Patent Description:
In the industrial processing of tuna and other pelagic species, the use of canning equipment is widely used. There are different types of machines for packing or canning tuna and pelagic species, where operators manually feed the machine with blocks of tuna (in the form of loins, pieces and/or crumbs). Then the equipment compacts the blocks, cuts them into portions and inserts the portions into the cans, at production rates of between <NUM> and <NUM> cans per minute. In the state of the art there is a plurality of disclosures directed to equipment and machines related to canning fish and specifically canning tuna and pelagic species, among which is the patent document <CIT> that teaches a movement system actuated by toothed wheels and a motor-reducer with a frequency converter.

On the other hand, patent document <CIT> discloses a machine that uses blocks of tuna, automating the feeding of raw material to the canning machine.

Document <CIT> discloses a machine that generates tuna advance with a conveyor band, and with rotating compaction chambers, equipped with a load cell to measure the compaction force.

Documents <NPL>) and <NPL>) disclose an automated system for portioning and filling of salmon into cans by optimally grouping and cutting salmon to reduce wastage. Document <NPL>, discloses the application of statistical experimental design and response surface methodologies to define the optimal conditions for a tuna canning process and reduce the variability in net weight of the canned tuna.

Currently existing canning equipment and machines have the drawback that they do not have the ability to adapt the operating parameters of the machine precisely to the particular conditions of each production of the machine (e.g. raw material used, can format, covering liquid used, etc.), which may vary during the production itself, but instead follow an empirical trial and error operation, where an operator periodically adjusts machine parameters (e.g. change target fill weight) according to his experience. This subjective adjustment of the operating parameters of the machine is an inaccurate adjustment that produces variations in the drained weight of the cans, generating weights below what is defined, with the consequent rejection, or more usually, weight above what is defined to avoid non-compliance with the minimum requirements, with the consequent economic loss. In addition to the weights, the imprecise adjustment of the packing machine's operating parameters can also affect the quality of the canned product, and in particular the height (maximum, minimum and average) of the tuna cake in the can, characteristics that may affect the appearance of the product and that, in unbalanced conditions, can lead to product rejection.

In the state of the art, there is therefore, a need to implement a tuna canning system that has the ability to adjust the operating parameters of the machine objectively, precisely and adaptively to the particular and changing conditions of each production, with the aim of purpose of optimizing production.

This invention refers to a system according to claim <NUM> and an adaptive method for canning tuna according to claim <NUM> that solves the problems of the state of the art previously listed, allowing to adjust the operating parameters of a tuna canning machine in a precise, adaptive and optimized way to the particular conditions for each production. By means of these adjustments it is possible to optimize the production; for example, the gain of the production process can be improved (i.e. percentage of weight obtained at the end of the process with respect to the weight of raw material that enters the packing machine), the precision in the filling weight of the cans and/or the quality or appearance of the product.

In one embodiment, the tuna canning machine comprises at least one sensor configured to measure one or more properties of the tuna during the canning process, such that the input parameters comprise said properties of the tuna. Advantageously, the system obtains optimal operating parameters of the tuna canning machine that adapt to the changing conditions of the raw material entering the packer during production. The variations produced in the properties of the input raw material can significantly affect the productivity of the machine, since these properties (such as humidity or temperature) influence the weight of the tuna and/or the compaction of the tuna performed by the machine before canning and weighing the can, thereby affecting the filling weight and the gain of the production line. Sudden changes and fluctuating situations in the properties of the tuna can produce significant deviations in the filling weights (thus increasing their standard deviation) that decrease the machine's productivity.

The tuna canning machine may comprise a temperature sensor configured to measure the temperature of the tuna during tuna canning. Preferably, the temperature sensor is located at the entrance of the packer, coming into contact with the blocks of tuna (e.g., a sensor located on the raw material conveyor band, and protruding or flush with the top edge of the band), to obtain continuous temperature measurements on the tuna as it enters the packer input conveyor.

Additionally, or alternatively, the tuna canning machine may comprise an NIR sensor configured to continuously measure one or more properties of the tuna, such as humidity, protein, fat and ash content of the tuna. To do this, this NIR sensor uses diode array technology, and is based on the analysis of the near infrared, through the spectroscopic technique that naturally uses the electromagnetic spectrum. Spectrometry is the measurement of the amount of energy an element absorbs as a function of wavelength. The NIR sensor is located, preferably also at the entrance of the packing machine, so that the near infrared light is directed at the tuna or raw material that enters the machine. The light is modified according to the composition of the tuna and the NIR sensor detects this modified light. The spectral modifications are converted into information on the composition of the tuna through specific calibrations for each type of product. It is a technique used in other sectors (meat, fruit and vegetable or dairy) that allows the detection of contaminants and foreign matter in food and that allows the measurement of various parameters such as humidity, fats, proteins, sugars, fiber, etc..

In this invention, the NIR sensor is configured to measure some parameters that have been identified as relevant for the performance of the tuna canning process, since they can directly affect the absorption and gain obtained at the end of the process, thus influencing the determination of the optimal adjustment of the operating parameters of the machine. Notably, the following parameters have been identified: humidity, fats, proteins, and ashes. The NIR sensor is preferably configured to measure any combination of these parameters (e.g., only humidity, humidity, and fats, etc.). Any commercial NIR sensor can be used, on which a specific calibration is performed to be used on tuna.

The use of the properties of the tuna measured, preferably continuously (with a short sampling time), by means of the NIR sensor or the temperature sensor during production allows the machine to be optimally adjusted, since it takes into account truly relevant properties that affect the productivity of the packer. In addition, this property is usually very changeable, since the tuna introduced into the machine does not always have to be the same (type of species, type of packaging, type of processing, fishing area, etc.) nor is it introduced into the packer under the same conditions of temperature or humidity, for example.

The tuna canning machine may comprise a vibration sensor, configured to detect vibrations in some part of the machine, which may be associated, according to a vibration pattern, with unwanted operations of the machine. Using information captured by the vibration sensor, the machine can be configured to obtain a vibration pattern that corresponds to what is obtained when the machine is at peak performance.

This invention advantageously allows the machine to be adjusted with the optimal parameters calculated according to the characteristics of each production, either at the beginning of it (e.g., once before starting production or just started) or dynamically over manufacturing time, to adapt continuously and repeatedly to changing production conditions, such as the temperature and humidity properties of tuna. Thus, when the packer control unit detects a change in any of the production input parameters, such as a significant alteration (e.g. above a certain threshold) in the temperature or humidity of the tuna, the control unit can send the new production input parameters to the optimization unit, including the parameter value (e.g., temperature, humidity, etc.) altered, so that it calculates the optimal operating parameters, and sends them to the control unit for application, so that the tuna canning machine can maintain an optimal productivity throughout the production process.

The invention makes it possible to obtain optimal packer settings calculated by the optimization module. Optimum operating parameters are preferably obtained through supervised machine learning that includes training using, at least partially, historical production data. For this, the data types of past productions acquired by the training tuna canning machines must be identical, or at least similar, to the data types that are acquired in the new production of the tuna canning machine. Thus, the input parameters of the new production must be a data type included, at least partially, in the production data history. Training tuna canning machines are preferably machines of the same type (model, manufacturer) as the tuna canning machine on which the new production is carried out, which can be part of the set of training tuna canning machines.

The settings calculated by the optimization module are optimal operating parameters that are applied to the tuna canning machine to optimize the new production. Depending on the established target function, one or another aspect of the production process will be improved. In one embodiment, the target function is based on maximizing the number of cans produced per mass unit of input tuna. The target function defined in this way improves the gain and accuracy of the filling weights, which directly influences the productivity of the line. Other objective functions can be used, such as maximizing gain, minimizing the standard deviation of fill weights, maximizing the quality of product appearance, or a combination of several factors applying a relevance factor (i.e., weights) to each of them.

The application of the optimal settings on the machine can be done manually by an operator and/or automatically by the packer's own control unit. For example, there may be a combination of manual and automatic adjustments, where for some machine adjustments may be necessary, due to their physical nature (e.g., change of nozzle used in the packer), manual change to be made, while other adjustments can be implemented automatically by the control unit (e.g., automatic change of target packing weight).

Next, a series of drawings that help to better understand the invention, and that are expressly related to an embodiment of said invention, which is presented as a non-limiting example of it, are very briefly described.

<FIG> illustrates an adaptive tuna canning system according to one embodiment of this invention. System <NUM> comprises a tuna canning machine <NUM>, and an optimization unit <NUM>.

The tuna canning machine <NUM> includes a control unit <NUM>, implemented for example, by means of a computer in communication with a PLC, in charge of controlling the machine (including, e.g. controlling of parameters or configurable settings of the machine) during the tuna canning process of a production.

The optimization unit <NUM> comprises a data processing unit <NUM>, which is configured to:.

Once the optimal operating parameters <NUM> have been obtained, the optimization unit <NUM> sends them to the tuna canning machine <NUM> for their application in the new production. The tuna canning machine <NUM> is configured, either automatically and/or manually, to execute the canning of tuna in the new production based on the optimal operating parameters <NUM> received.

The at least one training tuna canning machine <NUM> may include the tuna canning machine <NUM>. <FIG> shows several training tuna canning machines <NUM>, which may be located in different locations, to obtain the production data history <NUM>. System <NUM> could use a single training tuna canning machine <NUM>. Furthermore, in one embodiment, the tuna canning machine <NUM> itself can be the training tuna canning machine <NUM>, so that the optimal operating parameters <NUM> of the tuna canning machine <NUM> are obtained by maximizing a target function using past production data <NUM> of own tuna canning machine <NUM>.

In <FIG> the optimization unit <NUM> is represented as an external device to the tuna canning machine <NUM>, implemented for example by means of a remote server connected to the control unit <NUM> through the Internet, or by means of a local computer located in the own installation of the tuna canning machine <NUM>, and in wired or wireless communication with the control unit <NUM>. However, in another embodiment the optimization unit <NUM> can be part of the tuna canning machine <NUM> itself, as an additional element integrated into the machine or even integrated into the control unit <NUM> itself (in this case the communication between the optimization unit <NUM> and the control unit <NUM>, since both are integrated in the same device). The data processing unit <NUM> could therefore form part of the control unit <NUM>, or even be the same unit (that is, the control unit <NUM> of the tuna canning machine <NUM> can be configured to perform the functions of data processing unit <NUM> optimization unit <NUM>).

The optimal operating parameters <NUM> obtained by the optimization unit <NUM> are applied in the new production of the tuna canning machine <NUM>. There are different ways for this. For example, the optimization unit <NUM> can send these parameters to the control unit <NUM> (as shown in <FIG>), which is configured to receive the optimal operating parameters <NUM> for the new production and automatically apply configurable settings in the tuna canning machine <NUM> based on the optimal operating parameters <NUM> received.

Optimum operating parameters <NUM> may include machine parameters or settings that are manually made by a machine operator (due to, e.g., a physical change to be made to the tuna canning machine <NUM>). In this case, the calculated optimal operating parameters <NUM> can be represented on a screen <NUM>, so that an operator can configure the tuna canning machine <NUM> according to said parameters.

The optimization unit <NUM> is preferably configured to calculate the optimal operating parameters <NUM> through supervised machine learning that includes a training stage using the production data history <NUM>.

The tuna canning machine <NUM> preferably comprises at least one sensor <NUM> configured to measure one or more properties of the tuna <NUM> during the canning process, where the properties of the tuna <NUM> form part of the input parameters <NUM> of the new production.

The at least one sensor <NUM> may comprise a temperature sensor configured to measure the temperature of the tuna entering the machine during the canning process and/or a NIR sensor (near-infrared spectral region) configured to measure, by means of spectroscopy techniques of the near infrared, at least one tuna property. In one embodiment, the NIR sensor is configured to measure at least one of the following properties of the tuna: humidity, protein, fat and ash content of the tuna.

Adaptive tuna canning system <NUM> can be applied to any type of tuna canning machine <NUM>.

This invention also refers to an adaptive tuna canning method, as represented in the flow chart of <FIG>, according to a possible embodiment. Method <NUM> comprises the following steps:.

Method <NUM> may comprise measuring <NUM> one or more properties of tuna <NUM> during the canning process in tuna canning machine <NUM>; where the input parameters <NUM> of the new production comprise the properties of the tuna <NUM> measures. The properties of the tuna <NUM> measured may comprise, e.g., the temperature, humidity, protein, fat and/or ash content of the tuna. Measurements of the properties of tuna <NUM> can be made only once, e.g., before starting new production or during new production.

Alternatively, the measurements can be performed repetitively during the canning of tuna in the new production. In this case, as shown with dashed lines in the flow diagram of <FIG>, the method may comprise receiving <NUM>, repeatedly during the running of the tuna canning of the new production, some input parameters <NUM> of the new production including one or more properties of the tuna <NUM> measurements in each iteration; calculating <NUM>, for each iteration, optimal operating parameters <NUM>; and configuring <NUM> the tuna canning machine <NUM> (by applying configurable settings on the tuna canning machine <NUM>) based on the optimal operating parameters calculated at each iteration. In a similar embodiment of the system <NUM>, the control unit <NUM> can be configured to send to the optimization unit <NUM>, repeatedly during the new production, input parameters <NUM> of the new production, including one or more properties of the tuna <NUM> measured by the at least one sensor <NUM> in each iteration; and receiving, in each iteration, optimal operating parameters <NUM> obtained by the optimization unit <NUM>. The control unit <NUM> can be configured to apply, automatically in each iteration, configurable settings in the tuna canning machine <NUM> based on the optimal operating parameters <NUM> received in each iteration (some of the optimal operating parameters can be manually adjusted by an operator).

<FIG> illustrates a block diagram of the adaptive tuna canning system <NUM> according to a particular embodiment. The tuna canning machine <NUM>, or packer, on which the adaptive canning method is applied, comprises:.

In one embodiment, the input weigher <NUM> is an automatic weighing system installed in a section of the conveyor before reaching the packer, with the function of weighing the tuna continuously. In another embodiment, the input weigher <NUM> is a scale configured to obtain punctual measurements of input tuna. The scale is used if the tuna is received in batches (e.g., boxes, bags, carts), where an operator located at the entrance of the machine must take each of these sets, place it on the scale and, once that weight has been recorded, deposit the tuna, emptying the container into a fish receiving tray that is connected to the packer's inlet conveyors. In this case, the tuna container element must be previously used to tare the scale, so that weight will be subtracted in each new weighing and the weight of the container itself will not be collected as part of the raw material.

The input tuna weight is used to later calculate the diminishment generated during packaging, that is, the amount of tuna mass that is lost in the process. This loss is one of the fundamental parameters to know to evaluate the performance of a line or factory, in order to take into account, the liquid that contained the tuna and that is lost in the compacting process, or the small crumbs and pieces that escape at different points of the machine (due to gaps, friction, etc.). For the calculation of the losses to be correct, all the product that is introduced into the machine must be weighed, as well as the weight of all the manufactured cans obtained by means of the dynamic weigher <NUM>. To calculate the loss, the total net weight of the canned tuna is subtracted from the total weight of the tuna put into the machine. Therefore, when the target function is related to gain and filling weight accuracy, the input weigher <NUM> and the dynamic weigher <NUM> are important to obtain the optimal operating parameters <NUM>.

The cans <NUM> accepted by the dynamic weigher <NUM> (i.e., those that have acceptable weights) are transported to the following stages of the production line until the final product <NUM> is obtained, with the added covering liquid, and the can duly closed and sterilized. Subsequently, the draining process <NUM> is carried out in the laboratory of a certain number of cans produced, randomly selected from a production, obtaining an average drained weight, which is defined as the arithmetic mean of the weights obtained after the process of draining the covering liquid. Knowledge of the drained weight and packing weight of a can (or the average packing weight of the cans produced in that production), together with the machine and raw material conditions, and settings at the time of manufacture, allows obtaining relevant production parameters, such as absorption and gain.

By providing the control unit <NUM> with access to Internet <NUM>, the optimization unit <NUM> can collect the data collected during production. Subsequently, in the server <NUM> the data is structured, stored and processed to prescribe the optimal settings of the machine (i.e., optimal operating parameters <NUM>). The optimization unit <NUM>, in addition to providing relevant information about the process, has the ability to discover which are those variables that influence the final result of the production and, according to given input conditions, recommending to the user how to adjust them so that the objectives set according to a target function <NUM> (e.g. weight gain and precision objectives) are the best possible, based on the behavior obtained in past productions, previously received and stored in memory (e.g. in a database <NUM>), which corresponds to a production data history <NUM>. As explained above, said production data history <NUM> may correspond to past production data <NUM> of the same tuna canning machine <NUM>, or other similar tuna canning machines located in different factories or locations.

The optimization unit <NUM> has the objective of prescribing a series of optimal parameters, providing recommendations of the machine parameters on which it is possible to act, and that, based on past production histories, they are identified as optimal for specific input conditions. To do this, optimization unit <NUM> performs the following steps:.

In the tuna canning process, different critical variables can be considered for the production process. In one embodiment, the following parameters related to weight and gain are considered:.

Additionally, or alternatively, the following critical parameters related to product quality may also be considered:.

The following critical parameters related to the performance of the production line can also be considered:.

Once the critical variables have been identified, the desired final objectives are defined. The final objective is related to maximizing the profitability of the production line. Depending on the critical variables considered, different final objectives can be expressed:.

The first objective is focused directly on the final product, and on the optimization of the raw material. This objective, related to gain, implicitly implies the optimization of diminishment and absorption. Accuracy in weight translates into optimizing the standard deviation of the weights, trying to obtain the minimum possible value. The lower this deviation, the lower the target packaging weight may also be, reducing the margin that must be maintained to meet the established minimums. The second objective focuses on other aspects of the production line, related to the use of the machine, minimization of breakdowns, preventive maintenance, etc..

Once the critical parameters, and the variables to be optimized to meet the defined objectives have been defined, they are grouped into a single target function that encompasses them, and which will be the one that the algorithm will try to optimize.

Measured gain as the ratio between the final drained weight, and the weight at the entrance to the packer, the standard deviation of the machine does not influence said value, since with a greater standard deviation the accumulated weight of tuna at the entrance will increase, but also the accumulated weight of tuna after draining. Considering that you want to produce a certain number of cans, if the machine has a higher standard deviation, both the input weight, and the output weight will increase to produce the same number of cans (as shown in the chart in <FIG>).

However, if instead of considering output weight, the number of cans manufactured is counted, both gain and precision in weight can be grouped together, being able to take as a final objective to optimize the number of cans produced per mass unit of input tuna in the packer (e.g. number of cans produced per kilogram of input tuna), since this variable is affected both by the standard deviation of the weight (accuracy in weight), and by the gain. In particular, as illustrated in <FIG>, an increase in the standard deviation causes a decrease in the number of cans per kilogram of input tuna (<FIG>), and a decrease in gain also causes a decrease in the number of cans per kilogram of input tuna (<FIG>).

On the other hand, in addition to achieving the objectives of weight gain and precision, the appearance of the product can also be considered, either the appearance when it comes out of the package and/or the final appearance obtained. The quality of the required appearance can be a more or less a relevant factor. In the event that appearance is not considered an important factor, accuracy in weight and final gain can be prioritized first. In cases where appearance is key, it can also be included in the final objective, although to achieve this it is necessary in many cases to increase the packaging weight.

With regard to the appearance of the final packaging, this can be objectively assessed by means of an artificial vision system, based on the elements detected in the image (residues of skin, sangacho, thorns, etc.) and the height of the tuna cake, and its distribution inside the can.

The final appearance of the product can be evaluated manually, although an artificial vision system could alternatively be used to objectify the measurement. The final appearance is defined during the draining process, wherein a certain number of cans are opened, the user observes their appearance, and manually enters the evaluation they consider into the system. In order to quantify the final appearance, a scale can be defined, e.g., a scale of five values in which value <NUM> represents an unacceptable or poor appearance, and value <NUM> represents an optimal appearance.

When it comes to maximizing the number of cans produced per mass unit of input tuna (that is, maximizing gain and weight accuracy), the appearance of the product can also be taken into account (be it the appearance of the final packaging, the final appearance or a combination of both). In that case, it has to be established how much importance should be given to each of the two factors. Two options can be considered:.

If the final appearance is applied, in both cases it will be essential that during the registration of drained weights in the system by the user, the appearance obtained in the open cans is also registered, by means of the defined scale, in order to later have information on whether the system is meeting the desired objectives with the machine settings applied in each production.

In the second option, the target function is made up of two terms, the one that represents the number of cans manufactured per mass unit of input tuna (e.g. for each Kg of input tuna), and the appearance. The relevance factors that must be applied to each term (which we can call the gain factor and the appearance factor) can be considered to be two percentages, and both must add up to <NUM>%. Thus, for example, if appearance is of little or very little importance for a given product, prioritizing maximizing the number of cans, an importance percentage of <NUM>% or <NUM>% could be set for the appearance factor and <NUM>% or <NUM>%, respectively, for the gain factor. These percentages must be set by the user for each product to be manufactured and are added as two new parameters to be entered manually at the beginning of a production.

On the other hand, in addition to establishing the importance of each term, these can also be converted to relative values between <NUM> and <NUM>. In this way, the maximum function can be determined, with a value equal to <NUM>, at which the optimization algorithm has to get as close as possible, being able to compare the results obtained in different productions. For example, the number of cans manufactured per Kg of tuna will be higher the lower the target final weight, without meaning that the gain will be higher. Obtaining the relative values, it is possible to have an idea of the profitability obtained in each case, and different productions can be compared, regardless of the rest of the conditions.

To transform the gain value, an ideal maximum gain can be calculated, considering perfect conditions. These conditions are set, for example, as standard deviation = <NUM>, diminishment = <NUM>%, ideal absorption = <NUM>% (a value higher than the usual values is set).

Under these conditions, a target packing weight can be established: <MAT>.

In addition, the fact that the deviation is zero implies that the machine always packs the exact weight of tuna in each can, so that the input weight of tuna for a can would be equal to the packing weight. Thus, the maximum number of cans that could be manufactured under ideal conditions for each Kg of tuna would be: <MAT> wherein: <MAT>.

As an example, for a required drained weight of <NUM> grams, the target pack weight would be (<NUM> /<NUM>,<NUM>) = <NUM>,<NUM> grams, and the maximum number of cans that can be manufactured with <NUM> of tuna at the inlet of the packer, considering null diminishment, it would be (<NUM> / <NUM>,<NUM>) = <NUM>,<NUM> cans.

Thus, the first factor of the target function is the following, where α is the relevance factor assigned by the user to the gain factor: <MAT>.

Regarding the second term of the target function, it is necessary to transform the appearance value to a factor between <NUM> and <NUM>, simply dividing the appearance value obtained by the maximum possible, which according to the scale defined in the previous example will be <NUM>. Therefore, the second factor of the target function is: <MAT>.

Thus, putting both parts together, the target function can be expressed with the following formula: <MAT>.

The higher the value calculated for the target function, the higher the gain obtained in the corresponding production.

Next, two different algorithms are detailed that can be used for the automatic identification of the optimal settings (i.e., identification of the optimal operating parameters <NUM>): an algorithm based on iterative searches and an algorithm based on machine learning models.

With respect to the algorithm based on iterative searches, whether the appearance is initially set as the minimum requirement that the product to be manufactured must meet, or if its measurement is included in the target function to be maximized, the process to find the recommended settings is similar in both cases and is explained below.

The identification and compilation of all the parameters that are part of the process have already been carried out previously:.

Although the target packing weight could be considered at first as a production parameter, it can also be considered as a modifiable setting that can be acted upon to increase gain, in the event that tuna canning machine <NUM> implements an automatic adjustment (for example, by self-adjusting the advance of the machine), so that the average pack weight of all accepted cans <NUM> approaches the target pack weight indicated by the user.

For each registered production, all the related variables are stored, both those that are collected directly from the plant, and those that are calculated later, also including the result of the target function as one more variable. With these data, a history is formed in the form of a table or matrix, wherein each row represents a production, and each column represents a different variable. As the number of registered productions increases, the table grows, adding new data from past productions <NUM>, already made with a given tuna canning machine <NUM>. The more information there is, and the greater variability in the values that the considered parameters take, the better the optimal settings can be prescribed to optimize the gain and appearance parameters.

<FIG> shows an example of a table where, in each row, the data of past productions <NUM> (production <NUM>, production <NUM>,. , production n) are compiled, forming the data history of productions <NUM>. Columns record production parameters <NUM>, tuna properties <NUM>, machine properties <NUM>, modifiable settings <NUM>, KPIs <NUM> and final metrics <NUM>, including target value <NUM>. If any of the variables of the production parameters <NUM> changes value during a production of the machine, it is determined that it is another additional past production (i.e. a new row is added in the table, corresponding to new data from a past production <NUM>) for system purposes. The same can happen if other parameters change during production, such as the properties of the machine <NUM>, the properties of the tuna <NUM>, or the target packing weight (for example, if the average temperature of the tuna measured by the temperature sensor changes in a different way), it can be considered as a different past production with the new temperature value, and incorporate said production in another row of the production data history table <NUM>).

Once enough information has been collected in the production data history <NUM>, the algorithm can make prescriptions about the optimal settings that can be modified to maximize the final objective. To do this, as illustrated in the example in <FIG>, the algorithm based on iterative searches looks for the table for those productions whose input parameters match input parameters <NUM> of the new production. In the example shown, the input parameters <NUM> of the new production are defined by the production parameters <NUM>, although they could include others (such as the properties of the machine <NUM> or the properties of the tuna), depending on the search precision required. Once the corresponding rows have been selected in the table (selected outputs <NUM>), the result of the target function is checked for each of them, looking for the one that obtains the highest value. Once found, the settings and configurations registered in the chosen row are selected (optimal production <NUM>), corresponding to the modifiable settings <NUM>, which will depend on the type of tuna canning machine <NUM>. The selected settings and configurations (e.g. advance, torque, pusher pressure, etc.) are the optimal operating parameters <NUM> that are applied below to the tuna canning machine <NUM> in the new production.

Regarding the algorithm based on machine learning models, machine learning techniques are applied for the prediction and prescription of parameters. To use this type of algorithm, it is necessary to have a sufficient history, that is, there must be a large number of registered productions, with some variability in the parameters collected. Optionally, to obtain a greater quantity and variability of the data, these can be collected from multiple tuna canning machines, located for example, in different factories, cities, countries, etc..

In the first place, the prediction system of the final target value is developed, given a series of input parameters, which will include both the parameters that define the production, the properties of the raw material, and certain machine settings. This is a regression problem, since the expected output is a continuous variable, which is itself a subfield of supervised machine learning. There are different techniques to solve this type of problem, among which linear regression algorithms, logistic regression, decision trees and deep learning algorithms stand out.

Regardless of the algorithm applied, the process is similar, and is mainly made up of a training stage, a validation stage, and a test stage. The training stage is where most of the available historical data is used. The algorithm is given a multitude of records, with their corresponding inputs and the expected output for each of them, so that the machine learning the algorithm can learn based on historical data and identify patterns that would be impossible to extract manually. It is important that the variability of the data is high since, if not, the result could be biased to a specific case, making the system unable to generalize when the input parameters change, providing incorrect outputs.

In the validation stage, a set of production records is used, of which the expected output is known, and which have not been used to train the algorithm. For each of them, the output returned by the already trained model is obtained, and the results are analyzed in order to validate the system, adjust the model parameters, etc. Finally, in the test stage, the model is executed directly with new data, which the algorithm has not previously used, and whose output is not previously known.

The system works in such a way that, for a given set of conditions (input parameters <NUM> defining a new output), the already trained machine learning model <NUM> is executed using these parameters as input to the algorithm (inference), performing a random generation <NUM> of possible settings to establish a set of combinations of settings <NUM> that include possible values that the machine settings can take, obtaining a prediction of the value of the target function, target value <NUM>, for each combination of settings <NUM> , as represented in the chart of <FIG>. Then, the target value <NUM> with a higher value (maximum target value <NUM>) is selected <NUM>, and the combination of selected settings <NUM> corresponding to the maximum target value <NUM> are retrieved, which are the optimal operating parameters <NUM> to apply in the new production of tuna canning machine <NUM> (<FIG>).

A user modifiable parameter that is very relevant to the gain obtained at the end of production is the target pack weight, which is the target weight set in the dynamic weigher and/or in the control unit <NUM> for accepted cans <NUM>. This variable is normally entered manually by the user, estimating it according to the minimum drained weight that the final product must meet, and according to values also estimated for packaging diminishments and coverage liquid absorption. According to an embodiment of this invention, the selection of this value can be advantageously automated, as well as the selection of the rest of the modifiable configurations <NUM> of the machine. Following the same approach described, the optimization algorithm can prescribe the optimal target packaging weight, which maximizes the profitability of a particular production. The optimal target packing weight calculated by the optimization unit <NUM> can be sent to the tuna canning machine <NUM> for manual application by an operator or, alternatively, it can be received and applied in an automated way in the control unit <NUM>.

When a sufficiently large number of data have been collected in the production data history <NUM>, a total self-adjustment of the machine can be reached, without the need for user intervention. To ensure optimal automated operation, it is advisable to have a large number of productions with different input parameters, settings, and even different machines and factories.

The adaptive tuna canning system of this invention can be applied to any tuna canning machine. <FIG> illustrates, by way of example, a perspective view of a tuna canning machine <NUM>, in which blocks of tuna (raw material <NUM>), previously weighed on a scale (input weigher <NUM>), are introduced manually on a packer infeed conveyor. The tuna canning machine <NUM> packs the tuna into empty cans <NUM> received through an empty can feeding system <NUM>, directing the full cans <NUM> of tuna to the dynamic weigher <NUM> (not shown in the Figure).

<FIG> shows, in a schematic side view, a tuna canning machine <NUM> similar to the one shown in <FIG>, where the dynamic weigher <NUM> is shown at the outlet of the packer, receiving the full cans <NUM> from the packer through a conveyor system outlet <NUM>. In this embodiment, the tuna canning machine <NUM> has several sensors installed for measuring the properties of the tuna <NUM>, in particular, a temperature sensor <NUM>, a NIR sensor <NUM> and a vibration sensor <NUM>.

The temperature sensor <NUM> comes into contact with the tuna cakes to measure the temperature when the tuna cakes have already been compacted by a density control system <NUM>, implemented by means of a group of conveyors that exert pressure on the tuna cakes. The density control system <NUM> can be optionally assisted by a rammer <NUM>, which is responsible for controlling and homogenizing the density of the tuna, trying to distribute the tuna on the conveyor bands in a more homogeneous way to reduce the possible gaps that may appear.

The NIR sensor <NUM> is responsible for measuring the degree of humidity, fat, protein and/or ash content of the raw material <NUM> that enters the packer through a feeding conveyor system <NUM>. The vibration sensor <NUM>, located by example in the indexer-reducer assembly <NUM> of the packer, it can be used to detect undesired operations of the machine according to a vibration pattern.

An advance conveyor system <NUM> is responsible for moving the tuna cakes, already compacted by a density control system <NUM>, towards a nozzle <NUM> through an intermittent start-stop movement, which generates an intermittent advance, controlled, for example, by a servomotor. Next, the nozzle <NUM> is responsible for shaping the tuna cakes with a certain shape that adapts to the empty cans <NUM>, and a pusher <NUM> introduces the tuna portions into the empty cans according to an adjustable pressure and path. The base of the pusher (the part of the pusher that contacts the portion or tuna cake) can have perforations through which a blowing system of the machine can inject air and/or water vapor at the beginning of the movement of recoil of the pusher, in order to prevent the portion of tuna from sticking to the pusher <NUM> when it starts to recoil.

The tuna canning machine <NUM> shown in <FIG> also comprises an artificial vision system <NUM> located at the output of the packer, on the output conveyor system <NUM> (that is, after the stage of filling the empty cans), to capture the appearance characteristics of manufactured cans. The artificial vision system <NUM> can be used to make height measurements of the tuna portions canned by the pusher <NUM>. To do this, the artificial vision system <NUM> can comprise a laser profile sensor configured to make one or more height measurements (or profile detection) of the portion of tuna in the can. The control unit <NUM> receives these height measurements and is responsible for dynamically adjusting the height of the canned tuna portions by controlling the pressure and the path of the pusher <NUM>.

The artificial vision system <NUM> may also comprise a lighting system and a color camera (e.g., linear RGB camera) configured to capture color images of the canned tuna portions as they progress through the output conveyor system <NUM>. The artificial vision system <NUM> is preferably configured to detect defects (e.g., detection of sangacho, dark areas, foreign objects, holes in the tuna, or the presence of raw material in the sealing area) in the canned tuna portions by analyzing the images captured by the color camera and/or by analyzing the height measurements made by the laser profile sensor.

<FIG> shows, as an example, a list of parameters that can be used by the adaptive tuna canning system <NUM> based on the tuna canning machine <NUM> of <FIG>. A series of parameters can be entered manually (manual registration) through a graphical interface of the control unit <NUM> of the tuna canning machine <NUM>, while other parameters can be automatically generated (automatic registration). Parameters can include:.

The right column of <FIG> shows different parameters obtained from the production:.

In the example in <FIG>, the parameters that are susceptible to prescription are highlighted from those that are fixed and, therefore, not susceptible to prescription. The parameters that are amenable to prescription are mostly machine parameters <NUM>, although an end product parameter <NUM>, the optimum target pack weight, can also be prescribed. In addition, the parameters related to the raw material <NUM> could also be recommended, although their modification would have to be done manually if it can be carried out. For example, recommending the introduction of tuna at a certain temperature, or recommending the purchase of tuna of a specific species or origin, depending on the product to be manufactured.

Claim 1:
An adaptive tuna canning system, comprising:
a tuna canning machine (<NUM>) comprising a control unit (<NUM>) in charge of controlling the machine during the tuna canning process of a production; and
an optimization unit (<NUM>) comprising a data processing unit (<NUM>) configured to:
collect a production data history (<NUM>) formed by a plurality of past production data (<NUM>) captured by at least one training tuna canning machine (<NUM>) during the tuna canning process in different past productions;
receive input parameters (<NUM>) from a new production of the tuna canning machine (<NUM>);
establish a target function (<NUM>) for the new production of the tuna canning machine (<NUM>); and
calculate, from the historical production data (<NUM>), optimal operating parameters (<NUM>) of the tuna canning machine (<NUM>) for the new production by optimizing the target function (<NUM>);
and wherein the tuna canning machine (<NUM>) is configured to execute the canning of tuna in the new production based on the optimal operating parameters (<NUM>).