrosity measurement (DFM) subsystem <NUM> and an indirect fibrosity measurement (IFM) subsystem <NUM> which are operatively coupled with the SMICS <NUM>. In the implementation <NUM>, the machine intelligence component <NUM> of SMICS <NUM> is configured to implement a machine learning process (MLP) <NUM> which may be configured to utilize one or more of the machine learning techniques described above in connection with <FIG>. The DFM subsystem <NUM> and IFM subsystem <NUM> are each configured to provide one or more inputs to the MLP <NUM>. It shall be appreciated that some embodiments may include only one of the DFM subsystem <NUM> and IFM subsystem <NUM>. Additionally, some embodiments may include multiple instances of either or both of a DFM subsystem and an IFM subsystem which may be configured to provide one or more inputs to the MLP <NUM>.

The DFM subsystem <NUM> is configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture. To this end, the DFM subsystem may include one or more of an optical or other electromagnetic spectrum range sensor system (EM/optical sensor system <NUM>), a mechanical force sensor system <NUM>, and a sonic sensor system <NUM>. It shall be appreciated that some embodiments may include only one of the foregoing sensor systems, while some embodiments may include two or more of the foregoing sensor systems. Additionally, some embodiments may include multiple instances of any one or more of the foregoing sensor systems.

The EM/optical sensor system <NUM> includes one or more optical or other electromagnetic spectrum sensors (EM/optical sensors) <NUM> configured to provide digital images of the extrusion die mixture. It shall be appreciated that EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise a number of sensor types. In some forms, the EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise cameras or other optical sensors adapted to the visible light spectrum, ultraviolet light spectrum, infrared light spectrum, or combinations thereof. In some forms, the EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise or utilize light diffusion sensing systems and sensors adapted to sense directional diffusion of light from a surface. One or more of the EM/optical sensors <NUM> may also be used in combination with spectral filters, polarization filters, and other types of filters. The use of either or both of incoherent optical sensors and systems and coherent sensors and systems (e.g., laser sensors and system) is further contemplated. The use of either or both of monochrome and color imaging is contemplated, for example, color imaging techniques may be utilized on extrudate leaving an extruder die at a point downstream of the die to infer fiber structure in the bulk (fiber orientation, fiber length, and other fiber features such as patterns, fiber bubble metrics, and the other fibrosity metrics disclosed herein).

The EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise or utilize multispectral or hyperspectral sensor or imaging systems such as spatial scanning systems and sensors, spectral scanning systems and sensors, snapshot imaging systems and sensors, spatio-spectral scanning systems and sensors, and/or other types of systems and sensors adapted to sample multiple spectra per unit area such as by sensing of a hyperspectral cube or other multi-dimensional spatio-spectral constructs. EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may additionally or alternatively comprise systems and sensors adapted to operate in non-optical ranges of the electromagnetic spectrum. In some forms, EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise electromagnetic tomography systems and sensors, X-ray systems and sensors, nuclear magnetic resonance systems and sensors, and/or additional types of non-optical spectrum EM/optical sensors and systems.

It shall be further appreciated that digital images of the extrusion die mixture provided by the EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> may comprise a number of forms corresponding to the different forms of the EM/optical sensor system <NUM> and its constituent EM/optical sensors <NUM> contemplated herein, including, for example, conventional digital image matrices or arrays, spectral data, and other data structures.

In forms including one or more sensors adapted to the optical spectrum, such sensors may comprise a charge-coupled device (CCD) array, a complementary metal-oxide-semiconductor CMOS array, and/or other types optical sensors arrays, devices, and elements. The one or more EM/optical sensors <NUM> may be configured with one or more lens systems configured to capture macroscopic images, microscopic images, instances of both, or combinations of both. As described above, the one or more EM/optical sensors <NUM> may be provided in forms configured to detect light in the visible spectrum, infrared spectrum, and/or ultraviolet spectrum. The optical sensor system <NUM> also includes image processing electronics <NUM> which is configured to process raw data from the one or more EM/optical sensors <NUM> into the form of digital images which are provided to MLP <NUM> of SMICS <NUM>.

The one or more EM/optical sensors <NUM> may be positioned and configured to capture images of the extrusion die mixture at an extrusion die location range <NUM>. The extrusion die location range <NUM> may be located in the range extending from a location at or proximate the inlet of the extrusion die <NUM> to a location at or proximate the outlet of the extrusion die <NUM>. Additionally or alternatively one or more EM/optical sensors <NUM> may be positioned and configured to capture images of the extrusion die mixture at a post-processing location range <NUM> which may be any accessible point or location of automated post-processing equipment <NUM>. In such instances, time stamping and time adjustment techniques such as those disclosed herein may be utilized to provide a time adjusted correlation of the measurement location with the extrusion die location allowing measurements at downstream locations to be correlated with conditions at the time the measured material passed through the extrusion die <NUM>.

Depending on the process location(s) at which measurements or readings of the one or more EM/optical sensors <NUM>, time stamping and/or time adaptation techniques may be utilized to temporally correlate the measurements or readings of the one or more EM/optical sensors <NUM> with other sensor measurements or readings or other process parameters. For example, where one or more measurements or readings are taken by EM/optical sensors <NUM> at a post-processing location, such as a point or location of automated post-processing equipment <NUM>, time stamping and/or time adaptation techniques may be utilized to determine a point in time at which the material subject to the sensor measurements or readings was at an earlier production process point or location. As noted above, such techniques may be utilized, for example, to correlate one or more measurements or readings are taken by EM/optical sensors <NUM> with other process measurements such as temperature or moisture at a different process location, such as at or proximate the outlet of the extrusion die <NUM>. Such time stamping and/or time adaptation techniques may account for variation in process rates over time. The same or substantially similar time stamping and alignment techniques may additionally or alternatively be utilized in connection with the other sensor systems and sensors disclosed herein including, for example, the mechanical force sensor system <NUM> including one or more mechanical force sensors <NUM> and/or the sonic sensor system <NUM> including one or more sonic sensors <NUM>.

Some forms contemplate the use of computer-based estimators, observers, soft sensors in addition to one or more physical EM/optical sensors <NUM>. Such computer-based estimators, observers, soft sensors may additionally or alternatively be utilized in connection with the other sensor systems and sensors disclosed herein including, for example, the mechanical force sensor system <NUM> including one or more mechanical force sensors <NUM> and/or the sonic sensor system <NUM> including one or more sonic sensors <NUM>.

The mechanical force sensor system <NUM> includes one or more mechanical force sensors <NUM> which may include strain gauges, force transducers, piezoelectric sensors, piezoresistive sensors, capacitive sensors, elastoresistive sensors, elastography sensors and/or other types of sensors elements configured to sense mechanical force. The one or more mechanical force sensors <NUM> may be configured to measure one or more tensile metrics (for example, tensile strength, dynamic tensile resistance behavior, pulling force, or another tensile force metric), compression metrics (for example, simulated biting force via a tooth emulating sensor arrangement, simulated tactile force via a soft tissue emulating sensor arrangement, compressive force to one or more displacement or deformation criteria, compressive force to failure, or another compression force metric), and/or a metric correlated with mechanical force (for example a density metric) of the extrusion die mixture. The one or more mechanical force sensors <NUM> may comprise one or more elastographic sensor systems or sensors configured for actively mechanically exciting a material under evaluation and dynamically evaluating a sensed response.

One or more of the mechanical force sensor <NUM> may be positioned and configured to selectably contact the extrusion die mixture at an extrusion die location range <NUM>. The extrusion die location range <NUM> may be located in the range extending from a location at or proximate the inlet of the extrusion die <NUM> to a location at or proximate the outlet of the extrusion die <NUM>. Additionally or alternatively one or more sensor elements of the mechanical force sensor <NUM> may be positioned and configured to selectably contact the extrusion die mixture at a post-processing location range <NUM> which may be any accessible point or location of automated post-processing equipment <NUM>. It shall be appreciated that, depending on the particulars of the extrusion machine and extrusion die used in a given embodiment, a proximate location may be considered a location within <NUM> or less, a location within <NUM> or less, or a location within <NUM> or less.

The sonic sensor system <NUM> includes one or more sonic sensors <NUM> which may comprise ultrasound transmitter and receiver or transceiver components such as used in ultrasound imaging systems. The one or more sonic sensors <NUM> may be configured with one or more acoustic waveguide structures to direct sound waves to a measurement target and collect sound waves reflected by a measurement target. The sonic sensor system <NUM> also includes a sonic image processing electronics <NUM> which processes the raw data received from the one or more sonic sensors <NUM> into the form of digital images which are provided to MLP <NUM> of SMICS <NUM>.

The one or more sonic sensors <NUM> may be positioned and configured to direct sound to and detect reflected sound from the extrusion die mixture at an extrusion die location range <NUM>. The extrusion die location range <NUM> may be located in the range extending from a location at or proximate the inlet of the extrusion die <NUM> to a location at or proximate the outlet of the extrusion die <NUM>. Additionally or alternatively one or more sonic sensors <NUM> may be positioned and configured to direct sound to and detect reflected sound from the extrusion die mixture at a post-processing location range <NUM> which may be any accessible point or location of automated post-processing equipment <NUM>.

The IFM subsystem <NUM> includes one or more sensors (for example, sensors S1, S2,. Sn) configured to measure one or more extrusion process parameters. The extrusion process parameters may preferably include one or more of a flow rate of the extrusion die mixture, a flow pressure of the extrusion die mixture, a temperature of the extrusion die mixture, and a moisture content or characteristic of the extrusion die mixture. The extrusion process parameters may additionally or alternatively include higher-order indicators of such extrusion process parameters. One or more motor operating parameter (e.g., motor torque, power consumption, motor currents, and motor voltages) may be correlated with one or more of the foregoing extrusion process parameters, for example, the flow rate or flow pressure of the extrusion die mixture. Pressures and temperatures at one or more locations of the extruder, thermal power loading of one or more temperature control loops, temperature of the cooling die, flowrate and/or temperature change of the cooling fluid in the cooling die heat exchanger, may be correlated with one or more of the foregoing extrusion process parameters, for example, the temperature of the extrusion die mixture. Measurements from the extruder feed including water and oil flow rates as well as the feed rate of the dry feedstock may be correlated with one or more of the foregoing extrusion process parameters, for example, the flow rate of the extrusion die mixture or the moisture content or characteristic of the extrusion die mixture. Measurements of the moisture content of the dry feedstock and/or moisture measurements of the mixture along the processing line may be correlated with one or more of the foregoing extrusion process parameters, for example, the moisture content or characteristic of the extrusion die mixture. Additional online or offline obtained measurements related to the product features such as an analysis indicating the degree of protein denaturation and cross-linking or an analysis of the viscoelastic behavior may also be correlated with one or more of the foregoing extrusion process parameters.

The one or more sensors may be positioned and configured to measure one or more extrusion process parameters at an extrusion die location range <NUM>. The extrusion die location range <NUM> may be located in the range extending from a location at or proximate the inlet of the extrusion die <NUM> to a location at or proximate the outlet of the extrusion die <NUM>. Additionally or alternatively one or more sensors may be positioned and configured to provide sensor readings from any accessible point or location of automated post-processing equipment <NUM>. The IFM subsystem <NUM> also includes an input/output (I/O) and signal processing unit <NUM> which conditions and processes the raw data received from the one or more sensors into a form suitable for use as input to MLP <NUM> of SMICS <NUM>.

It shall be appreciated that the SMICS <NUM> is one example of a supervisory machine intelligence control system operatively coupled with at least one of a direct fibrosity measurement (DFM) subsystem configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture, and an indirect fibrosity measurement (IFM) subsystem configured to measure one or more process parameters associated with the extrusion die mixture, and which is configured to modify one or more of the plurality process settings in response to at least one of the one or more physical fibrosity parameters, and the one or more process parameters, effective to modify the extrusion die mixture and the resulting meats substitute product <NUM> produced therefrom.

In the example implementation <NUM>, the MLP <NUM> is configured to determine one or more process settings adjustments or modifications (PSA) <NUM> and/or one or more ingredient adjustments or modifications (IA) <NUM> in response to at least one of the one or more physical fibrosity parameters received from the DFM subsystem <NUM>, and the one or more extrusion process parameters received from the IFM subsystem <NUM>. The PSA <NUM> and/or IA <NUM> are provided to and utilized by the EPCS <NUM> in performing control operations and are effective to modify the physical and aesthetic characteristic extrusion die mixture and the resulting meats substitute product <NUM> produced therefrom. For example, PSA <NUM> and/or IA <NUM> may be used by the EPCS to adjust or modify the ingredients <NUM>, process settings <NUM>, and/or post-process settings <NUM>.

The MLP <NUM> is one example of a process implemented by a SMICS component to receive a desired product data input from an operator, receive feedback input from at least one of the DFM subsystem and the IFM subsystem, execute a machine-learning algorithm or process to identify one or more control relationships between one or more of the plurality process settings and the desired product data input, and utilize the one or more control relationships to modify one or more of the plurality process settings.

As described above, the MLP <NUM> receives feedback inputs form one of both of DFM subsystem <NUM> and IFM subsystem <NUM>. The feedback inputs received from the DFM subsystem <NUM> may include digital images of the extrusion die mixture. The MLP <NUM> may utilize and treat the digital images themselves as at least one of the physical fibrosity parameters. Additionally, or alternatively, the MLP <NUM> or another processing component of the SMICS may be configured to further process the digital images to determine one or more of the physical fibrosity parameters from the images of the extrusion die mixture. Such further processing of the images of the extrusion die mixture to determine physical fibrosity parameters may include processing to determine one or more of a fiber size metric, a fiber orientation metric, a fiber alignment metric, a fiber entanglement metric, an inter-fiber distance metric, a torsion force metric, a density metric which (which may be may be calculated or derived from one or more of the foregoing metrics), and a metric indicating the presence, degree, and characteristics of air or gas bubble formation within the fibers or in the inter-fiber matrix, for example, a bubble count per unit area or unit volume, and average bubble size (e.g., diameter, radius, volume, etc.), and/or a bubble frequency. Such metrics may be defined, for example, in accordance with the examples described in connection with <FIG>.

The MLP <NUM> may also receive feedback inputs from other process sensors (OPS) <NUM> which may be, for example, ingredient moisture or humidity sensors, ingredient feed rate sensors, process flow rate sensors, process flow pressure sensors, process temperature sensors, process humidity or moisture sensors, and other types of sensors provided to measure other aspects of the process performed by system <NUM>. The MLP <NUM> may utilize the inputs from the DFM subsystem <NUM> and/or the IFM subsystem <NUM> as well as the inputs from OPS <NUM> in its machine learning process. In certain embodiments, the inputs from DFM subsystem <NUM> and/or IFM subsystem <NUM> are preferably prioritized or weighted over other inputs to guide or constrain the machine learning process performed by the MLP <NUM>. In certain preferred embodiments, this is believed to advantageously accelerate and improve the efficacy of the machine learning process performed by MLP <NUM>.

The MLP <NUM> may utilize a combination of the inputs from the DFM subsystem <NUM> and/or the IFM subsystem <NUM> as well as a variety of inputs from OPS <NUM>. The inputs from OPS <NUM> may include motor operating parameters (e.g., motor torque, power consumption, motor currents, and motor voltages), pressure and temperature measurements from different locations of the extruder, thermal power loading of the temperature control loops, temperature measurements from the cooling die, flowrate and temperature change of the cooling fluid in the cooling die heat exchanger, measurements from the extruder feed including water and oil flow rates as well as the feed rate of the dry feedstock and when available an indication of the moisture content of the dry feedstock, moisture measurements of the mixture along the processing line, online or offline obtained measurements related to the product features such as an analysis indicating the degree of protein denaturation and cross-linking or an analysis of the viscoelastic behavior, and/or other inputs from OPS <NUM>.

The MLP <NUM> may utilize to generate, maintain, and update control models or other control components for a number of purposes. In certain embodiments, the control models or other control components associated with MLP <NUM> may detect deviations and abnormalities in the operating state of the process from a nominal operating state, establish a root cause for detected deviations and abnormalities, provide control actions to return the process to the nominal operating state (either executing directly or suggestion to an operator, determine a new operating state, which is more desirable than the current operating state based on criteria including but not limited to a higher production rate, improved product quality, improved operational stability, and provide control actions to transition the operating state from one state to another state while satisfying conditions including, for example, a minimum time for transition or a minimum amount of off-spec product.

In certain embodiments, the control models or other control components associated with MLP <NUM> may utilize mathematical models relating the degree of protein denaturation and cooking/cross-linking to the residence time of the processed mixture and the thermal energy input to the extruder as well as estimated temperature levels within the extruder, utilize mathematical models relating the motor torque and motor power consumption compensated by the feed rate to the viscosity of the processed mixture. In certain embodiments, the control models or other control components associated with MLP <NUM> may utilize mathematical models relating the degree of fiber orientation, fiber length, or product bulk properties such as the presence of bubbles or molten regions to the rate of protein denaturation and an estimated temperature profile in the die determined based on the rate of cooling and process mixture temperature at the die inlet compensated by the process mixture flowrate. In certain embodiments, the control models or other control components associated with MLP <NUM> may utilize a combined mathematical model that includes one or more of the aforementioned mentioned mathematical models and one or more additional models.

With reference to <FIG>, there is illustrated a schematic diagram depicting certain aspects of an example implementation <NUM> of the sensing subsystems <NUM> relative to a plurality of process operations of the system <NUM>. The illustrated process operations of the system <NUM> include process operations P<NUM> through Pn which are examples of process operations performed with an extrusion process machine (for example, extrusion process machine <NUM>). Process operation P<NUM> involves an ingredient addition I<NUM> (for example, an addition of one or more of the ingredients <NUM>) and is controlled by one or more process control inputs C<NUM> which are determined using one or more process settings (for example, one or more of the process settings <NUM>). Process operation P<NUM> is monitored by one or more sensors S1a. S1n which are examples of process sensors configured to provide other process inputs (for example, OPS inputs <NUM>). The one or more sensors S1a. S1n may be configured to provide continuous sensor outputs or discrete sensor outputs. Similarly, the process control inputs C<NUM> may be determined and provided as continuous control inputs or as discrete control inputs.

Process operation Pn involves an ingredient addition In (for example, an addition of one or more of the ingredients <NUM>) and is controlled by one or more process control inputs Cn which may be determined using one or more process settings (for example, an addition of one or more of the process settings <NUM>). Process operation Pn is monitored by one or more sensors Sna. Snn which are examples of process sensors configured to provide other process inputs (for example, OPS inputs <NUM>). As indicated by the notation "n" the implementation <NUM> may further include a plurality of additional process operations which are not illustrated <FIG>. The one or more sensors Sna. Snn may be configured to provide continuous sensor outputs or discrete sensor outputs. Similarly, the process control inputs Cn may be determined and provided as continuous control inputs or as discrete control inputs.

The illustrated process operations of the system <NUM> include post-processing operations Ppp which are examples of process operations performed with automated post-processing equipment (for example, automated post-processing equipment <NUM>). Post-processing operations Ppp are controlled by one or more post-processing control inputs Cpp which are determined using one or more post-process settings (for example, one or more of the post-process settings <NUM>). Post-process operation Ppp is monitored by one or more sensors Sppa. Sppn which are examples of process sensors configured to provide other process inputs (for example, OPS inputs <NUM>). The one or more sensors Sppa. Sppn may be configured to provide continuous sensor outputs or discrete sensor outputs. Similarly, the one or more post-processing control inputs Cpp may be determined and provided as continuous control inputs or as discrete control inputs.

The implementation <NUM> includes one or both of a direct fibrosity measurement (DFM) subsystem and an indirect fibrosity measurement (IFM) subsystem (for example, DFM <NUM> and/or IFM <NUM>) each or both of which may be configured to measure one or more process operations associated with the extrusion die <NUM> or post-processing operations Ppp. The DFM and/or IFM subsystems include the attributes and features and may be configured and implemented in accordance with the DFM <NUM> and/or IFM <NUM>, respectively. Accordingly, it shall be appreciated that implementation <NUM> is one example of an implementation configured to provide inputs from a DFM and/or an IFM as well as inputs from other process sensors to a machine learning component of a supervisory machine intelligence control system (for example, MLP <NUM> of SMICS <NUM>).

It shall be appreciated that the system <NUM>, the implementation <NUM>, and/or the implementation <NUM> may be utilized in performing a number of methods according to the present disclosure. One example method comprises operating the system <NUM> (according to the implementation <NUM>, the implementation <NUM>, and/or other implementations) to produce a meat substitute product (for example, meat substitute product <NUM>). One example method comprises operating the system <NUM> (according to the implementation <NUM>, the implementation <NUM>, and/or other implementations) determine a meat substitute product recipe. One example method comprises operating the system <NUM> (according to the implementation <NUM>, the implementation <NUM>, and/or other implementations) to control or optimize the aesthetic and physical characteristics of a meat substitute product.

With reference to <FIG>, there is illustrated a schematic diagram depicting certain aspects of an example implementation of a control system <NUM> which may be implemented or utilized in connection with the system of <FIG> or another example system for producing alternative protein-based meat substitutes. The control system <NUM> includes on-line controls <NUM> and a controlled system <NUM>. The on-line controls <NUM> include a product feature controller <NUM> and a melt controller <NUM>. The on-line controls <NUM> may comprise some or all of the components of the EPCS <NUM> or another electronic process control system, and may additionally or alternatively comprise some or all of the components of the SMICS <NUM> or other supervisory machine intelligence control system. The controlled system <NUM> includes a material pre-processing, extruder, and extrusion die cooling components <NUM> (sometimes referred to as components <NUM>), which may comprise some or all of the controllable components of the wet extrusion process machine <NUM> or another wet process extrusion machine, and post-processing components <NUM>, which may comprise some or all of the components of automated post-processing equipment <NUM> or other post-processing equipment.

The control system <NUM> is configured in a hierarchical, multi-layer, closed-loop form comprising an inner feedback loop including the melt controller <NUM> and the components <NUM>, and an outer feedback loop including the product feature controller <NUM> and the post-processing components <NUM>. In the inner feedback loop, the melt controller <NUM> provides process control outputs <NUM> to the components <NUM> and process measurement feedbacks <NUM> are provided from the components <NUM> to the melt controller <NUM>. In the outer feedback loop, the product feature controller <NUM> provides melt feature references <NUM> to the melt controller <NUM> and provides post-processing references <NUM> to the post-processing components <NUM>. The product feature controller <NUM> also receives product features references <NUM> which may comprise DPFI <NUM> or other product feature references. The product feature controller <NUM> also receives product feature measurements pertaining to measurements or sensed characteristics of the extrudate <NUM> which passes through the components <NUM> (such measurements or sensed characteristics being possible over a range extending from before an extrudate enters an extrusion die to after the extrudate exits the extrusion die), the post-processed product at one or more points or locations in the post-processing components <NUM>, and/or the ultimate meat substitute product <NUM>.

The melt controller <NUM> is preferably configured to and operable to regulate rheological features of an extrusion process melt, such as viscosity and elastic strain. Control of such melt features may be useful to provide undisturbed operation of the extruder, as well as the quality and features of the end product. Such melt features may be sensitive to and influenced by a number of potential disturbances and variations in the system, such as varying characteristics of the input protein powder, which arises from unavoidable and often unknown variations in naturally sourced product, or different environmental conditions of material preparation and storage. The melt controller <NUM> is therefore configured to avoid situations such as clogging of the extruder, poor physical consistency of extrudate, or poor fiber generation.

The melt controller <NUM> may utilize estimates of melt features obtained from system measurements such as measurements provided by one or more of the sensing subsystems <NUM> or other sensing systems or components. In some forms, such measurements may be collected exclusive or predominantly at one or more preprocessing steps, at the extruder, and at the beginning of an extrusion die rather than of the post-die extrudate. This technique may be preferred in embodiments wherein an extrusion die comprises a largely passive element (only the overall cooling rate being controllable or regulated) which may introduce significant transportation delay. The process control outputs <NUM> may comprise commands, variables, or other control parameters for extrusion system actuators, such as extruder screw speed, input flow, and temperature setpoints of the extrusion zones. In some forms, dynamic aperture control of an extrusion die opening is also contemplated.

It is further contemplated that a multivariable control problem and solution methodology may be determined based on dynamic system models of which may be identified by a machine learning component or model and functional relations between process measurements and melt features which may be identified by a machine learning component or model. Such multivariable control problem and solution methodologies may take a number of forms. For example, if the system is weakly coupled, a classical single input single output control with an appropriate compensation mechanism could be utilized. If system variables are strongly coupled methodologies such as linear quadratic regulator or model predictive control can be utilized.

Further possible details shall now be further described.

One or more components of at least one of the EPCS and the SMICS, may comprise a component of a hierarchical, multi-layer, closed-loop control system.

The hierarchical, multi-layer, closed-loop control system may include an inner feedback loop including a melt controller and an outer feedback loop including a product feature controller.

In the inner feedback loop, the melt controller may provide process control outputs to one or more extrusion system components and may receive process measurement feedbacks from one or more sensing systems associated with the extrusion system.

In the outer feedback loop, the product feature controller may provide melt feature references to the melt controller, and may provide post-processing references to one or more post-processing system components.

The SMICS may receive inputs from both the DFM subsystem and the IFM subsystem and may utilize these inputs in a machine learning process which, in certain forms, may comprise one or more of a deep learning algorithm, a supervised learning algorithm, and a reinforcement learning algorithm.

Claim 1:
A system (<NUM>) comprising:
a wet extrusion process machine (<NUM>) configured to receive, mix, and convey a plurality of ingredients to an extrusion die (<NUM>), the plurality of ingredients including a protein powder, an oil, and water; and
an electronic process control system (EPCS) (<NUM>) configured to control the wet extrusion machine using a plurality of process settings effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die;
characterized in that the system comprises a supervisory machine intelligence control system (SMICS) (<NUM>) operatively coupled with at least one of a direct fibrosity measurement (DFM) subsystem configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture, and an indirect fibrosity measurement (IFM) subsystem configured to measure one or more extrusion process parameters associated with the extrusion die mixture;
wherein the SMICS is configured to modify one or more of the plurality process settings in response to at least one of the one or more physical fibrosity parameters, and the one or more extrusion process parameters.