Optical method and system for monitoring a viscosity of a substance in an industrial process

Abstract

Optical methods and systems for monitoring a viscosity of a substance in an industrial process are provided. A light beam which is at least partially coherent is projected on the substance. A plurality of light acquisitions are performed. Each light acquisition is performed over an exposure period and provides a light scattering data set representative of a scattering of the light beam in an observation volume of the substance during the exposure period. Each light scattering data set includes a speckle pattern. The plurality of light acquisitions are performed at an acquisition frequency. An evolution of a contrast of the speckle pattern over time is monitored. A duration of the exposure period is selected such that the contrast of the speckle pattern has a measurable change within a predetermined viscosity range over which the viscosity is subject to change.

Description

[0001] OPTICAL METHOD AND SYSTEM FOR MONITORING A VISCOSITY OF A SUBSTANCE IN AN INDUSTRIAL PROCESS TECHNICAL FIELD

[0002] The technical field generally relates to methods and systems monitoring the viscosity of a substance in an industrial process during which the viscosity of the substance is subject to change over a predetermined viscosity range.

[0003] BACKGROUND

[0004] Various industrial processes involve substances which are subject to a change in viscosity. For example, in the process of making cheese products or chocolate products, the substance being transformed typically undergo one or more transformations to a state of lower viscosity to one or higher viscosity. In various industrial processes, it is desired to monitor this change in viscosity, as it may provide valuable information for use in carrying out the industrial process. Viscosity or viscoelasticity is however known to be difficult to measure empirically. If available at all, viscosity sensors adapted to a given industrial process are often known to be unreliable and have not, therefore, been adopted widely for monitoring industrial processes.

[0005] In view of the above, there remains a need for a method and system for monitoring the viscosity of a substance in an industrial process.

[0006] SUMMARY

[0007] In accordance with one aspect, there is provided an optical method for monitoring a viscosity of a substance in an industrial process wherein the viscosity is subject to change over a predetermined viscosity range during said industrial process, the method comprising:

[0008] a) projecting a light beam which is at least partially coherent on the substance; b) performing a plurality of light acquisitions, each light acquisition being performed over an exposure period and providing a light scattering data set representative of a scattering of the light beam in an observation volume of the substance during said exposure period, each light scattering data set including a speckle pattern, said plurality of light acquisitions being performed at an acquisition frequency; andc) monitoring an evolution of a contrast of the speckle pattern over time; wherein a duration of the exposure period is selected such that the contrast of the speckle pattern has a measurable change within the predetermined viscosity range.

[0009] In some implementations, the exposure period is further selected such that an associated sigmoid-like curve of the contrast of the speckle pattern as a function of the viscosity of the substance comprises a transitional segment over the predetermined viscosity range. The transitional segment of the sigmoid-like curve may completely or partially extend within the predetermined viscosity range.

[0010] In some implementations, the method further comprises, for each light acquisition, calculating, in real time, a speckle contrast parameter representative of the contrast of the speckle pattern over the exposure period.

[0011] In some implementations, said monitoring the evolution of the speckle contrast parameter comprises monitoring a spatial evolution of the speckle pattern.

[0012] In some implementations, each light scattering data set comprises a 2D image of the speckle pattern in the observation volume.

[0013] In some implementations, the optical method comprises calculating a speckle contrast parameter representative of the contrast of the speckle pattern over the exposure period, said calculating comprises, for each 2D image:

[0014] a. dividing the 2D image into a plurality of tiles;

[0015] b. for each tile, calculating a speckle contrast value; and

[0016] c. calculating an image average of the speckle contrast values for the plurality of tiles of said 2D image.

[0017] In some implementations, calculating a speckle contrast value for each tile comprises subtracting a minimum light intensity on the tile from a maximum intensity on the tile and dividing by an average light intensity for the tile.In some implementations, calculating a speckle contrast value for each tile comprises analysing a histogram of a light intensity distribution over the 2D image.

[0018] In some implementations, said monitoring the evolution of the speckle contrast parameter comprises monitoring a temporal evolution of the speckle pattern over a number of said light acquisitions. Each light scattering data set may comprise a plurality of light intensity values each integrated over an exposure sub-period of the acquisition period for at least one pixel. Calculating the speckle contrast parameter may comprise, for each light acquisition, calculating a temporal evolution of said light intensity value over a number of said sub-periods of the light acquisition for each of the at least one pixel.

[0019] In some implementations, monitoring the evolution of the speckle contrast parameter over time comprises displaying a graphical representation of the speckle contrast parameter over time.

[0020] In some implementations, the optical method further comprises outputting, in real time, the speckle contrast parameter to an industrial system associated to the industrial process.

[0021] In accordance with another aspect, there is provide an optical monitoring system for monitoring a viscosity of a substance in an industrial process wherein the viscosity is subject to change over a predetermined viscosity range during said industrial process, the optical system comprising:

[0022] a coherent light source generating a light beam which is at least partially coherent and configured to project said light beam on the substance, a scattering of the light beam in the substance resulting in the emission of scattered light from the substance;

[0023] a light sensing device configured to detect the scattered light;

[0024] a controller operatively connected to the light detecting device and configured to perform a plurality of light acquisitions, each light acquisition being performed over an exposure period and providing a light scattering data set representative of a scattering of the coherent light beam in an observation volume of the substance during said exposure period, each light scattering data set including a speckle pattern, said plurality of light acquisitions being performed at an acquisition frequency; anda processor configured to monitor an evolution of a contrast of the speckle pattern over time;

[0025] wherein a duration of the exposure period is selected such that the contrast of the speckle pattern has a measurable change within the predetermined viscosity range.

[0026] In some implementations, the coherent light source comprises a laser diode.

[0027] In some implementations, the light sensing device comprises a 2D matrix of pixels and / or a photodiode.

[0028] In some implementations, the exposure period is further selected such that an associated sigmoid-like curve of the contrast of the speckle pattern as a function of the viscosity of the substance comprises a transitional segment over the predetermined viscosity range. The transitional segment of the sigmoid-like curve may completely or partially extend within the predetermined viscosity range.

[0029] Other features and advantages will be better understood upon of reading of detailed embodiments with reference to the appended drawings.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart of a method for monitoring a viscosity of a substance in an industrial process according to one embodiment.

[0031] FIG. 2 is a schematic representation of an optical system for monitoring a viscosity of a substance in an industrial process according to one embodiment.

[0032] FIG. 3 is a graph of the contrast of the speckle pattern as a function of viscosity for different values of exposure period.

[0033] FIG. 4 is a flow chart of a spatial analysis for calculating the speckle contrast parameter according to one variant.FIGs. 5A, 5B and 50 are three examples of 2D images acquired in a substance for different levels of viscosity.

[0034] FIG. 6 shows the zone of an image selected for the purpose of calculating a speckle contrast value; FIG. 6A shows the division of the selected zone into tiles.

[0035] FIG. 7 is a schematic representation of an optical system for monitoring a viscosity of a substance in an industrial process according to another embodiment.

[0036] DETAILED DESCRIPTION

[0037] In the following description, similar features in the drawings have been given similar reference numerals. In order not to unduly encumber the figures, some elements may not be indicated on some figures if they were already mentioned in preceding figures. It should also be understood herein that the elements of the drawings are not necessarily drawn to scale and that the emphasis is instead being placed upon clearly illustrating the elements and structures of the present embodiments.

[0038] The terms “a”, “an” and “one” are defined herein to mean “at least one”, that is, these terms do not exclude a plural number of items, unless stated otherwise. Terms such as “substantially”, “generally” and “about”, that modify a value, condition or characteristic of a feature of an exemplary embodiment, should be understood to mean that the value, condition or characteristic is defined within tolerances that are acceptable for the proper operation of this exemplary embodiment for its intended application.

[0039] Unless stated otherwise, the terms “connected” and “coupled”, and derivatives and variants thereof, refer herein to any structural or functional connection or coupling, either direct or indirect, between two or more elements. For example, the connection or coupling between the elements may be mechanical, optical, electrical, logical, or any combination thereof.

[0040] In the present description, the terms “light” and “optical”, and variants and derivatives thereof, are used to refer to radiation in any appropriate region of the electromagnetic spectrum. The terms “light” and “optical” are therefore not limited to visible light, but can also include, without being limited to, the infrared or ultraviolet regions of theelectromagnetic spectrum. Also, the skilled person will appreciate that the definition of the ultraviolet, visible and infrared ranges in terms of spectral ranges, as well as the dividing lines between them, may vary depending on the technical field or the definitions under consideration, and are not meant to limit the scope of applications of the present techniques.

[0041] To provide a more concise description, some of the quantitative expressions given herein may be qualified with the term "about". It is understood that whether the term "about" is used explicitly or not, every quantity given herein is meant to refer to an actual given value, and it is also meant to refer to the approximation to such given value that would reasonably be inferred based on the ordinary skill in the art, including approximations due to the experimental and / or measurement conditions for such given value.

[0042] In the present description, the term “about” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e. the limitations of the measurement system. It is commonly accepted that a 10% precision measure is acceptable and encompasses the term “about”.

[0043] In the present description, when a broad range of numerical values is provided, any possible narrower range within the boundaries of the broader range is also contemplated. For example, if a broad range of values from 0 to 1000 is provided, any narrower range between 0 and 1000 is also contemplated. If a broad range value of from 0 to 1 is mentioned, any narrower range between 0 and 1, i.e. with decimal value, is also contemplated.

[0044] In accordance with various aspects, there are provided a method and an optical system for monitoring a viscosity of a substance in an industrial process.

[0045] Viscosity may be understood as referring to the measure of the internal resistance to flow of a fluid or fluid-like substance under an applied shear or tensile stress. Typically, viscosity is expressed as a quantitative property that characterizes the degree of molecular friction within the fluid, typically determined under controlled temperature and pressure conditions. It is known in the art to provide viscosity values in units of centipoise (cP) orPascal-seconds (Pa s), as measured using a standardized viscometrical method (e.g., rotational or capillary viscometer) according to recognized industry standards.

[0046] In accordance with one aspect, the method and system of the present description can be used in the context of various industrial processes wherein the viscosity of a fluid or semisolid substance is subject to change over a predetermined viscosity range during the industrial process, without requiring measurement of a viscosity value. As will be explained below, viscosity can be related to the contrast of a speckle pattern observed when probing the substance with a coherent or partially coherent light beam. Monitoring this pattern, as opposed to a viscosity value measured by a viscometer, can provide useful and actionable information to operator of the industrial process.

[0047] It will be readily understood by one skilled in the art that the contrast speckle pattern can also be understood as representative of a viscoelasticity of the substance. A viscoelastic material combines two behaviors: it can deform like a viscous fluid under long-term stress and recover partially or fully like an elastic solid when the stress is removed.

[0048] One example of an industrial process to which the present method and system may be applied is the manufacturing of a cheese or other milk product. Typical cheese manufacturing processes first involve heating a starting milk substance and reducing its pH. A culture of bacteria, referred to in the art as the rennet, is then added, and the milk is stirred for a very short time to ensure that the culture is evenly distributed in the milk. The resulting substance stands still for the curdling process to take place. As part of this process, the enzymatic culture firstly breaks down the envelope of the milk casein which can take a variable amount of time, for example around 5 to 6.5 minutes. This period of time may be referred to in the art as the enzymatic phase, pre-coagulation phase or as the setting period. During the enzymatic phase, the texture of the milk curd undergoes some changes, but these changes are typically minor and do not substantially affect the overall properties of the milk curd. However, at one moment the milk suddenly starts to coagulate, and the viscosity and firmness of the milk curd changes rapidly, marking the onset of a coagulation phase. This transition moment between the end of the enzymatic phase and the onset of the coagulation phase is designated as the set-time of the milk curd. The set-time of the milk curd is often considered an important information for cheesemakers as it is traditionally used as a reference to determine the cut-time, that is,the moment at which the milk curd is cut in order to drain the whey. A correct determination of the set-time is key to determining and suggesting an optimal cut-time, which in turn helps maximize yield, increase the quality and improve the homogeneity of cheese. The method and system described herein may be used to monitor the viscosity of the milk curd over a viscosity range extending between the low viscosity state of the early enzymatic phase, and the firm state of the late coagulation phase.

[0049] In another example, the industrial process may be a manufacturing step of a chocolate product, for example during a step referred to in the art as “conching” of molten chocolate. The viscosity of chocolate in liquid form impacts several aspects of the transformation chain of chocolate such as production, transport, filling, dipping, coating, and dosing. Furthermore, the properties of the final chocolate product are directly related to the viscosity of the chocolate mass from which it has been obtained. It is common practice in the chocolate industry for clients of chocolate manufacturers to specify a target value for the viscosity of the chocolate mass for purchase. The present method and system may therefore be used to monitor the viscosity of the chocolate mass over the typical viscosity range observed over the conching step.

[0050] In yet another example, the industrial process may be a step in the manufacturing of a batter used for making fried products such as potato fries. In such processes, the viscosity of the batter may be an important factor impacting the quality of the final product and the efficacy of its manufacturing. For example, optimally the viscosity of the batter should be high enough to promote adherence of the bater to potatoes, and low enough to avoid formation of clumps negatively affecting the texture of the fries.

[0051] Other non-limitative examples of industrial products in which the viscosity of a substance may be subject to change over a predetermined viscosity range. Several additional example can be found in the food industry, such as other milk products manufacturing such as yogurt or tofu, sauces, etc. Viscosity monitoring may also be of interest in the pharmaceutical industry, beauty industry, petroleum industry, construction industry, etc.

[0052] Referring to FIGs. 1 and 2, embodiments of the optical method 100 described herein and of a corresponding optical monitoring system 20 are schematically illustrated.The method 100 first includes projecting 102 a light beam 22 on the substance 24. The light beam 22 may be embodied by any beam of light exhibiting, at least partially, spatial and / or temporal coherence. As understood by one skilled in the art, a coherent light beam may have a spatial or temporal coherence; spatial coherence refers to a fixed phase relationship between respective electric field values at a same moment in different locations, and temporal coherence a fixed phase relationship between respective electric field values at a same location but at different times. In some implementations, the light beam 22 may be only partially coherent, that is, the fixed phase relationship between respective electric field values may not be constant, but have enough coherence such that a speckle pattern is generated on a sensor observing scattered light from the interaction of the light beam with the substance, as explained further below. In some implementations, the light beam is a laser beam. The light beam 22 may be generated by a light source 21 , for example a coherent light source 21. The coherent light source 21 may for example be embodied by a laser source based on any type of gain medium such as for example a gas laser, a solid-state laser, a fiber laser, a dye laser or a semiconductor laser or laser diode. In some implementations, the coherent light source is embodied by a laser diode, such as for example a Fabry-Perot (FP) laser diode, a Distributed Feedback (DFB laser diode), a Distributed Bragg Reflector (DBR) laser diode, a Quantum Cascade (QCL) laser diode, Vertical Cavity Surface Emitting Lasers (VCSELs) laser diode, or the like. In some variants, the laser diode may be a spatial multimode laser diode. In other variants, the laser diode may be a singlemode laser diode.

[0053] The method 100 next includes performing a plurality of light acquisitions 104. Each light acquisition is performed over an exposure period and provides a light scattering data set 106 representative of the scattering of the light beam 22 in an observation volume of the substance 24 during the exposure period.

[0054] In the configuration shown in FIG. 2, the light beam 22 is projected on the substance 24 at a non-zero incidence angle 0. A fraction of the light from the light beam 22 undergoes specular reflection at the surface 60 of the substance 24 and leads to a reflected light beam (not shown) at a reflection angle equal and opposite to the incidence angle 0. Another fraction of the coherent light beam 22 is transmitted through the surface 60 of the substance 24 and is elastically scattered by the particles inside the substance 24 in close proximity to the point of entry 62 of the light beam 22, defining an observation volume 25.Scattered light then escapes the substance 24 through the surface 60 of the substance 24 in random directions.

[0055] The optical system 20 includes a light sensing device 26 configured to detect a scattered light beam 28, which comprises the light rays of the scattered light having an optical path reaching the light sensing device 26. In the illustrated implementation, the light sensing device 26 is for example embodied by a camera having an array of pixels 27. In other variants (see FIG. 8), the light sensing device may be embodied by a photodiode, as further explained below. The light sensing device 26 is positioned over the substance 24 to detect the scattered light in its field of view, which defines the scattered light beam 28, thereby obtained the light scattering data sets.

[0056] As one skilled in the art will readily understand, each light scattering data set includes a speckle pattern.

[0057] The term “speckle” is used in the art to designate random granular patterns which can be observed upon imaging the projection of a coherent light beam on a rough surface. The appearance of the light reflected by such a surface typically includes variations in intensity, or bright and dark “specks” in the reflected pattern. The speckle effect is a result of the constructive and destructive interference of light waves that take place when at least partially coherent light, such as a laser beam, is used. Speckles are therefore known to arise from the interference of a coherent light beam with itself after it has interacted with an irregular surface or a scattering volume. Theoretically, a perfectly monochromatic, perfectly coherent laser beam forms a wave having a planar wavefront, which would lead to a perfectly uniform image on a light sensor. However, when such a wave hits a scattering medium, either in reflection or transmission, the irregularities of the scattering change the wavefront, resulting in an irregular intensity pattern in the reflected or transmitted light. The random granular patterns in the intensity pattern are known in the art as speckles. A perfectly reflective or perfectly flat surface would not generate speckles. It is known in the art to polished surfaces of mirrors, lenses or microscope slides with variabilities lower than 1 / 1 Oth of the wavelength to avoid speckle generation. A speckle pattern may be observed even from a flat surface if light is allowed to transmit through the surface is then backscattered and transmitted back through the surface (a diffuse reflection).Different parameters of patterns of speckles such as the average intensity, the difference between the most intense and least intense speckles, changes over time, etc. are all indicators of surface roughness. This technique is for example used to measure metal surfaces in the industrial field.

[0058] In the context of the method described herein, the scattered light detected by the light sensing device 26 is not a result of specular reflection of the light beam 22 on an irregular surface, but results from scattering of the coherent light transmitted through the surface 60 into the substance 24 immediately below the surface 60, defining the observation volume 25. The particles inside the observation volume 25 of the substance 24, irrespectively of their nature, disturb the wavefront of the light beam 22 and scatter light waves randomly, some of which travelling towards the surface 60 of the substance 24. The disturbances induced in the wavefront lead to interference patterns on the light sensing device 26, producing speckles. It will be noted that in this context, the speckles are not caused by roughness on the surface 60 of the substance 24: as a matter of fact, in the context of some industrial processes the surface of the substance may be perfectly smooth. The speckles appear because light has penetrated a few millimetres into the substance 24, and it is the return of this disturbed light to the light sensing device 26, coming from different places in the substance 24, that creates the speckles. But just as the roughness of the surface gives the appearance of speckles, here the random volumetric perturbations will also give the appearance of speckles, and the evolution of the scattering during the measurement will ultimately affect the contrast. The Brownian motion of the scatterers in the medium are responsible for affecting the contrast by dynamically changing the speckle pattern. The change of speckle pattern is an indicator of this Brownian motion, which is related to viscous properties of the medium.

[0059] The exposure period T, also referred to as the exposure time, corresponds by definition to the length of time over which photons scattered by the substance 24 are collected by the light sensing device 26 for a given light scattering data set. By way of example, in implementations employing an array of pixels 27 as the light sensing device 26, the exposure period may correspond to the length of time over which each pixel collects photons from the scattered light beam, and the light scattering data set may be embodied by a total photon count value for each pixel, which can be represented as a 2D image. Inother examples of implementation, if the light sensing device includes a photodiode, the exposure time may correspond to the length of time over which the photodiode collects photons from the scattered light beam, and the light scattering data set may be embodied by a variation of the intensity value of the detected light as a function of time over the exposure period.

[0060] The contrast of the observed speckle pattern depends on the viscosity of the substance. The selection of the duration of the exposure period T determines the sensitivity regime of the contrast of the speckle pattern. Referring to FIG. 3, in some implementations, for a given exposure period T the contrast of the observed speckle pattern as a function of the viscosity of the substance can be represented as a sigmoid-like curve 50 transitioning between the contrast values associated with the substance in its most liquid and most solid states during the industrial process, arbitrarily set to 0 and 1 in the illustrated example. In some implementations, the sigmoid-like curve 50 includes an initial flat segment 52, having a contrast value of 0, at the lower end of the viscosity range, a final flat segment 56 having a contrast value of 1, at the higher end of viscosity range, and a continuous transitional segment 54 in between. It will be readily understood that in other variants the sigmoid-like curve may have a higher initial contrast and lower final contrast. In other variants, the contrast may have a behavior as a function of viscosity behaving in a fashion which does not correspond to a sigmoid-like curve. It will be understood that the behavior of the sigmoid-like curve or other profile representing the contrast vs viscosity behavior depends on different factors such as the composition of the substance and the nature of the transformation undergone by the substance during the industrial process.

[0061] In the example of FIG. 3, there are shown three different sigmoid-like curves 50a, 50b and 50c respectively associated with three arbitrarily drawn examples of exposure periods, T=100 .s; T=1ms and T=5ms. During a given observed industrial process, the viscosity of the substance is subject to change over a predetermined viscosity range bound by a minimum value, corresponding to the least viscous, or most liquid, state of the substance during its monitoring, and a maximum value, corresponding the most viscous, or least liquid, state of the substance during the same timeframe. The exposure period T is selected so that the contrast of the speckle pattern has a measurable change within the predetermined viscosity range.The graph of FIG. 3 shows several examples of viscosity ranges, and how they relate to the selection of the exposure period T.

[0062] Viscosity range A corresponds to a low viscosity substance which is initially in a somewhat liquid state and is subject to only a small increase in viscosity during an observed industrial process. In this example, even with an exposure period as small as 100 .s, the contrast of the speckle pattern would be 0 for all viscosity levels within range A. An even shorter exposure period would therefore be required.

[0063] Viscosity range B corresponds to a substance having a relatively high minimum viscosity value which increases to an even higher value during an observed industrial process. If an exposure period of 100 is or 1 ms is selected, then the contrast of the speckle pattern would be 1 for all viscosity levels within range B. Such a selection would therefore not allow the monitoring of a change in the viscosity of the substance. However, a longer exposure period, such as 5 ms in the illustrated example, would cover the change of contrast in the speckle pattern between about 0 and 1. Such an exposure period would therefore lead to a variation in the contrast of the speckle pattern detectable when the viscosity of the substance changes during the industrial process.

[0064] Viscosity range C corresponds to a substance changing between two intermediate values of viscosity during an observed industrial process. In the illustrated example, an exposure time of 100 is would be too short (the contrast value is 1 over all the viscosity values within range C) and an exposure time of 5 ms would be too long (the contrast value is 0 over all the viscosity values within range C). An exposure time of 1 ms would allow the observation of a change from about 0.3 to 1 in the contrast of the speckle pattern during the observed industrial process. It will be noted that in this example, the viscosity range covers only a portion of the transitional segment 54 of the sigmoid-like curve 50b. The minimum viscosity value of the substance during the observed industrial process is higher than the 0-contrast value detectable during this exposure period, which means that the available sensitivity for this exposure period is not fully exploited. An exposure period of 1 ms may still be used, provided that the difference in the contrast associated with the minimum and maximum viscosity values defining viscosity range C is sufficiently detectable with the system used for the monitoring.Finally, viscosity range D corresponds to a substance having a viscosity undergoing a substantial change during an observed industrial process from a low viscosity level to a medium viscosity level. In this case, an exposure period of 5 ms would be too long to cover the change in viscosity to be observed. An exposure period of 100 is would cover the change of contrast in the speckle pattern between 0 and 1. Such an exposure period would therefore lead to a variation in the contrast of the speckle pattern detectable when the viscosity of the substance changes during the industrial process. It is to be noted that the initial flat segment 52 of the sigmoid-like curve 50a for a 100 ps exposure period is flat for about the first half of viscosity range D, and that changes of viscosity within that first half would not lead to an observable change in contrast of the detected speckle pattern. In some variants, the associated exposure period may still be used if the change in viscosity occurring within the second half of the viscosity range D suffices to provide the desired monitoring. As will be noted, the sigmoid-like curve 50b associated with an exposure period of 1 ms shows a variation in contrast only in the highest part of viscosity range D. Furthermore, only about a third of the available contrast sensitivity for this exposure period falls within this viscosity range. This exposure period may be of use if the contrast variation over the highest part of viscosity range D provides the desired monitoring and is detectable with the system used for the monitoring.

[0065] Referring back to FIG. 1, the optical method 100 further includes a step of monitoring 110 an evolution of the contrast of the speckle pattern over time.

[0066] In some embodiments, the method 100 may include, for each light acquisition, calculating 108, in real time, a speckle contrast parameter representative of a contrast of the speckle pattern over the exposure period, the monitoring step above comprising monitoring the speckle contrast parameter over time.

[0067] The speckle contrast parameter may be embodied by any measurable or calculated variable having a value scaling with the level of contrast in the speckles observed from the scattering of the light beam. Different implementations of the steps of obtaining 106 the scattering data sets and calculating 108 and monitoring 110 the speckle contrast parameter may be envisioned, examples of which are provided below. In some implementations, the calculation of the speckle contrast parameter may be achievedbased on one of two methods of analysis, a spatial analysis and temporal analysis. In some embodiments, both analyses can also be combined to achieve desired results.

[0068] Referring to FIG. 4, there is presented a flow chart of the steps involved in calculating the speckle contrast parameter using a spatial analysis, according to some embodiments. In this variant, the step of performing a plurality of light acquisitions includes acquiring 112, over time, a plurality of 2D images each representative of a scattering of the coherent light beam in the substance. Preferably, in such variants, the light sensing device 26 may be embodied by a camera or other form of 2D matrix of pixels 27 (see FIG. 2). By way of example, the camera can be an 8-bit camera or a 16-bit camera, that is, the camera can capture images with 256 levels of each colour in an 8-bit format. In some variants, the camera is configured to capture images with a pixel dimension of 250x250. Pixel dimensions express the number of pixels horizontally and vertically that define the resolution of the image. In other variants, a single mode optical fiber with a photodiode could be considered as a suitable 1 -pixel camera, and a plurality of such 1 -pixel cameras could be provided in an array to obtain the 2D images.

[0069] In some implementations, each light scattering data set may be obtained by capturing a series of light scattering data subsets and averaging the data values associated with each pixel. By way of example, several light scattering data subsets may be captured over several acquisition sub-periods at a fixed interval over a given acquisition period. The averaging of the data values associated with each pixel results in a 2D image corresponding to a light scattering data set. Each 2D image represents the distribution of the scattered light in the observation volume 25 corresponding to the space immediately under an observation area of the substance covered by the field of view of the camera. The observation area may be selected so as to enable individual pixels to be resolved, while providing enough data for reliable statistical analysis. In typical implementations, the camera may have a shutter that opens for the duration of the exposure period each time an image is taken. By definition, in imaging applications, the amount of light that reaches each pixel of the camera corresponds to the exposure, and the accumulated light intensity on each pixel during one exposure period corresponds to a data point of the corresponding light scattering data subset.The plurality of light acquisitions, corresponding for example to obtaining the plurality of 2D images embodying the plurality of light scattering data sets, are performed at an acquisition frequency. In some implementations, the plurality of 2D images is obtained by repeating the image capture process explained above at an image capture frequency, defining the acquisition frequency. The image capture frequency can vary according to the needs of the application. By way of example, for an exposure period of 1 ms, it is possible to capture an image every millisecond. In other variants, the capture frequency may be longer (e.g., 5ms, 10ms, every second, etc.). The capture frequency may be constant of may vary in time in some variants.

[0070] Each 2D image includes a speckle pattern. FIGs. 5A, 5B and 50 show three examples of 2D images 40a, 40b and 40c acquired at times where the viscosity of the substance corresponds to values towards the beginning, the middle and the end of the transitional segment of the corresponding sigmoid-like curve, respectively. Each 2D image 40a, 40b and 40c has a corresponding speckle pattern 42a, 42b and 42c. The contrast level of the speckle pattern is seen to increase over time.

[0071] Referring back to FIG. 2, in some embodiments, the optical system 20 may include a controller 29 operatively connected to the camera 26 (embodying a light detecting device) and configured to obtain therefrom, over time, the plurality of light scattering data sets. The controller 29 may be integral or external to the camera 26. The controller 29 sends control signals to the camera 26 to open and close the shutter and perform any task associated with the acquisition process. The optical system 20 further includes a processing unit 30 such as a server, computer, tablet, or the like. The processing unit 30 can be a single machine or a cluster of machines interconnected with each other, and the controller 29 may in some embodiments be integral to the processing unit 30. The processing unit 30 can include physical machine(s) and / or virtual machine(s). For instance, the camera 26 can be connected to a computer or computers positioned next to a tank 23 containing the substance 24 and transmit the images it acquires to these computers. In other examples, the camera 26 can be wireless and transmit the images to virtual computers stored in a cloud infrastructure at another location. In one variant, the processing unit 30 may be a microcontroller in operative communication with the camera 26. The microcontroller can have one or more processor(s) and memory. The memory canstore a program or code instructions to carry out the calculation of a speckle contrast parameter when executed by the processor(s).

[0072] Each image captured by the camera 26 can be stored by the processing unit 30 before being analyzed. In one variant, images are stored in a directory on a microcontroller or computer. When the program is run (i.e., the processor performing the calculation), it is configured to extract the images from this directory and perform the associated operations, as detailed below. In other cases, images may be saved in a relational or non-relational database.

[0073] Referring to FIGs. 4, 6 and 6A, after acquiring 112 a plurality of 2D images, the monitoring 104 of the viscosity of the substance involves calculating 114 the speckle contrast parameter of the speckle pattern in each 2D image. In some implementations, this calculation may be performed by dividing 116 the 2D image, or a selected zone of the 2D image, into a plurality of tiles. In the illustrated example of FIG. 6A, the central zone of the 2D image 40a of FIG. 6 is shown divided into 25 tiles of equal size, but one skilled in the art will readily understand that the number of tiles and format could vary. By dividing the 2D image into tiles, it is meant that the 2D image is segmented into equal portions such that the local mean intensity of the detected light is approximately constant, so that each portion can be analyzed. The tiles are added to a list and a speckle contrast value is then calculated 118 for each tile. In some embodiments, the speckle contrast value may be obtained by subtracting the minimum light intensity of the tile from the maximum intensity and dividing by the average intensity for the tile. A standard deviation can be calculated, normalized or not by the average intensity. A histogram can be analyzed in more details to extract more information than the average and standard deviation (kurtosis, etc..), as explained below. Other definitions of the speckle contrast value may be envisioned.

[0074] The method may then involves calculating 120 an image average of the speckle contrast values for the plurality of tiles of the 2D image. In embodiments involving a plurality of light scattering data sub-set consisting of a plurality of sub-images, this step may be repeated for each sub-image until the size of the contrast data list is greater than or equal to a threshold value. For example, if the threshold value is 100 and 400 images per second are accumulated in the directory after the 50th sub-image, the average contrast ismeasured. The calculated image average of the speckle contrast value, or a value directly derived therefrom, is outputted as the speckle contrast parameter.

[0075] In some cases, it can be considered that once the speckle contrast parameter for a moment in time is outputted, the images that were previously analyzed are removed or deleted from the directory. For instance, once analyzed, these images can be moved to a different directory on a different device and replaced with new images captured by the camera. In some implementations, storing these images in another directory for future use may be useful for automation purposes. For example, these images can be used as a training dataset to train a suitable machine learning algorithm.

[0076] A more detailed example of carrying out the spatial analysis process is presented as follows (using Python syntax and terminology, where np=numpy):

[0077] the images are extracted from the directory in which they have been stored and are then sorted by filename and placed in a list of images (named files); the processor loops through each image in the list and opens it;

[0078] Once opened, the processor segments the image into tiles of the same size (show by the function tilejmage) and add the tiles into a list of tiles; The processor then gets the tile at the centre of the list of tiles (img = tiles[C*C / / 2] where “ / / ” means integer division without remainder);

[0079] The time at which the image was captured is determined. If it's the first image, the time is set to 0.

[0080] The image is then put in an array (a form of list) img_array = np.array(img) and then the mean of all the colours in the image is calculated (called first mean) firstMean = np.mean(np.mean(img_array)) (line 68). The first mean corresponds to the mean of the colours in the first image. Because it’s the first, it is stored and used as an initial value at time 0 - in other terms, it corresponds to the first image captured;

[0081] The image array goes through a function image_contrast in which the contrast for each tile of the image is calculated ( np.std(tile) I np.mean(tile), or can also be calculated simply with np.max(tile) - np.min(tile) I np.mean(tile)) and returned in a contrast list (maned contrasts).The processor determines the size of the contrast list is at least 100. For example, for 5 images you can have 125 tiles (25 tiles per image), which is equal to 125 contrast value for each of these tiles. If it’s more than 100, it returns the mean of all these contrasts and the time at which it was taken. This would result in a value such as at 5 minutes the contrast is 0.3.

[0082] As mentioned above, in some examples of implementation in which the light sensing device includes a photodiode, the exposure time may correspond to the length of time over which the photodiode collects photons from the scattered light beam, and the light scattering data set may be embodied by a variation of the intensity value of the detected light as a function of time over the exposure period. In such variants, calculating the speckle contrast parameter may involve monitoring a temporal evolution of the speckle pattern over a given light acquisition. Each light scattering data set may for example include a plurality of light intensity values each integrated over an exposure sub-period of the acquisition period for a given pixel. The speckle contrast parameter may then be obtained by calculating a temporal contrast value representative of this light intensity value over the number of sub-periods of the light acquisition, and outputting this temporal contrast value as the speckle contrast parameter. In a multi-pixel variant, the temporal contrast value may be calculated for each pixel, and the speckle contrast parameter obtained from a statistical analysis.

[0083] In the illustrated variant of the optical system 20 shown in FIG. 7, the light sensing device 26 may be embodied by a photodiode, embodying a single pixel. A focussing lens 38 and / or any optical component or assembly of optical components may be used to direct the scattered light beam 28 to the light sensing device. In another example, the scattered light beam may be directed into an optical fiber (not shown) carrying the scattered light beam to the light sensing device located away from the substance.

[0084] In another variant, an array of pixels such as the camera shown in FIG. 2 may be used in conjunction with a multi-pixel temporal analysis. In one example, the method may include looping through the images (light intensity values) from a given directory, determining whether the image is the first image taken (similar to what was done for the spatial analysis), and then calculating the temporal contrast for each pixel of the images. To do so, the numeral values of the pixels in each image are added into sequences of lists calledstack_array (ex: values of the pixels of image 1 are added in a list with the values of the pixels of image 2 in the same list). The variance is calculated based on the standard deviation of the stack_array divided by the average (or mean) of the same stack_array. The variance is then flattened, meaning that the array (or list) is transformed into a single list or array (or a single dimension, since the calculation is based on a two-dimensional array). Finally, based on the variance, the mean standard deviation and skew are calculated along with the time of the time at which the initial images were taken.

[0085] In some implementations, it is possible to build a histogram from the data obtained through the temporal or spatial analysis. Typically, a histogram gives the intensity distribution across an image, i.e., the number of pixels with each possible intensity level or across time at a given pixel. For example, in the case of an 8-bit grayscale image, it is possible to have a total of around 256 possible intensities. The histogram can therefore be generated from the set of pixels in the collected images. For example, as explained in the previous code, one could stack the pixels of several images together and then calculate the histogram at that point in time. The processing unit could then save the histogram data in a directory or database and use it at another time (several seconds or minutes later) to compare it with a newly generated histogram, based on newly processed images representing a different state of the curd. By making this comparison, it would be possible to see the difference in pixel distribution and consequently the variation in speckles over time.

[0086] It should be noted that the algorithms described above may run continuously throughout the process of monitoring the viscosity of the substance. In other words, in preferred embodiments the processing unit continuously receives images, processes them and returns a new contrast / time value for display.

[0087] The monitoring of the evolution of the speckle contrast parameter can then occur. At each given exposure period, or after a certain number of images have been taken at a given interval, the algorithm (or the processor) returns a time and contrast value representative of the average (mean) contrast for these images.

[0088] The results of the monitoring step 110 of the present optical method 100 may be outputted in a variety of fashions, for example depending on the requirements of the industrialprocess being monitored. In some implementations, the speckle contrast parameter can be outputted to a user for consultation. In some cases, the output can simply be the raw value and time. In other variants, the processor may be configured to accumulate the values it has determined (the set of time / contrast values) and generate a graph. As shown in both FIGs. 2 and 7, in one example, the method may involve displaying a graphical representation 32 of the speckle contrast parameter over time. The optical system 20 may include a display 34, such as an LCD screen, showing the graphical representation to an operator of the industrial process. In some embodiments, the processor may be in communication with a front-end module, or a display module adapted to display a numerical value of the speckle contrast parameter.

[0089] Alternatively or additionally, the processor can further be configured to determine that the result it outputs corresponds to a preset critical time or a preset threshold. For example, in the context of a cheese manufacturing process, the processor can be configured to determine that the viscosity level associated with the observed contrast of the speckle pattern corresponds to or is proximal to a curing time and / or a cutting time of the cheese. In this case, the processor may transmit an alert indicating the cutting or curing time. An alert can be transmitted in the form of a signal to a light (LED light), an audio queue, an e-mail alert on an employee's device, and so on. In some embodiments, the method may involve outputting, in real time, the speckle contrast parameter to an industrial system 36 associated to the industrial process. The industrial system 36 may be configured to take any appropriate action related to receiving notice of the speckle contrast parameter having reached a particular value or undergone a change predetermined to represent a target change in the viscosity of the substance.

[0090] Of course, numerous modifications could be made to the embodiments above without departing from the scope of protection.

Claims

Claims:

1. An optical method for monitoring a viscosity of a substance in an industrial process wherein the viscosity is subject to change over a predetermined viscosity range during said industrial process, the method comprising:a) projecting a light beam which is at least partially coherent on the substance; b) performing a plurality of light acquisitions, each light acquisition being performed over an exposure period and providing a light scattering data set representative of a scattering of the light beam in an observation volume of the substance during said exposure period, each light scattering data set including a speckle pattern, said plurality of light acquisitions being performed at an acquisition frequency; andc) monitoring an evolution of a contrast of the speckle pattern over time; wherein a duration of the exposure period is selected such that the contrast of the speckle pattern has a measurable change within the predetermined viscosity range.

2. The optical method according to claim 1, wherein the exposure period is further selected such that an associated sigmoid-like curve of the contrast of the speckle pattern as a function of the viscosity of the substance comprises a transitional segment over the predetermined viscosity range.

3. The optical method according to claim 2, wherein the transitional segment of the sigmoid-like curve completely extends within the predetermined viscosity range.

4. The optical method according to claim 2, wherein the transitional segment of the sigmoid-like curve partially extends within the predetermined viscosity range.

5. The optical method according to any one of claims 1 to 4, further comprising, for each light acquisition, calculating, in real time, a speckle contrast parameter representative of the contrast of the speckle pattern over the exposure period.

6. The optical method according to claim 5, wherein said monitoring the evolution of the speckle contrast parameter comprises monitoring a spatial evolution of the speckle pattern.

7. The optical method according to any one of claims 1 to 4, wherein each light scattering data set comprises a 2D image of the speckle pattern in the observation volume.

8. The optical method according to claim 7, wherein calculating a speckle contrast parameter representative of the contrast of the speckle pattern over the exposure period, said calculating comprises, for each 2D image:a. dividing the 2D image into a plurality of tiles;b. for each tile, calculating a speckle contrast value; andc. calculating an image average of the speckle contrast values for the plurality of tiles of said 2D image.

9. The optical method according to claim 8, wherein calculating a speckle contrast value for each tile comprises subtracting a minimum light intensity on the tile from a maximum intensity on the tile and dividing by an average light intensity for the tile.

10. The optical method according to claim 8, wherein calculating a speckle contrast value for each tile comprises analysing a histogram of a light intensity distribution over the 2D image.

11. The optical method according to claim 5, wherein said monitoring the evolution of the speckle contrast parameter comprises monitoring a temporal evolution of the speckle pattern over a number of said light acquisitions.

12. The optical method according to claim 11, wherein each light scattering data set comprises a plurality of light intensity values each integrated over an exposure subperiod of the acquisition period for at least one pixel.

13. The optical method according to claim 12, wherein calculating the speckle contrast parameter comprises, for each light acquisition, calculating a temporal evolution of said light intensity value over a number of said sub-periods of the light acquisition for each of the at least one pixel.

14. The optical method according to any one of claims 1 to 13, wherein monitoring the evolution of the speckle contrast parameter over time comprises displaying a graphical representation of the speckle contrast parameter over time.

15. The optical method according to any one of claims 1 to 14, further comprising outputting, in real time, the speckle contrast parameter to an industrial system associated to the industrial process.

16. An optical monitoring system for monitoring a viscosity of a substance in an industrial process wherein the viscosity is subject to change over a predetermined viscosity range during said industrial process, the optical system comprising:a coherent light source generating a light beam which is at least partially coherent and configured to project said light beam on the substance, a scattering of the light beam in the substance resulting in the emission of scattered light from the substance;a light sensing device configured to detect the scattered light;a controller operatively connected to the light detecting device and configured to perform a plurality of light acquisitions, each light acquisition being performed over an exposure period and providing a light scattering data set representative of a scattering of the coherent light beam in an observation volume of the substance during said exposure period, each light scattering data set including a speckle pattern, said plurality of light acquisitions being performed at an acquisition frequency; anda processor configured to monitor an evolution of a contrast of the speckle pattern over time;wherein a duration of the exposure period is selected such that the contrast of the speckle pattern has a measurable change within the predetermined viscosity range.

17. The optical monitoring system according to claim 16, wherein the coherent light source comprises a laser diode.

18. The optical monitoring system according to claim 16 or 17, wherein the light sensing device comprises a 2D matrix of pixels and / or a photodiode.

19. The optical monitoring system according to any one of claims 16 to 18, wherein the exposure period is further selected such that an associated sigmoid-like curve of the contrast of the speckle pattern as a function of the viscosity of the substance comprises a transitional segment over the predetermined viscosity range.

20. The optical monitoring system according to claim 16, wherein the transitional segment of the sigmoid-like curve completely or partially extends within the predetermined viscosity range.

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