Method for dynamically adjusting the control of fluid extraction from a product

Abstract

Method for dynamically adjusting the extraction control parameters of products stored in a chamber: - an initial phase (100) comprising: . determination of a target weight loss profile (101) for the product, . determination of the corresponding extraction climatic conditions (102); - an extraction phase (200) comprising: a / application of the climatic conditions (201); b / measurement of quantitative data (202); c / establishment of the actual weight loss profile (203); d / establishment of a predictive weight loss profile (204) based on the quantitative data; e / automatic adjustment of the climatic conditions (205) if a predefined deviation between the target profile and the actual or predictive profile is observed, and application of these adjusted climatic conditions; f / implementation of steps b / to e / until the end of the extraction cycle. Figure for the abstract: Fig 6

Description

Title of the invention: Method for dynamically adjusting the control of fluid extraction from a product technical field

[0001] The present invention relates to a system and a method for dynamically adjusting the control of fluid extraction, such as drying or draining, of products.

[0002] The invention finds a particularly advantageous application for the drying or draining of food or non-food products, such as meat, dairy, or vegetable products, such as cured meats, cheeses, fruits, vegetables, wood, etc. STATE OF THE ART

[0003] In the field of the agri-food industry, the drying of products such as charcuterie or cheese, or the draining of dairy products, are complex and delicate processes.

[0004] For example, in the case of drying in the processed meat sector, drying must be carried out under precise temperature and humidity conditions. In particular, industrial drying of processed meats is carried out using dedicated equipment such as a drying cabinet or chamber, in which the climatic drying conditions are controlled throughout the drying process.

[0005] In practice, the drying chamber, in which the products to be dried are stored, is equipped with devices for controlling and regulating the chamber's climatic conditions throughout the various drying cycles. These drying conditions are predefined by the operator according to the type and characteristics of the product to be dried.

[0006] The drying chamber is typically equipped with sensors to monitor and control in real time the drying parameters, such as chamber temperature, humidity, ventilation, etc., based on the instructions entered by the operator.

[0007] Some drying chambers may be equipped with systems for detecting deviations in the evolution of drying parameters, which alert the operator via a light or sound signal, so that the operator can manually modify the drying parameters according to the deviations observed.

[0008] Thus, traditionally, the operator must monitor the drying state of the products and manually adjust the drying parameters to obtain a product that meets the specifications. The quality of the final product therefore depends essentially on the expertise of the operators and the manual adjustments based on visual and olfactory observation. This practice leads to variations in the quality of the final product as well as significant energy consumption.

[0009] In the case of dairy products (particularly cheeses), draining essentially consists of separating the curd from the whey. The draining phase is a crucial step as it determines the texture, structure, and, to some extent, the flavor of the final product. Generally, draining instructions vary depending on the type (fresh cheese or soft cheese, hard cheese, etc.) and the desired characteristics (creamy, firm, etc.). The draining phase is usually carried out in draining rooms that also provide a controlled environment in terms of temperature and humidity. Furthermore, as with drying, the draining instructions are generally adjusted manually by the operator during the draining process, so the final quality of the product will depend primarily on the operator's expertise and manual adjustments based on visual and olfactory observations.Generally, the operator manually and occasionally measures the pH of the product, visually assesses the whey, etc. This practice therefore also leads to variations in the quality of the final product as well as significant energy consumption.

[0010] For hard cheeses, the draining stage may be followed by a drying period to reduce their water content and prepare the rind before ripening. This drying may also be carried out in a temperature and humidity controlled environment.

[0011] Thus, in currently implemented processes, the draining of products such as cheese relies on traditional practices combining the monitoring of environmental parameters (temperature, relative humidity, ventilation), manual and spot checks (pH, visual and olfactory observations), and the use of standardized curves to model the draining or acidification processes. However, these approaches have limitations because the fixed models do not take into account real-world variations such as differences in weight loss or pH. Furthermore, the standardized curves do not incorporate human observations, such as the color or clarity of the whey, into the numerical systems. Finally, they result in high energy consumption, particularly due to ventilation not adjusted to the actual whey flow, which generates additional costs.

[0012] The present invention addresses this need for more adaptive and efficient solutions. PRESENTATION OF THE INVENTION

[0013] The present invention therefore relates to improving the process of controlling the drying or draining of a product, by proposing a solution allowing to automate the regulation of drying or draining conditions. The present invention proposes, in particular, a solution for automatically and dynamically adjusting drying or draining conditions throughout the various drying or draining cycles.

[0014] Hereafter, expressions such as "weight reduction in fluid", "extraction", "fluid extraction", "fluid reduction", "fluidic reduction", "dehydration", will refer indifferently to drying or draining, the fluid being water, whey, etc.

[0015] To achieve this, the invention implements real-time modeling of the fluid extraction profile (drying or draining for example), with automatic adjustment of the parameters relating to fluid extraction throughout the extraction cycle in order to achieve product quality, and to optimize the energy efficiency of the extraction cycles.

[0016] The invention thus relates to a method for dynamically adjusting the control parameters for the extraction of fluid in products, such as those based on plants, meat or milk, stored in a chamber (for example, a drying or draining chamber), throughout the different fluid extraction cycles.

[0017] The invention also relates to a system configured to implement the dynamic adjustment process, and a drying control method based on the dynamic adjustment process.

[0018] According to the invention, the method for dynamically adjusting the control parameters of the extraction of products stored in an extraction chamber, throughout the different extraction cycles, the extraction including drying or draining, comprises: - an initial phase of: . determination of a target weight loss profile based on at least the type of product, the initial weight of the product, the duration of the extraction cycle, and the target weight loss of the product at the end of the cycle, the target weight loss profile defining the theoretical evolution of the weight loss of the products during the extraction cycle, . determination of the climatic extraction conditions to be applied in the chamber throughout the extraction cycle responding to the theoretical evolution of the weight loss and the product quality; - an extraction phase following the target weight loss profile determined in the initial phase, during which the following actions are carried out: a / application in the chamber of climatic conditions corresponding to the target weight loss profile; b / real-time measurement throughout the extraction cycle of quantitative data relating to the extraction and including at least the actual weight loss, the temperature and humidity level of the chamber, and the ventilation speed; c / real-time establishment of the actual weight loss profile; d / real-time establishment of a predictive weight loss profile based on current quantitative data; e / automatic adjustment of the climatic conditions to be applied in the chamber if a predefined deviation between the target profile and the actual profile is observed, or if a predefined deviation between the target profile and the predictive profile is observed; and application of these adjusted climatic conditions for the duration of the remaining cycle; f / implementation of steps b / to e / until the end of the extraction cycle.

[0019] The automatic adjustment step e includes the application of one or more corrective actions including the adjustment of the device settings impacting the temperature in the chamber, the ventilation speed, the humidity in the chamber, the cycle duration, etc.

[0020] In other words, the dynamic adjustment method comprises: An initial phase of determining a target weight loss profile and determining the climatic conditions to be applied in the chamber throughout the extraction period that meet the target weight loss profile.

[0021] The determination of the target weight loss profile takes into account, in particular: - the type of product (to be dried or drained), and possibly the fluid extraction characteristics (drying or draining) induced by the composition of the product; - the initial weight of the product; - the duration of the extraction cycle; and - the target weight loss of the product cumulative at the end of the extraction cycle.

[0022] The target weight loss profile defines the theoretical evolution of the (fluid) weight loss of the products during the extraction cycle. The climatic conditions to be applied (temperature, humidity, ventilation speed, etc.) in the chamber throughout the extraction period correspond to the theoretical evolution of weight loss.

[0023] Following this initial phase, an extraction phase, following the target weight loss profile determined in the initial phase, is implemented during which the following actions are carried out: - application in the chamber of climatic conditions corresponding to the target weight loss profile; - real-time measurement throughout the extraction cycle of quantitative data relating to fluid extraction, and real-time establishment of the actual weight loss profile; - determination of a predictive weight loss profile for the duration of the remaining cycle if the climatic conditions corresponding to the target weight loss profile are maintained; - automatic adjustment of the climatic conditions to be applied in the chamber if a predefined difference between the target profile and the actual profile is observed, or if a predefined difference between the target profile and the predicted profile is observed; and - application of these adjusted climatic conditions.

[0024] Quantitative data relating to the ongoing fluid extraction include, for example: - the actual weight loss of the product; - the room temperature; - the relative humidity of the room; and / or - ventilation speed, etc.

[0025] The model representing the target weight loss profile over time can be defined by the following general equation: P(t) = Pcy(^)

[0026] Where P(t) is the weight loss at time t; Pcy is the weight loss of the product at the end of the extraction cycle; tcy is the extraction cycle time; and r is a time constant related to the properties of the product and the extraction conditions.

[0027] The model representing the target weight loss profile over time can also be defined by a polynomial, logarithmic, multi-exponential type model.

[0028] According to one embodiment, the extraction can be draining, and the quantitative data further include the acidity of the product, via continuous measurement of the pH in the product, for example directly in the core of the dairy product or in a representative solution.

[0029] Thus, in the case of a draining cycle, for example of dairy products: - the initial phase further includes the determination of a target acidity profile, the climatic conditions to be applied being determined to meet both the target weight loss profile and the target acidity profile; - the extraction phase also includes real-time pH measurement and real-time establishment of the actual acidity profile, as well as the establishment of the predictive acidity profile if the applied climatic conditions are maintained; - Automatic adjustment of climatic conditions is carried out if a predefined difference between the target acidity profile and the actual acidity profile is observed, or if a predefined difference between the target acidity profile and the predicted acidity profile is observed, with the adjustment of climatic conditions responding to both the target weight loss profile and the target acidity profile; and - application of these adjusted climatic conditions.

[0030] The target acidity profile defines the theoretical evolution of the pH level of the product during the extraction cycle.

[0031] The asymptotic decay model relating to the target acidification profile can be defined by: pH ^pH x ( 1 target z imitai max v 7

[0032] Where pHcibie(t) is the pH at time t; pHinitiai is the initial pH value at the beginning of the draining process; ApHmax is the maximum expected variation of pH during a theoretical cycle of infinite duration (t —> +oo); tph is a time constant, influencing the rate of acidification, and is relative to the properties of the product to be drained and the draining conditions.

[0033] The adjustment of the climatic conditions to be applied may include the application of one or more corrective actions, such as the adjustment of the parameters or instructions of the devices impacting the climatic conditions in the chamber and therefore the evolution of the quantitative data, for example the temperature in the chamber, the ventilation speed, the humidity in the chamber, the duration of the cycle, etc.

[0034] Advantageously, the automatic adjustment step e / of climatic conditions can also take into account qualitative data relating to the operator's perceptions of the products during fluidic extraction. For example, in the case of sausage, qualitative data may include crusting, firmness, color, etc. For example, in the case of dairy products, qualitative data may include the visual appearance of the whey (color, clarity, visible deposits), the condition of the product (texture, firmness, or other sensory indicator), olfactory perception, etc.

[0035] In alternative versions, depending on the available sensors or analyzers, qualitative data can be included within the quantitative data. For example, the visual appearance of whey can be determined via a suitable optical sensor, the condition of the dairy product can be determined via a suitable analyzer, and the olfactory perception can be determined via sensors such as olfactometers, electronic noses or any suitable analyzer.

[0036] Thus, in practice, the dynamic adjustment process can also include: - during the initial phase, a weighting is assigned to each of the qualitative data, during the initial phase, according to the quality of the product to be obtained; and - during the extraction phase, all or part of the weightings are adjusted throughout the extraction cycle according to the observed difference between the actual weight loss and the target weight loss, and / or indications relating to the qualitative data entered by the operator corresponding to the operator's perception of the product for which fluid extraction is in progress; - during the extraction phase, the dynamic adjustment of the climatic conditions to be applied throughout the cycle also takes into account the evolution of qualitative data.

[0037] The level or weighting reflects the importance or criticality of the qualitative data on the quality of the final product (dried or drained) or on the extraction process.

[0038] In one variant, the initial phase may further include: - the identification of qualitative data relating to the type of products and corresponding to the operator's feelings or observations on the products for which fluid extraction is underway; and - the assignment of a level or weighting to each of the qualitative data, depending on the quality of the product to be obtained.

[0039] The extraction phase may also include the adjustment of all or part of the weightings, throughout the extraction cycle according to the observed difference between the actual weight loss and the target weight loss, and / or indications relating to qualitative data entered by the operator corresponding to the operator's perception of the product for which fluid extraction is in progress.

[0040] The input entered by the operator may take the form of an evaluation or rating of the product by the operator for each of the qualitative data points, for example, in the form of weightings, scores, ratings, indices, or the positioning of a cursor on a rating scale. The system may include an interface configured to allow the operator to enter this data.

[0041] Advantageously, the predictive profile is determined by taking into account qualitative data.

[0042] Thus, depending on the indications (and therefore the feelings) entered by the operator, the system can adjust all or part of the weightings assigned to the qualitative data.

[0043] In addition, when one or more assigned weights are modified, the system implements one or more predefined corrective actions associated with the qualitative data for which the weights are modified.

[0044] Corrective actions may include adjusting humidity, ventilation, chamber temperature, etc. The weighting of each qualitative data point can thus be dynamically adjusted based on the observed weight loss difference and operator feedback. In practice, the dynamic adjustment of climatic conditions to be applied throughout the cycle therefore takes into account changes in both quantitative and qualitative data.

[0045] Advantageously, the determination of the target weight loss profile and / or the target acidity profile during the initial phase is based on weight loss profiles and / or the target acidity profile of previous cycles.

[0046] Advantageously, the extraction phase may include a time-delay period during which no deviation calculation is performed, and the measured quantitative data are recorded but not compared to the target curves.

[0047] Advantageously, the dynamic adjustment process can further include the optimization of energy consumption related to the extraction cycle, consisting of: - measuring energy consumption in real time, including both electrical and thermal consumption; - calculate the energy efficiency defined by the ratio of weight loss per kWh consumed; - Depending on the difference between the calculated energy efficiency and the predefined minimum acceptable energy efficiency ratio, implement measures to optimize energy efficiency. Such measures must include at least one of the following actions: automatic shutdown of the extraction cycle, or adjustment of the ventilation. Brief description of the drawings

[0048] The present invention and its advantages will become more apparent from the following description of several embodiments given by way of non-limiting examples, with reference to the accompanying drawings.

[0049] [Fig. 1] is a graph illustrating a target weight loss profile, a profile of the actual evolution of weight loss, and a dynamic prediction profile of weight loss, in the particular case of sausage drying and according to an embodiment.

[0050] [Fig.2] is a graph illustrating a target weight loss profile and a profile of the actual evolution of weight loss, in the particular case of sausage drying and according to a method of implementation.

[0051] [Fig.3] is a graph illustrating the evolution of the measurement of the weight of condensed water hour by hour during a sausage drying cycle and according to an embodiment.

[0052] [Fig.4] is a graph illustrating the upper and lower tolerances around the target profile of weight loss, in the particular case of sausage drying and according to a method of implementation.

[0053] [Fig.5] is an example of a user interface adapted to the delicatessen sector, according to one embodiment.

[0054] [Fig.6] is a simplified flowchart illustrating the major steps of the dynamic adjustment process according to one embodiment. DETAILED DESCRIPTION Variant #: *** DRYING ***

[0055] The different steps implemented by the system according to one embodiment are detailed below, in the particular case of sausage drying, it being understood that the general principle applies in the case of drying all meat, dairy or vegetable products.

[0056] Step 1: Phase of determining the target weight loss kinetics

[0057] Before the start of each drying cycle (initial phase 100 of [Fig. 6]), the The system determines a theoretical or predicted target weight loss profile (step 101), for example in the form of a predicted target weight loss curve. This target profile is established based on the specific characteristics of the product to be dried. For example, in the case of sausage, the target profile can be established by taking into account: - the type of product (e.g., sausage) which determines the drying behavior based on the composition of the product to be dried (moisture, fat content); - the initial weight of the product to be dried; - the duration of a drying cycle (for example, generally 10 to 30 days for sausages); and - the weight loss target, for example in the form of a percentage relative to the initial weight (e.g. 30% weight loss).

[0058] The system establishes in particular a target curve of the theoretical or predicted evolution of the weight loss during the entire duration of the drying cycle.

[0059] The system can also take into account historical data from previous drying cycles, to refine the predicted target curve.

[0060] In other words, before the start of each drying cycle, a target weight loss curve is generated based on the characteristics of the product to be dried (initial weight, composition, initial moisture content, fat content, etc.), the theoretical drying time, and the target weight loss (for example, 30% in 20 days). This information allows for the definition of the evolution of the drying climatic conditions to be implemented (such as temperature and humidity) throughout the drying cycle. This target weight loss curve is theoretical or predictive.

[0061] In practice, the model representing weight loss over time can be defined by the following general equation: P(t) = / -<,,(■£?)

[0062] Where P(t) is the weight loss at time t; Pcy is the weight loss of the product at the end of the extraction cycle; tcy is the duration of the extraction cycle; and r is a time constant related to the properties of the product and the extraction conditions.

[0063] This weight loss model can be applied to any product to be dried or drained.

[0064] This model thus describes how weight loss evolves over time according to the product's characteristics. The exponential law reflects the fact that water loss is rapid at the beginning, then slows down over time. The time constant r depends on the characteristics of the product to be dried and influences the rate of this evolution: the lower it is, the faster the weight loss. Thus, the time constant r can be adjusted according to the product's specific characteristics. For a product that loses moisture more slowly, the value of r can be higher. In practice, the constant r is initially estimated and then learned, for a given product, from previous drying cycles.

[0065] The model defined above assumes that the weight loss of the product to be dried is constant. This is particularly true for sausages, whose weight loss generally follows a decreasing exponential trend, typical of drying processes. Initially, the loss is faster because free water is easily removed. Over time, the rate of loss slows down because removing the bound water from the product becomes more difficult. This phenomenon is well illustrated by documents relating to sausage drying, where the initial drying phases remove a large amount of water, and the final phase focuses on removing residual moisture.

[0066] In figures 1, 2 and 4, the Co curve is an example of a theoretical target weight loss profile for sausage drying.

[0067] In the case of uneven weight loss over time, other more complex models (polynomial, logarithmic, multi-exponential) can be used to adjust the shape of the curve. The following second-order polynomial model is given as an example:

[0068] With: P(t) is the weight loss at time t; Pcy is the weight loss of the product at the end of the extraction cycle; tcy is the duration of the extraction cycle; and x (%) is the ratio between the water loss at the end of the cycle (kg / h) and the water loss at the beginning of the cycle (kg / h). This ratio is relative to the product properties and the extraction conditions

[0069] . The system determines the evolution of the drying climatic conditions to be implemented during the drying cycle to achieve the target profile (step 102), as well as the corresponding instructions or controls for the devices or means equipping the chamber or coupled to the chamber that have an impact on these climatic conditions. These climatic conditions are, for example: temperature, humidity, ventilation speed, etc.

[0070] Step 2: Real-time monitoring of quantitative data and continuous adjustment of climatic conditions

[0071] Once the theoretical target weight loss profile is determined, an extraction phase 200 ([Fig. 6]) is activated: drying under the climatic drying conditions corresponding to the theoretical target profile is implemented (step 201), and the system continuously measures and collects quantitative drying data throughout the drying cycle (step 202). The quantitative data may include any data measurable without operator intervention, by means of a sensor, analyzer, or any suitable measuring module.

[0072] These quantitative data include, in particular: - the actual weight loss of products stored in the drying chamber (via sensors measuring condensed water for example); - the room temperature; - the humidity level in the room, etc.

[0073] For example, the system can be coupled to suitable sensors, devices or modules configured to continuously measure throughout the drying cycle: - the weight loss of the stored products, for example by measuring the condensation of water present in the chamber; and - so-called "climatic" parameters (or climatic variables), that is to say those relating to the environment in the drying chamber which have an impact on drying stored products, such as room temperature, room humidity, ventilation speed, etc.

[0074] From this quantitative data, the system establishes in real time the actual weight loss profile (step 203). In Figures 1 and 2, the curve Ci illustrates an example of the actual evolution of the weight loss of the stored products during the first days of drying.

[0075] The evolution of the water loss via the measurement of condensed water is illustrated in [Fig.3]: the ordinate axis is relative to the volume of condensed water V(Liter), and the abscissa axis is relative to time t.

[0076] These quantitative data are compared to the predicted target profile. In particular, the actual weight loss can be compared to the predicted target profile. The system can also establish a predictive weight loss profile (step 204), for the remaining drying cycle time, based on the current quantitative data. The detection of a predefined discrepancy between the actual (current) or predicted (end of cycle) weight loss and the theoretical curve triggers the adjustment of all or part of the climatic parameters (step 205 with a return to step 201).

[0077] In practice, as illustrated in [Fig.4], the high tolerance curve Th and the low tolerance curve TB are defined. When the deviation is outside these tolerances, the system adjusts the climatic parameters.

[0078] For example, if a drying delay relative to the theoretical drying time or an advance in drying relative to the theoretical drying time is identified, the system is configured to automatically adjust the climatic parameters (ventilation, humidity, temperature) in order to maintain the actual weight loss profile substantially close to the theoretical weight loss profile. The automatic adjustment results in the implementation of one or more corrective actions.

[0079] In practice, the predictive control law applied by the system to adjust the climatic variables when a deviation in weight loss is detected can be given by: AP(t) = PréeI(t) - PcMe^ u(t)^ Kp. AP(t) + KifQAP(T)dr

[0080] Where Pcibie(t) is the projected target weight loss; Préei(t) is the actual weight loss; AP(t) is the difference between the actual weight loss Préei(t) and the expected weight Pcibie(t); u(t) is the corrective action applied by the system; Kp is a proportional gain, predefined to adjust the instantaneous gap; and K; is an integral gain, predefined to correct for the accumulation of the gap over time.

[0081] Thus, this approach ensures that the system remains close to the target trajectory by reacting quickly and appropriately to detected variations. Based on the detected deviation AP(t), the system adjusts its prediction of the drying profile for the remainder of the cycle.

[0082] In practice, these adjustments can also be influenced by the operator's observations of the product's evolution (color, firmness, crusting), which enrich the product modeling. Thus, the adjustment of the climatic parameters or conditions to be applied can take into account the operator's observations. Step 3: Integrating the operator's feedback

[0083] The system may include a user interface that allows the operator to adjust perception sliders to refine the system's prediction. These sliders allow for the integration of qualitative data relating to the product during the drying process.

[0084] For example, for sausage drying, such quantitative data may include: - crusting: if the surface of the product dries out too quickly, the humidity or ventilation should be adjusted; - Firmness: allows monitoring of the evolution of the product's internal texture. A texture that is too firm indicates drying too quickly, implying the need to adjust the humidity and temperature of the chamber.

[0085] In practice, the weight loss profile taking into account qualitative data can be defined as follows: P is correct) ~ Theoretical P ( O + feeling ( )

[0086] Where PajuSté(t) is the adjusted prediction (or profile) of weight loss at time t, taking into account qualitative data; Ptheoretical(t) is the initial prediction (or profile) of weight loss; and Fressentie(ri) is a corrective function dependent on the feeling q relative to a qualitative data i.

[0087] This solution allows the visual and olfactory observations of the operator to be integrated into predictive adjustments, thereby increasing the reliability and accuracy of the system to maintain product quality.

[0088] In practice, a weighting is assigned to each qualitative data, this weighting being adjusted dynamically during drying, and participates in the adjustment of climatic conditions.

[0089] An example of weighting rules to integrate the operator's feelings is described below, in the case of sausage drying.

[0090] The integration of the operator's feelings into the control system makes it possible to combine qualitative data (such as firmness, crusting or color) with quantitative data (temperature, humidity, weight loss), in order to maximize product quality and allow automatic correction in case of deviations.

[0091] To ensure optimal corrective actions, weighting rules for the different sensory factors are defined.

[0092] A sensory factor corresponds to a qualitative data point relating to the type of products to be dried. The value of the sensory factor represents the operator's perception or observation of the products during the drying process, after visual and / or olfactory evaluation.

[0093] Sensory factors or qualitative data that may be taken into account: The main sensory or perceptual factors that the operator can adjust include, for example: - Crusting: is an indicator of the rate of formation of the dry outer layer on the product. - Firmness: is an indicator of the internal texture of the product (soft or firm). - Colour: is a visual indicator of the proper progress of maturation. - Casing adhesion: is an assessment of the connection between the skin and the meat of the product. - Odor: indicates a good fermentation process or contamination. - Product shape and uniformity: indicates any deformation of the product during drying.

[0094] Rules for weighing feelings: Each sensory factor (or perception) has a different influence on the quality of the product, and its weighting is adjusted according to its relative importance to the proper progress of the drying process.

[0095] By way of example, weightings initially defined for each of the sensory factors may be: - Crusting: 30% - Firmness: 25% - Color: 20% - Casing adhesion: 15% - Odor: 5% - Shape and uniformity: 5%

[0096] These weightings are defined in particular according to their direct impact on product quality. For example, poor crust formation can quickly affect the entire drying process by preventing uniform dehydration of the product, which justifies its high weighting.

[0097] Adaptive weighting rule based on deviations and previous cycles: The objective is to allow the system to modulate these weightings during the drying cycle based on the deviations observed between the actual weight loss curve and the target curve. This adaptive mechanism makes it possible to give more importance to certain factors at critical points in the cycle.

[0098] The weighting evolves dynamically according to the observed differences and the feelings of operators.

[0099] Adaptive weighting algorithm: H',- (t) =wi0+ a - &P(J) + 0-

[0100] Where Wi(t) is the weighting of factor i (crusting, etc.) at time t; Wio is the initial weighting of factor i (e.g., 30% for crusting); AP(t) is the difference between the actual weight loss and the target weight loss at time t; p is the operator's perception of factor i; and a and [3] are weighting coefficients adjusting the influence of the differences and operator perceptions.

[0101] Example of a weighted adjustment: If, after 10 days of drying, the difference in weight loss is significant (e.g. AP(10) > 5%), the system can increase the weighting of feelings related to crusting and firmness, as these factors are critical to understanding the causes of delay or advancement in the process. The new weighting of crusting could increase to 40% if the operator indicates that the product is too dry on the surface, and that of firmness to 30%.

[0102] Step 4: Implementation of automatic corrective actions Once the weightings are adjusted, the system proposes or implements automatic corrective actions based on the most influential feedback and measured discrepancies. These actions are tailored to correct the problems identified by the operator.

[0103] Corrective action activation rule: For each perceived value (r), a corrective action is triggered if: Action,- = 1, if w^t) > threshold,■ 0, if w{(t) > threshold.

[0104] Or threshold; is a predefined weighting threshold for each factor i.

[0105] Thus, if the adjusted weighting w / t) exceeds this threshold, the corrective action associated with this feeling is automatically triggered.

[0106] Examples of automatic corrective actions: - Crusting too quickly: the weighting of crusting is adjusted to 40% (wcrusting = 40%). Corrective action: increase humidity by 5% and reduce ventilation by 15% to slow down crust formation.

[0107] - Insufficient firmness: the firmness weighting is adjusted to 30% (wfirmness=30%) Corrective action: increase ventilation by 10% to accelerate internal drying.

[0108] - Colour too pale: the relative colour weighting is adjusted to 25% (wcouieUr (25%) Corrective action: increase the temperature by 2°C to promote more even ripening and accelerate color development.

[0109] Concrete cases of adjustments based on observed discrepancies and feelings, in the case of sausage drying, will be described below.

[0110] Case 1: Significant delay in actual weight loss profile - Pessimistic prediction [YES] Scenario: The drying cycle of a 10 kg sausage begins. The target curve predicts a 20% weight loss after 10 days. However, after 5 days (t = 5), the actual weight loss is only 7%, whereas the theoretical curve predicted 10%. This represents a delay in weight loss. The system can signal this delay to the operator. The operator indicates that the product is still too soft and the color remains too pale. The system translates the operator's feedback into parameters to be adjusted, for example, adjusting the ventilation because the product is too soft to make up for the weight loss deficit.

[0112] Action of the system or digital twin: The system detects a delay in weight loss. The system incorporates the operator's perception, adjusts the prediction to anticipate a possible persistent delay. The system proposes to increase ventilation and reduce humidity to accelerate evaporation. - Discrepancy detected in weight loss: AP(5) = -0.3 kg - Adjusted prediction: The model now predicts a final weight loss of 28% instead of 30%, at the end of the drying cycle, unless adjustments are made. - Correction proposed or implemented by the system: increase ventilation by 15% and reduce humidity from 85% to 80%.

[0113] Expected result: This adjustment allows us to gradually catch up. After 10 days, the actual weight loss returned in line with the adjusted prediction, and the system continues to refine its calculations based on changes in measurements and operator feedback.

[0114] Case 2: Observed advance of the actual weight loss profile - Optimistic prediction

[0115] Scenario: Another cycle starts with a target of 30% weight loss for a similar product. After 10 days (t = 10), the system detects that the weight loss is 22%, whereas the target curve predicted 20%. The operator confirms that the firmness is good and that the product has taken on an appropriate color.

[0116] Action of the system or digital twin: The system detects a lead over the target curve and readjusts the prediction to forecast potentially faster weight loss than expected. By incorporating positive feedback from the operator (satisfactory firmness and color), the system proposes to maintain the current pace with a slight reduction in ventilation to avoid excessive drying. - Discrepancy detected in weight loss: AP(10) = +0.2kg - Adjusted prediction: the final weight loss is readjusted to be achieved in 18 days instead of 20 days - Correction proposed or implemented by the system: maintain ventilation but reduce the temperature by 2°C to slightly slow down the process and stabilize the quality.

[0117] Expected result: The product maintains good quality, and the weight loss curve follows the readjusted model.

[0118] The objective is achieved with a confirmed lead, allowing the cycle to be completed two days earlier, with energy savings.

[0119] Case 3: Advance observed, then accentuated - Adjusted prediction

[0120] Scenario: For a 15 kg product with a target weight loss of 35%, the system predicts a loss of 10% after 5 days. However, the actual weight loss already reaches 12%, and the operator notes that the crusting is too rapid and the product seems too firm.

[0121] Action of the system or digital twin: The system detects a significant advance in weight loss. Taking into account the operator's perception (rapid crusting, excessive firmness), the system adjusts its prediction to anticipate an imbalance in product quality if the drying process continues at this rate. The system proposes or implements an increase in humidity and a reduction in ventilation to prevent excessive surface drying. - Initial deviation detected: AP(5)=+0.3kg - Adjusted prediction: the model predicts final weight loss achieved in just 16 days. - Proposed correction: increase the room humidity from 80% to 85% and reduce ventilation by 20%.

[0122] Expected result: By adjusting environmental or climatic parameters, the acceleration of the process is tempered, making it possible to avoid excessive drying and to maintain the expected quality of the product. The cycle ends according to the readjusted forecasts, guaranteeing a well-balanced product.

[0123] These three cases illustrate how the adjustment system reacts dynamically to the observed discrepancies between the theoretical curve and the actual data, while incorporating the operator's feedback to refine its predictions and adjustments. This system makes it possible to avoid excessive delays or advances in drying, while ensuring that the final product meets the quality criteria. Step 5: Integrated Energy Optimization

[0124] In addition to the dynamic adjustment of drying parameters, the process may further include the optimization of energy consumption related to drying. Energy consumption is measured in real time for each phase of the cycle, including both electrical (ventilation, compressors) and thermal (heating and cooling) consumption. The system calculates the energy efficiency ratio (kg of weight loss per kWh consumed) and automatically stops the drying cycle when this ratio becomes too low.

[0125] Energy efficiency law: The energy efficiency R(t) is defined by: „ . , pertdty'Ppei'tÀt'Ài)

[0126] Where R(t) is the moving average energy efficiency at time t, Pperte(t) is the cumulative weight loss of the product at time t, Etotai(t) is the total cumulative energy consumption (electrical + thermal) at time t. At is the predefined sliding time interval over which R(t) is averaged (example 1 day).

[0127] Automatic cycle stop condition: The system can interrupt the drying cycle when: R(t) < RseuU Rseuii represents a minimum acceptable energy efficiency ratio. This model guarantees efficient energy management, allowing for cost optimization while maintaining product quality.

[0128] Step 6: Learning and complete automation

[0129] The system can use historical data to improve its predictions over time. Through machine learning, future drying cycles are optimized by dynamically adjusting dryer parameters. Automation evolves towards autonomous management where the operator intervenes only in case of anomalies.

[0130] As the system or digital twin collects data on feelings and corrective actions taken, a machine learning module, also known as "machine learning" in the English-language literature, can dynamically adjust the weightings and activation thresholds based on the results of previous cycles. The system optimizes its performance over the course of the cycles through a learning loop.

[0131] In addition, the system can automatically adjust the weighting coefficients a and [3 to modulate the influence of deviations and feelings, depending on the successes or failures of previous corrective actions.

[0132] Thanks to this learning loop, the system thus becomes able to predict more accurately which correction will be most effective depending on the specific conditions of the product and the cycle.

[0133] The adaptive sensory weighting system prioritizes critical sensory factors while automatically adjusting the drying process to maintain product quality. Integrating these sensory perceptions, combined with measured deviations from the target curve, enables personalized control and automatic activation of corrective actions. Through machine learning, the system continuously refines the weightings and corrections, making the drying process increasingly efficient with each cycle. Example of an operator interface

[0134] An example of a user interface 1 adapted to the delicatessen sector is illustrated in [Fig. 5]. This user interface 1 allows the operator to enter parameters enabling the system to determine the theoretical or predicted target weight loss profile 10. This interface allows the operator in particular to define: - the maximum capacity of the chamber 11 (in kg); - the type of product 12 (e.g., sausage) which determines the drying behavior according to the composition of the product to be dried (moisture, fat); - the duration of cycle 13 (in hours); - the chamber load at the start of cycle 14 (in kg); - target weight loss 15 (in %); - the upper and lower tolerances 16 (in %); - the threshold of the energy ratio 17 (in Wh / kg).

[0135] Of course, the system can automatically determine the values ​​of certain parameters based on previous drying cycles, while still allowing the operator to make adjustments. For example, when the operator enters "sausage" as the product type 12, the system can suggest values ​​to the operator regarding the target weight loss 15, tolerances 16, and the energy ratio threshold 17.

[0136] User interface 1 can also display: - time-related information about drying such as start of cycle 18, end of cycle 19, time elapsed 20, time remaining 21, etc. - information relating to the drying process such as current load rate 22, current weight loss 23, current difference 24 between target weight loss and actual weight loss, current energy ratio 25, predicted weight loss at the end of the cycle 26, prediction of the difference at the end of the cycle 27, prediction of the ratio at the end of the cycle 28.

[0137] The user interface 1 can also allow the operator to evaluate qualitative data, or to indicate his feelings 30 concerning, for example, homogeneity 31, blooming 32, crusting 33, firmness 34, color 35, adherence to casing 36, odor 37, or variability in weight loss 38.

[0138] On the interface shown in [Fig. 5], the perceived temperature is captured by adjusting the sliders, the system translating the position of each slider into a weighting value. Variant #: *** DRAINAGE ***

[0139] The method of dynamically adjusting the control parameters for drying can be transposed to the draining process, for example to the draining of dairy products in which the weight loss results from the operation of separating the curd from the whey, for example by pressing.

[0140] Thus, in addition to the climatic data mentioned above for the drying process, such as the ambient temperature of the draining chamber, the ambient relative humidity, and the temperature of the cold loop, the adjustment process for draining can take into account additional data such as: - measurements relating to the flow of whey, namely the hourly flow (volume or flow rate at hourly intervals), and / or the cumulative volume of whey. - measurements relating to the pH of products, determined continuously, directly on a product (for example, at the core of the dairy product or in a representative solution).

[0141] Thus, following the principle of the adjustment process applied to drying described above, in the case of a draining cycle, the dynamic adjustment process for the control parameters of the draining of products stored in a draining chamber may include, in particular:

[0142] During the initial phase 100 ([Fig.6]): . determining the target weight loss profile (step 101) for the product to be drained; . the determination of the target acidity profile; . the determination of the climatic drainage conditions (step 102) throughout the drainage cycle meeting the target weight loss profile and the target acidity profile.

[0143] The target acidity profile defines the theoretical evolution of the pH level of the product during the draining cycle, pH being an indicator of the acidification and maturation of the product.

[0144] During the draining phase 200 and throughout the draining cycle:

[0145] . the application of the initial climatic conditions corrected by adjustments potentials determined in step 205 (step 201). . real-time measurement of quantitative data (step 202) including pH measurement, and real-time establishment of the actual weight loss profile (step 203) and the actual acidity profile; and . the determination of a predictive weight loss profile (step 204) and a predictive acidity profile for the remaining cycle duration if the initial climatic conditions are maintained; - the automatic determination of adjustments to be applied to the chamber's climatic conditions (step 205) if a predefined deviation between the target profiles and the actual profiles is observed, or if a predefined deviation between the target profiles and the predictive profiles is observed; and - the application of these adjusted climatic conditions (return to step 201).

[0146] The automatic adjustment of the climatic conditions to be applied in the chamber can also take into account the qualitative data entered by the operator.

[0147] Determination of the target weight loss kinetics

[0148] Before the start of each draining cycle, the system determines a theoretical or predicted target weight loss profile, for example in the form of a predicted target weight loss curve. This target profile is established according to the specific characteristics of the product to be drained. For example, in the case of dairy products to be drained, the target profile can be established taking into account: - the type of product to be produced and the associated recipe; - the initial weight of the product to be drained; - the duration of a draining cycle; - the weight loss target, for example in the form of a percentage relative to the initial weight.

[0149] The weight loss profile in the case of draining corresponds to an hourly flow profile (cumulative volume or hourly flow rate) of whey. The same model as that used for drying can be applied: P(t)=pj£)

[0150] Where P(t) is the weight loss at time t, this weight loss is relative to the cumulative flow rate or volume of whey; Pcy is the weight loss of the product at the end of the draining cycle; tcy is the duration of the draining cycle; and r is a time constant related to the properties of the product and the extraction conditions. Determination of acidity kinetics

[0151] A model for monitoring acidification could be: pH...(t) = pH. .. , - ^pH x( \ -e~tlTPH)

[0152] Where pHcibie(t) is the pH at time t; pHinitiai is the initial pH value at the start of the draining process (e.g., 6.5); ApHmax is the maximum expected variation of pH during the theoretical cycle of infinite duration (t —> +oo); rpH is a time constant (e.g. 10), influencing the rate of acidification, and is relative to the properties of the product to be drained (e.g. initial moisture, fat) and the draining conditions.

[0153] At the beginning of the process, the pH decreases rapidly, reaching about 60% of its maximum variation after a time equivalent to rpH.

[0154] Determination of climatic drainage conditions

[0155] The system determines the evolution of the dewatering climatic conditions to be implemented during the dewatering cycle to achieve the target weight loss and acidity profiles, as well as the corresponding instructions or controls for the devices or means equipping the chamber or coupled to the chamber that have an impact on these climatic conditions. These climatic conditions are, for example: temperature, humidity, ventilation speed, etc.

[0156] Real-time measurement of quantitative data and continuous adjustment of climatic conditions

[0157] Once the theoretical target profiles for weight loss and acidity are determined, drainage under the climatic conditions corresponding to the theoretical target profiles is implemented, and the system continuously measures and collects quantitative data relating to the drainage throughout the drainage cycle. The quantitative data may include any data measurable without operator intervention, by means of a sensor, analyzer, or any suitable measuring module.

[0158] These quantitative data include, in particular: - the actual weight loss of the products stored in the chamber, via the measurement of the cumulative whey flow volume or the whey flow rate; - the climatic conditions of the chamber, such as temperature, humidity level in the chamber, etc. - the pH of the products, via, for example, a fixed pH sensor installed in the chamber.

[0159] From these measured data, an actual profile and a predictive profile for weight loss and acidity are estimated. For example: - if ventilation is increased, the model projects an acceleration of weight loss in the next few hours. - if humidity is reduced, the model anticipates a stabilization or a slowing down of acidification.

[0160] Example of a predictive profile of weight loss adjusted for current climatic parameters: Projected ( t — Pre-adjustment AP ( ) AP has precisely (t) = ki>< ventilation + ^2 x humidity + x temperature

[0161] Where Pprojeté(t) is the predicted weight loss if the predicted climatic conditions are maintained; Préei is the actual weight loss measured at time t; ventilation is the measured airflow resulting from the ventilation of the room; humidity is the humidity level measured in the room; temperature is the temperature measured in the room; ki, k2 and k3 are coefficients that can be adjusted according to the characteristics of the product and historical responses.

[0162] Example of a predictive profile of acidity adjusted by environmental parameters: pH .dt) = pH+kpH. (t) 1 projected 7 r real r adjustment y 7 pH adjustment ( / ) = x temperature + k5 x humidity

[0163] Where pHprojeté(t) is the predicted pH if the forecast climatic conditions are maintained; pHrei is the actual pH measured at time t; temperature is the measured temperature of the room; humidity is the humidity level measured in the room; K4 and k5 are coefficients that can be adjusted according to the characteristics of the product and historical responses.

[0164] Actual or predicted profiles are compared to target profiles. In particular, the detection of a predefined discrepancy between actual or predicted weight loss and target weight loss triggers the adjustment of all or part of the climatic parameters. Similarly, the detection of a predefined discrepancy between actual or predicted pH and target pH triggers the adjustment of all or part of the climatic parameters.

[0165] In practice, for each of the target profiles, upper and lower tolerance curves are defined. When the deviation falls outside these tolerances, the system adjusts the climatic parameters.

[0166] By way of example, the threshold levels can be defined as follows: Thresholds for flow (weight loss): - Late: Negative difference > 5%. - Very late: Negative gap > 10%. - Ahead: Positive difference > 5%. - Very far ahead: Positive gap > 10%. pH thresholds: - Late: Deviation of -0.2 units from the target curve. - Very late: Difference of -0.4 units. - Ahead: Difference of +0.2 units. - Very far ahead: Difference of +0.4 units.

[0167] In practice, these thresholds are adjustable by the operator. For example, for a more delicate cheese, the thresholds can be reduced to 3% and 7% for runoff. These thresholds can also be dynamically adjusted based on a history of actual deviations observed during previous cycles.

[0168] The adjustment of climatic parameters includes in particular the identification of one or more corrective actions to be implemented manually by the operator or automatically by the system. Corrective actions

[0169] In practice, each deviation is linked to probable causes. For example, excessively rapid weight loss may be due to over-ventilation, while a pH that is too low could indicate an unsuitable temperature. The corrective actions proposed by the system must therefore be consistent, with a clear indication of priorities.

[0170] For example, when a discrepancy between the target and actual profiles is detected, the system can generate an alert message for the operator, suggesting manual corrective actions to be taken. For example:

[0171] - for weight loss: When a deviation corresponding to a delay in weight loss is detected, the system can inform the operator via the following message: "Delay detected in serum flow. Check the drainage channels and consider slightly increasing ventilation." When a deviation corresponding to an advance in weight loss is detected, the system can inform the operator via the following message: "Serum loss too rapid. Reduce aeration to slow the flow and avoid excessive dripping."

[0172] -for pH: When a deviation corresponding to a pH that is too low is detected, the system can inform the operator via the following message: "pH below the target curve. Check the temperature and humidity to stabilize the fermentation." When a deviation corresponding to a pH that is too high is detected, the system can inform the operator via the following message: "pH above target. Ensure that environmental conditions favor acidification."

[0173] In practice, weight loss and pH are interdependent parameters. Too rapid a flow can influence the pH (slowed acidification), and a delay in the flow can be accompanied by excessive acidification.

[0174] As a result, the system of the invention can identify priorities in corrective actions. For example: - If the flow is delayed and the pH is too low: the system may suggest correcting the flow first to avoid prolonged stagnation, but maintaining controlled humidity so as not to worsen acidification. - If the flow is too fast and the pH too high: the system may suggest prioritizing the reduction of ventilation to slow the flow, while slightly increasing the temperature to stimulate acidification.

[0175] These corrective actions can be performed manually by the operator, but the system can be configured to perform these corrective actions automatically.

[0176] Another example of actions when a discrepancy is detected:

[0177] Example 1: Simple case (flow or pH drift) Detection: Delay in serum loss detected. Suggested action: Slightly increase ventilation and check the condition of the drainage channels. The pH is within target range; no action is required.

[0178] Example 2: Complex case two drifts detected (flow and pH aligned) Detection 1: Too rapid serum loss. Proposed action: Reduce ventilation and maintain humidity to slow down the flow. Detection 2: pH above target. Proposed action: Slightly increase the temperature to stimulate acidification, and verify that the adjustment does not disrupt the balance of the process.

[0179] Example 3: Contradictory case (divergent flow and pH) Detection: Two deviations detected with possible conflicting causes: Delayed serum loss: the system proposes to increase ventilation and maintain humidity to accelerate flow; . pH too high: the system suggests slightly increasing the temperature to stabilize the acidification. The system recommends a priority in the corrective actions to be taken: First correct the flow if the product appears visually too wet.

[0180] Other examples in which prioritization takes into account the threshold level:

[0181] Example 1: A single (moderate) drift Message: "Serum loss is delayed (-6%). Slightly increase ventilation to compensate for the loss."

[0182] Example 2: Several (serious) deviations Message: "Two deviations detected: Very delayed serum loss (-12%): High priority. Significantly increase ventilation and check drainage channels. pH significantly elevated (+0.5 units): Reduce humidity to stabilize acidification.

[0183] Example 3: Drift conflict Message: "Two critical deviations with conflicting actions: Very delayed serum loss (-10%): Increase ventilation. . pH very low (-0.4 unit): Reduce ventilation and humidity. Recommended priority: Stabilize the pH first if the product's visual condition appears acceptable. Integration of the operator's experience

[0184] The system may include a user interface that allows the operator to enter qualitative data relating to the product being drained in order to refine the system's prediction. This qualitative data may relate to the visual appearance (clarity or opacity of the serum), the texture of the product (too soft, too firm), or the color (an indicator of maturation or quality).

[0185] For example, for the draining of dairy products, such qualitative data may relate to the product being drained, for example:

[0186] - Crusting: Visual and tactile observation to detect a crust that is too rapid or absent. Impact: Adjustments to humidity or ventilation.

[0187] - Firmness: . Tactile feel (texture too soft or too firm). Impact: Adjustments to balance internal drying.

[0188] - Color: Visual observation to identify delayed or accelerated maturation. Impact: Changes in temperature or humidity.

[0189] For example: Pale color: Indicates delayed ripening or excessive humidity. / Possible actions: Slightly increase the temperature (e.g., by 2°C) to accelerate ripening. Reduce the humidity (e.g., by 3%) to limit internal condensation. Normal color (expected shade): Process compliant. / Action: No adjustment necessary. Dark color: May indicate excessive humidity or uneven drying. / Possible actions: Check ventilation. Slightly reduce the temperature (e.g., by 1°C) to slow down excessive ripening.

[0190] Qualitative data may also relate to the whey flowing from the product. Qualitative data may, for example, relate to the visual appearance of the whey as well as its color.

[0191] Visual aspect: - Clarity: Clear: Indicates normal and efficient flow. / Action: No intervention required. Problem: May indicate an imbalance or partial blockage in the system. / Possible actions: Check the filters and flow channels. Slightly increase ventilation to accelerate flow.

[0192] Color: - Light yellow (normal): Balanced drainage process. / Action: No intervention required. - Dark yellow: Indicates possible serum stagnation or poor filtration. / Possible actions: Reduce humidity to limit filter saturation. Check flow in drainage channels. - Pale to dark green: Chemical imbalance (e.g., excess acidity or contamination). / Possible actions: Check temperature and humidity conditions. Check pH for abnormal acidification.

[0193] Qualitative data may also include spot pH measurements taken by the operator during manual sampling of products at key times. These spot measurements complement continuous data by providing human validation, especially when there is doubt about the sensors or for specific batches.

[0194] This qualitative data (or operator perceptions) is used to estimate predictive profiles. In particular, these perceptions directly influence the projected curves by adjusting the coefficients related to environmental conditions. The predictive model taking these perceptions into account can be: Projected P (^) — actual Adjustment^ + perceived')

[0195] WHERE fresenti is the correction factor specific to a qualitative data point q

[0196] As with drying, weighting rules can be defined for the different sensations. The climatic conditions to be applied are also adapted according to the qualitative data. For example, adjustment of environmental parameters (temperature, humidity), proposed changes in the next cycle.

[0197] Adjustment of certain constants to the next cycle

[0198] To ensure continuous improvement, the system can at the end of each cycle adjust some of the coefficients used in the predictive models.

[0199] Adjustment coefficient k, The coefficients kb k2, k3, k4 and k5 can be adjusted after each draining cycle: z __ z ( ! ] ^new ~ K old x \ 1 )

[0200] WHERE knouveau is the adjusted coefficient; Kncien is the coefficient of the previous cycle; Acumui is the difference between the predicted weight loss at the end of the cycle and the actual weight loss at the end of the cycle. The target weight loss is the target weight loss.

[0201] At the end of each cycle, the system compares the projections with the actual results to refine the coefficients (kb, k2, k3, k4, and k5). For example, if cloudy whey is systematically corrected by increasing the ventilation rate, the ventilation-related coefficient ki in the predictive model is adjusted to automatically predict this correction. For instance, if a 10% increase in ventilation results in a weight loss of +1.5% instead of the projected +2%, the ki coefficient is adjusted accordingly.

[0202] Constant adjustment T pH The rpH constant can also be adjusted. / ApH \ ~pH "pH X \ &pHmax I

[0203] Where ^meau is consjanje adjusted Tancien csl |a constant of the previous cycle A pH is the difference between the pH at the end of the cycle of the target kinetics and the actual pH at the end of the cycle.

[0204] A pHmax is the maximum expected variation of pH during a theoretical cycle of infinite duration (t —> +oo);

[0205] For example:

[0206] For the first cycle (cycle 1), rpH = 10, and the final pH at the end of cycle 1 is ahead by +0.4 units. The adjustment for the next cycle (cycle 2): rpH = 9.6.

[0207] For cycle 2: rpH = 9.6, and the final pH at the end of cycle 2 is lagged by -0.2 units. The adjustment for the following cycle (cycle 3): rpH = 9.8. Time delay integration

[0208] The system may incorporate a time delay period. During the time delay period, no deviation calculations or alerts are generated or performed. The measured values ​​(weight loss, pH) are recorded but are not compared to the target curves. The duration of this time delay period may be, for example, 60 minutes from the start of the cycle. The operator may be able to adjust this duration.

[0209] This time delay is particularly useful to avoid generating alerts when the system takes time to reach its optimal conditions after launch (e.g., stabilized humidity, regulated ventilation). Determining a confidence index

[0210] A confidence index based on cycle history can enhance the reliability of the system's recommendations. This can help operators prioritize adjustments most likely to resolve discrepancies without creating conflicts.

[0211] The confidence index is a score (e.g., from 0 to 100%) assigned to each proposed adjustment, based on: - Historical effectiveness: Frequency of similar adjustments that effectively corrected the gaps. - Consistency: No significant conflicts were observed with other parameters during adjustment.

[0212] The confidence index can be calculated using the following formula: * >r * j z'* / cas rcussis \

Claims

1. Demands A method for dynamically adjusting the control parameters of the extraction of products stored in an extraction chamber, throughout the different extraction cycles, the extraction including drying or draining, the dynamic adjustment method comprising: - an initial phase (100) during which the following actions are carried out: . determination of a target weight loss profile (101) based at least on the type of product, the initial weight of the product, the duration of the extraction cycle, and the target weight loss of the product at the end of the cycle, the target weight loss profile defining the theoretical evolution of the weight loss of the products during the extraction cycle, . determination of the climatic extraction conditions (102) to be applied in the chamber throughout the extraction cycle corresponding to the theoretical evolution of the weight loss; - an extraction phase (200) following the target weight loss profile determined in the initial phase, during which the following actions are carried out: a / application in the chamber of climatic conditions (201) corresponding to the target weight loss profile; b / real-time measurement throughout the extraction cycle of quantitative data (202) relating to the extraction and including at least the actual weight loss, the temperature and humidity level of the chamber, the ventilation speed; c / real-time establishment of the actual weight loss profile (203); d / real-time establishment of a predictive weight loss profile (204) based on current quantitative data; e / automatic adjustment of climatic conditions (205) to be applied in the chamber if a predefined deviation between the target profile and the actual profile is observed, or if a predefined deviation between the target profile and the predictive profile is observed, and application of these adjusted climatic conditions for the duration of the remaining cycle; f / implementation of steps b / to e / until the end of the extraction cycle.

2. Dynamic adjustment method according to claim 1, wherein the automatic adjustment step e / includes the application of one or more corrective actions including the adjustment of device setpoints impacting the temperature in the chamber, the ventilation speed, the humidity in the chamber, the cycle duration, etc.

3. A dynamic adjustment method according to claim 1 or 2, wherein the model representing the weight loss over time is defined by the following general equation: = Pey(^) Where P(t) is the weight loss at time t; Pcy is the weight loss of the product at the end of the extraction cycle; tcy is the extraction cycle time; and r is a time constant related to the properties of the product and the extraction conditions.

4. A dynamic adjustment method according to any one of claims 1 to 3, wherein the model representing the target weight loss profile over time is defined by a polynomial, logarithmic, or multi-exponential type model.

5. A dynamic adjustment method according to any one of claims 1 to 4, wherein the quantitative data further include the acidity of the product, via continuous measurement of the pH in the product.

6. A dynamic adjustment method according to claim 5, wherein the extraction cycle is a draining cycle, and: - the initial phase further comprises the determination of a target acidity profile, the climatic conditions to be applied being determined to meet both the target weight loss profile and the target acidity profile; - the extraction phase further comprises the real-time measurement of pH and the real-time establishment of the actual acidity profile, as well as the establishment of the predictive acidity profile if the applied climatic conditions are maintained; - automatic adjustment of the climatic conditions is carried out if a predefined deviation between the target acidity profile and the actual acidity profile is observed, or if a predefined deviation between the target acidity profile and the predictive acidity profile is observed, the adjustment of the climatic conditions meeting both the target weight loss profile and the target acidity profile; and - application of these adjusted climatic conditions.

7. A dynamic adjustment method according to claim 1, wherein the logarithmic model for tracking acidification is defined by: pH(t) = pH... - ApH x (le^p») ' target 1 initial ' max ' WHERE pHcibie(t) is the pH at time t; pHinitiai is the initial value of the pH at the start of the draining process; ApHmax is the maximum expected change in pH during a theoretical cycle of infinite duration (t —> +oo); tph is a time constant, influencing the rate of acidification, and is relative to the properties of the product to be drained and the draining conditions.

8. A dynamic adjustment method according to any one of claims 1 to 7, wherein the step e / of automatic adjustment of climatic conditions also takes into account qualitative data relating to the operator's feelings concerning the products during fluidic extraction.

9. A dynamic adjustment method according to claim 8, wherein: - during the initial phase, a weighting is assigned to each of the qualitative data, according to the quality of the product to be obtained; and - during the extraction phase, all or part of the weightings are adjusted throughout the extraction cycle according to the observed difference between the actual weight loss and the target weight loss, and / or indications relating to the qualitative data entered by the operator corresponding to the operator's perception of the product for which fluid extraction is in progress; - during the extraction phase, the dynamic adjustment of the climatic conditions to be applied throughout the cycle also takes into account the evolution of the qualitative data.

10. A dynamic adjustment method according to claim 9, wherein the predictive profile is determined taking into account qualitative data.

11. A dynamic adjustment method according to any one of claims 1 to 10, wherein the determination of the target weight loss profile and / or the target acidity profile during the initial phase is based on weight loss profiles and / or the target acidity profile of previous cycles.

12. A dynamic adjustment method according to any one of claims 1 to 11, comprising a time-delay period during which no deviation calculation is performed, and the quantitative data measured are recorded but not compared to the target curves.

13. A dynamic adjustment method according to any one of claims 1 to 12, further comprising optimizing the energy consumption related to the extraction cycle, consisting of: - measuring energy consumption in real time, including both electrical and thermal consumption; - calculating energy efficiency defined by the ratio of weight loss per kWh consumed; - depending on the difference between the calculated energy efficiency and the predefined minimum acceptable energy efficiency ratio, applying measures to optimize energy efficiency, such measures comprising at least one of the following actions: automatic shutdown of the extraction cycle, adjustment of ventilation,

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