Methods and devices for providing beverage manufacturing analysis

Abstract

The quality of natural products such as beer, bread, wine, etc. is determined by many parameters: process parameters that may or may not be changed, external parameters, and parameters of the raw materials. With complex processes and many process steps, it is very difficult to relate the parameters to the quality, if possible. Data on process control, values ​​of product and process related parameters, as well as values ​​that can be interpreted as indicators of quality, substances and their amounts found in the product (either final or intermediate), other parameters, or a combination thereof. Between the values ​​obtained for the parameters, relationships can be determined that can be used for process optimization. This can be performed for one or more process steps, one or more parameters in the process, and one or more quality parameters.

Description

Technical Field

[0001] Various aspects and variations thereof relate to the field of process analysis and control for beverage manufacturing plants, particularly breweries for beer.

Background Art

[0002] The production of beverages, particularly the brewing of beer, is a complex process involving many different steps. The processes are mainly chemical and biological processes, and their course and results are determined by many factors in each step, and each of them may affect the final result. Although brewing conditions can be improved by trial and error, due to many factors that can affect the same factor with very small changes in the value of each factor, using trial and error may not be a feasible way to reach improved process control parameters.

Summary of the Invention

Means for Solving the Problems

[0003] It is preferable to obtain process parameter values and provide the relationship between these process parameters and the quality of the final product. Based on such a relationship, setpoint values for process control can be provided to reach a final product having improved quality and preferably optimal quality.

[0004] The first embodiment provides a method for determining, with respect to execution in an electronic processing analysis device, the relationship between at least one of a set of processing parameters of a first processing step and, on the one hand, a set of raw material parameters and on the other hand, the relationship between at least one product component parameter and a first product quantity that has undergone a first processing step as part of a manufacturing process. The method includes at least one of the following: obtaining processing data from a processing data acquisition control system, which includes at least one first processing parameter value relating to the first processing step of the first product quantity; and obtaining raw material data from the processing data acquisition control system, which includes at least one first raw material parameter value relating to the raw materials that form the basis of the first product quantity. Thus, either a first processing parameter value or a first raw material parameter value, or both, can be obtained. The method further includes identifying at least one product component potentially present in the first product quantity and obtaining at least one first product component parameter value relating to the first component in the first product quantity. Based on at least one of the first processing parameter values, and on the one hand the first raw material parameter value and on the other hand the product component parameter value, a relationship score is determined that shows the relationship between at least one of the processing parameter values ​​and the raw material parameter value on the one hand, and the relationship between the processing parameter value and the product component parameter value on the other hand.

[0005] The quality of a beverage may be determined primarily by the presence (or absence) of specific components in the beverage, and, if present, by the amount of the component present in the beverage, either in the beverage as a whole or per unit volume or mass of the beverage. The presence of such components may be related to any processing parameter, raw material, other factors, or combination thereof, and such relationships may be determined by the method according to the first embodiment. Along with information regarding such relationships, processing parameters and raw material parameters may be fine-tuned for subsequent batches with the aim of achieving improved component parameter values, which may provide improved quality of the beverage.

[0006] Secondly, the method as in the first embodiment may enable improved process initialization and ramp-up, as well as automatic calibration of beverage production lines and beverage production plants. Rather than characterizing a new plant for a known process that has machinery that may differ (either slightly or significantly) from a previous plant by obtaining reactor vessel dimensions, calibrating sensors and simulations, information regarding appropriate process parameters can be obtained by performing one or more pilot runs and applying the method as in the first embodiment.

[0007] A modification of the first embodiment includes at least one of the following: obtaining processing data from a processing data acquisition control system, which includes at least one first processing parameter value relating to a first processing step of a second product quantity; and obtaining raw material data from a processing data acquisition control system, which includes at least one first raw material parameter value relating to raw materials forming the basis of a second product quantity. In this modification, determining the relationship score between at least one of the processing parameter values ​​and, on the one hand, the raw material parameter value, and on the other hand, the relationship score between it and the product component parameter value, is also based on at least one of the second processing parameter value and the second raw material parameter value.

[0008] Typically, relationships can be determined more accurately by using data from multiple batches with different values ​​for the relevant parameter. With more data, the analysis can become more fault-tolerant. Furthermore, this can make it possible to determine whether the relationship between values ​​is linear, quadratic, cubic, exponential, monotonic / non-monotonic, strong / weak, correlated within a specific boundary, or any combination thereof.

[0009] Another variation further includes obtaining a plurality of processing parameter values ​​for a first processing step of a first product quantity; obtaining a plurality of raw material parameter values ​​for a plurality of raw materials that form the basis of the first product quantity; and determining the relationship between each of the plurality of processing parameter values ​​and each of the plurality of raw material parameter values, and the relationship with the product component parameter values, based on the plurality of processing parameter values, the plurality of raw material parameter values, and the product component parameter values. Based on the results of the analysis, at least one of a predetermined number of the plurality of processing parameters and plurality of component parameters is selected that has a relationship score showing the strongest relationship with the product component parameter value.

[0010] This modification provides direct and concise feedback to the user of a system employing the first embodiment. In this way, the user (e.g., a process control operator or process supervisor) can focus on the most relevant parameter to modify. Alternatively or additionally, without human involvement, the electronic computing system controls the process based on the most relevant parameter. Optionally, the value of one of the most relevant parameters may be set to the optimal value or a value close to the optimal value, while other parameters may be selected for presentation to the user to allow for fine-tuning of the values ​​of subsequent parameters.

[0011] In further embodiments, the manufacturing process includes batch processing, and the first product quantity comprises at least one batch. In batch processing, the quantities of beverages are well separated from each other. This allows for convenient and practical analysis of the relationships between various parameters in batch-by-batch analysis.

[0012] In yet another embodiment, at least one of the first processing parameter values ​​has an associated first processing parameter timestamp indicating the start of the first processing step, and the first raw material parameter value has an associated first raw material timestamp indicating the moment when the first raw material is added to the first product quantity.

[0013] Adding raw materials at specific moments during processing, changing temperature settings at particular moments, or actual changes in temperature at particular moments may affect the final result. By considering timing data while registering data and when determining relationships, relationship determination can be performed in a more accurate manner, and the timing of specific processing parameters may be provided in the output of the processing.

[0014] In yet another embodiment, the manufacturing process is a continuous process, further comprising obtaining a throughput time interval of the first processing step indicating a first quantity of product having gone through the first processing step.

[0015] A defined amount of beverage may be mixed with other amounts of beverage that have undergone other processing, while a specific amount of beverage is identified and its flow rate through the conduit and reactor is monitored. Based on this data, time can be determined as to when the defined amount enters the reactor or a specific processing step, and when the defined amount leaves the reactor or a specific processing step.

[0016] In yet another embodiment, the first processing parameter value is one of a control value and a measured value. The control value may be a setpoint of an actuator that is achieved or expected to be achieved by controlling the actuator with a specific pressure of, for example, gas, voltage, current, other, or a combination thereof. The measured value may then be an actual value in the reactor or other container or conduit from which the beverage is supplied.

[0017] Further embodiments further include obtaining objective quality relationships between objective quality score parameter data relating to at least one product parameter, and determining an objective quality score value for a first quantity based on the quality relationships and the values ​​of at least one product parameter. As shown above, the quality of a beverage may be determined by the components present, such as esters. Additionally or alternatively, quality may be determined by other characteristics of the beverage, such as viscosity, color, bitterness, clarity, translucency, sweetness, acidity, sourness, etc., or any combination of two or more of these characteristics. As is well known, such characteristics may be determined by the components present and their amounts, but physical characteristics such as the viscosity or hardness of foam may be more convenient to register.

[0018] Again, another embodiment includes obtaining a first quality score value for a first quantity, obtaining a second quality score value for a second quantity, and a quality relationship between a product component parameter and a quality score based on the first quality score value, the second quality score value, at least one product component parameter value for the first quantity, and at least one product component parameter value for the second quantity. This makes it possible to obtain more possible improved data and accuracy regarding the relationship.

[0019] Further embodiments include obtaining a first quality score value for a first quantity, obtaining a second quality score value for a second quantity, and further including a quality relationship between a processing parameter and a quality score based on the first quality score value, the second quality score value, at least one processing parameter value for the first quantity, and at least one processing parameter value for the second quantity. This makes it possible to obtain more possible improved data and accuracy regarding the relationship.

[0020] Another embodiment further includes obtaining a first quality score value for a first quantity, obtaining a second quality score value for a second quantity, and determining a quality relationship between a raw material parameter and a quality score based on the first quality score value, the second quality score value, at least one raw material parameter value for the first quantity, and at least one raw material parameter value for the second quantity. This makes it possible to obtain more possible improved data and accuracy regarding the relationship.

[0021] The electronic process analysis device determines, for a first quantity of product that has undergone a first processing step as part of the manufacturing process, the relationship between at least one of a set of processing parameters of the first processing step and, on the one hand, a set of raw material parameters and on the other hand, the relationship between at least one product component parameter. The device comprises an input, an output, and a processing unit. The input is configured to perform at least one of the following: obtaining processing data from a processing data acquisition control system, which includes at least one first processing parameter value relating to the first processing step of the first quantity of product; obtaining raw material data from a processing data acquisition control system, which includes at least one first raw material parameter value relating to the raw materials that form the basis of the first quantity of product; identifying at least one product component potentially present in the first quantity of product; and obtaining at least one first product component parameter value relating to the first quantity of product with respect to the first component. The processing unit is configured to determine a relationship score indicating the relationship between at least one of the first processing parameter values ​​and the raw material parameter value, and the relationship with the product component parameter value, based on at least one of the first processing parameter values ​​and, on the one hand, the first raw material parameter value and on the other hand, the product component parameter value. The output is configured to provide data on at least one of the relationships or the relationship score indicating the relationship.

[0022] Another aspect provides a computer program product including computer-executable code configured to cause a method to be performed in accordance with the first aspect or any variation or permutation thereof when the executable code is loaded into a memory connected to an electronic processing unit configured by a computer to program the electronic processing unit. A further aspect provides a non-transitory memory having such a computer program product stored therein or thereon.

[0023] Here, various aspects and variations thereof will be considered in more detail in conjunction with the drawings. The drawings are as follows:

Brief Description of the Drawings

[0024] [Figure 1] Shows a schematic diagram of at least a part of a brewery and a data processing system. [Figure 2] Shows a flowchart.

Mode for Carrying Out the Invention

[0025] FIG. 1 shows a system 100 for monitoring the production and processing of beverages. In this particular case, the processing is brewing beer. A part of the system is a brewery 110. For clarity, only some of the brewing equipment is depicted. FIG. 1 shows a mash tank 112, a boiling tank 114, and a cooler 116. For clarity, fermentation tanks, bright tanks, and filters are omitted. Those skilled in the art will recognize that other processes may also be added to the brewing line. Downstream of the cooler, a storage 118 is shown. The storage 118 can be an intermediate storage before fermentation and can be a fermentation tank, another type of container, or a combination thereof.

[0026] In the mashing tank 112, a first raw material supply line 132 is provided. Through the first raw material supply line 132, water and malt are provided to the mashing tank 112. Water and malt can be provided through one and the same conduit. In other embodiments, the first raw material supply line 132 comprises a plurality of conduits, for example one for each type of raw material. In the mashing tank 112, a first rudder 122 is provided as a processing actuator driven by a first electric motor 126. Further processing actuators can be provided, such as heaters, other mechanical stirrers, or combinations thereof.

[0027] The mashing tank 112 is connected to the boiling tank 114 via a first brewing conduit 134 and debouches into the boiling tank 114. The connection between the mashing tank and the first brewing conduit 134 can be controlled by a valve. Thus, the wort can be transferred from the mashing tank 112 to the boiling tank 114 in a batch manner or continuously.

[0028] In the boiling tank 114, a second raw material supply line 138 is provided. In this embodiment, the second raw material supply line 138 is configured to provide hops to the boiling tank 114 and optionally further raw materials such as spices, herbs, citrus peels, or combinations thereof. The boiling tank can be provided with a second rudder 124 driven by a second electric motor 128. Further processing actuators can also be provided in the boiling tank 114, such as heaters, other mechanical stirrers, or combinations thereof.

[0029] The boiling tank 114 is connected to the cooler 116 via a second brewing conduit 138 and debouches into the cooler 116. The connection between the boiling tank and the second brewing conduit 138 can be controlled by a valve. Thus, the wort can be transferred from the boiling tank 114 to the cooler 116 in a continuous or batch manner.

[0030] The boiling wort is preferably passed continuously through a cooler and supplied to the storage room 118. From the storage room, the remainder of the brewing process can be continued as is known to those skilled in the art.

[0031] On the left side of Figure 1, a monitoring and control data bus 108 is provided. The data bus 108 is connected to the central processing unit 102 of the monitoring and control system. The data bus 108 is also connected to various sensors and actuator controllers within the brewery 110. The monitoring and control system further comprises a memory 104 as a data storage module and a touchscreen 106 as an input / output module. Instead of the touchscreen, a separate electronic display screen, keyboard, mouse, tracking ball, display projector, speaker, etc., or a combination thereof may also be provided to receive and provide data. Such data may be provided to and / or received from a user, other systems, or a combination thereof. The central processing unit 102 is configured to control the functions of the monitoring and control system, in particular the handling of data. The memory 104 is configured to store data received and processed by the monitoring and control system, and to store data that enables the central processing unit 102 to be programmed to perform any processing or part thereof as described below.

[0032] The first raw material sensor module 152 is provided on or within the first raw material supply line 132. The first raw material sensor module 152 is configured to obtain data with respect to one or more raw materials added to the mashing tank 112, including values ​​for at least one of the following: the type of raw material, the quality level of the raw material, the quantity of the raw material, another value for another raw material parameter, or a combination thereof. Thus, the first sensor module 152 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following: flow, flow velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, and others or combinations thereof.

[0033] The first material sensor module 152 may also be configured to provide a timestamp with any generated data by sensing a substance. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and is preferably stored in the memory 104.

[0034] The second raw material sensor module 154 is provided on the second raw material supply line 136 or within the first raw material supply line 132. The second raw material sensor module 154 is configured to obtain data on at least one of the following with respect to one or more raw materials added to the boiling tank 114: the type of raw material, the quality level of the raw material, the quantity of the raw material, another value of another raw material parameter, or a combination thereof. Thus, the second sensor module 154 may include at least one of the following: an optical sensor, a magnetic sensor, an electrical sensor, a mechanical sensor, a chemical sensor, or other sensor, to measure at least one of the following: flow, flow velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, and others or a combination thereof.

[0035] The second material sensor module 154 may also be configured to provide a timestamp with any generated data by sensing a substance. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and is preferably stored in the memory 104.

[0036] The first processing sensor module 156 is provided in conjunction with the mashing tank 112. The first sensor module 156 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following: flow, flow velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, and others or combinations thereof. Thus, the first processing sensor module 156 is configured to obtain various characteristics of the wort in the mashing tank 112.

[0037] Furthermore, the first processing sensor module 156 may be configured to obtain data relating to the energy added to the wort by any heating means, the volume of wort available in the mashing tank 112, the control temperature or set temperature of the heating means, and other, or a combination thereof. The first processing sensor module 156 may also preferably be configured to provide a timestamp with any generated data by sensing a substance. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0038] A second processing sensor module 158 is provided in conjunction with the boiling tank 114. The second sensor module 158 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following: flow, flow velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, and others or combinations thereof. Thus, the second processing sensor module 158 is configured to obtain various characteristics of the wort in the mashing tank 112.

[0039] Furthermore, the second processing sensor module 158 may be configured to obtain data relating to the energy added to the wort by any heating means, the volume of wort available in the boiling tank 114, the control temperature or set temperature of the heating means, and other, or a combination thereof. The second processing sensor module 158 may also preferably be configured to provide a timestamp with any generated data by sensing a substance. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0040] A first actuator sensor module 162 is provided with a first electric motor 126. The first actuator module 162 is configured to control the first electric motor 126 by providing a specific voltage or current to control angular velocity, a provided torque, the duration for which a first ladder 122 has specific parameters, or a combination thereof. The first actuator sensor module 162 may also be configured to sense or otherwise obtain data on at least one of the actual voltage or current provided, or the actual angular velocity or torque provided by the first electric motor.

[0041] The first actuator sensor module 162 may also be configured to provide a timestamp with any generated data by sensing a substance, preferably. As shown, other actuators may also be provided in conjunction with the mashing tank 112. The first actuator sensor module 162 may be configured to control such actuators and to sense any data related to such actuators, such as the energy added to the wort in the mashing tank 112 by the heating means. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0042] A second actuator sensor module 164 is provided with a second electric motor 128. The second actuator module 164 is configured to control the second electric motor 128 by providing a specific voltage or current to control angular velocity, a provided torque, the duration for which a second ladder 124 has specific parameters, or a combination thereof. The second actuator module 164 may also be configured to sense or otherwise obtain data on at least one of the actual voltage or current provided, or the actual angular velocity or torque provided by the second electric motor.

[0043] A second actuator module 164 may also be configured to provide a timestamp with any generated data by sensing a substance, preferably. As shown, other actuators may also be provided in conjunction with the boiling tank 114. The second actuator module 164 may be configured to control such actuators and to sense any data related to such actuators, such as the energy added to the wort in the boiling tank 114 by the heating means. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0044] The first conduit sensor module 166 is provided in conjunction with the first brew conduit 134. The first conduit sensor module 166 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following properties of a substance in the first brew conduit 132: flow, velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, etc., or a combination thereof. Thus, the first conduit sensor module 166 may be configured to sense at least one of the physical and chemical properties of the wort transported through the first brew conduit 134. The first conduit sensor module 166 may also preferably be configured to provide a timestamp with any data generated by sensing the substance. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0045] A second conduit sensor module 168 is provided in conjunction with the second brew conduit 136. The second conduit sensor module 168 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following properties of a substance in the second brew conduit 134: flow, velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, etc., or a combination thereof. Thus, the second conduit sensor module 168 may be configured to sense at least one of the physical and chemical properties of the wort transported through the second brew conduit 136. The second conduit sensor module 168 may also preferably be configured to provide a timestamp with any data generated by sensing the substance. The data thus obtained is transmitted to a monitoring and control system via a data bus 108 and preferably stored in memory 104.

[0046] The cooling sensor module 172 is provided in conjunction with the cooler 116. The cooling sensor module 172 may include at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the following: flow, velocity, flow rate, weight, chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, etc., or a combination thereof, of a substance in the cooler 116. Thus, the cooling sensor module 172 may be configured to sense at least one of the physical and chemical properties of the wort transported through the cooler 116. The cooling sensor module 172 may also be configured to control the operation of the cooler 116 by controlling the flow of the coolant and providing a set temperature, control temperature, etc., or a combination thereof. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0047] The sample sensor module 174 is connected to the data bus 108 to sense a sample of beer or wort or another intermediate product of the brewery. The sample sensor module 174 may be equipped with at least one of optical sensors, magnetic sensors, electrical sensors, mechanical sensors, chemical sensors, or other sensors to measure at least one of the chemical characteristics, conductivity, temperature, transparency to visible or invisible light, acidity, sugar content, viscosity, color, etc., or a combination thereof of the substance in the sample. Thus, the sample sensor module 174 may be used to determine one or more substances in the sample, such as ethanol, methanol, sugars, preferably at least one of specific sugar molecules, carbon dioxide, specific esters, etc., or a combination thereof, preferably including at least one of the weight ratio and volume ratio of the detected substances in relation to at least one standard measure of weight and volume. The data thus obtained is transmitted to the monitoring and control system via the data bus 108 and preferably stored in the memory 104.

[0048] The operation of the monitoring and control system, which has data obtained from the brewery, will be examined in more detail in conjunction with the flowchart 200 depicted in Figure 2. Various parts of flowchart 200 can be summarized as follows: 202 Start processing 204 Define the quantity to be evaluated. 206 Start the processing step. 208 Add ingredients 210 Register raw material parameters 212 Execute the processing actuator 214 Register actuator parameters 216 Detect processing parameters 218 Register processing parameters 220 Is the processing step complete? 222 Processing complete? 224. Analyze the composition. Register 226 component parameters. 228 Is all the work complete? 230 Analyze the registered parameters 232 Obtain quality parameters 234. Associate component parameters with parameters registered during processing. 236. Associate component parameters with quality parameters. 238 Associate quality parameters with parameters registered during processing. Associate 240 component parameters with actuator parameters. Based on relationship 242, determine the best quality of processing control parameters. 244 Output the determined processing control parameters. 246 End 250 The next process for intermediate products

[0049] The procedure begins at terminator 202 and proceeds to step 204, where the amount of beverage to be analyzed is defined. The amount to be analyzed may be a batch of beverage, which, as a whole, relates to the exact same beverage undergoing a specific processing step by a particular instrument. Alternatively or additionally, the amount of beverage may be defined as the volume of a continuous or semi-continuous flow of beverage through the processing steps while undergoing processing.

[0050] Continuous flow rate can be defined by monitoring the flow (flow velocity as the speed of flow in distance per unit of time, mass per unit of time, volume per unit of time, or a combination thereof), as well as the start and end times at each monitoring point. The flow and the progression of central time can be used to determine when a defined quantity reaches and leaves the next processing step. In a brewery, the identified quantity at which the brewing process begins may be a quantity of water.

[0051] In 206, the processing step is initiated with a defined quantity. If the processing is batch processing, the processing of the defined quantity is initiated. If the processing step is continuous processing, the first portion of the identified quantity enters a processing vessel, processing conduit, another processing entity, or a combination thereof.

[0052] In step 208, one or more ingredients are added to an identified amount. For example, malt may be added to water. In step 210, the added ingredients are sensed by the first ingredient sensor module 152, and the data is registered, for example, in memory 104. Each ingredient may be registered as a parameter. Several values ​​are received for the ingredient parameters, sensed via automatic or manual input, or otherwise received, and can therefore be associated. The values ​​may relate to quality, sugar content, type, other as specified above, or a combination thereof.

[0053] The associated values ​​or combinations of values ​​may be (discrete, substantial, rational, or composite) numerical, Boolean, alphanumeric, or other. Furthermore, a timestamp may be associated with a raw material parameter at the moment of value acquisition. Alternatively or additionally, individual timestamps may be associated with individual parameter values. Timestamps, along with the throughput rate of the substance through the system, may be used to assign parameter values ​​to specific, defined quantities, particularly in continuous processing.

[0054] In step 212, the first ladder 122 is started at the start or after the start of an applicable processing step. In step 214, actuator parameter values ​​are obtained and registered, for example, in memory 104. Such parameters may be the operation of the first ladder 122, and the relevant values ​​may be rotational speed, angular velocity, torque, etc., sensed in revolutions per unit of time such as seconds or minutes, or a combination thereof. The values ​​may be, for example, actual values ​​obtained by sensors, or setpoints, such as the angular velocity value at which the processing unit controls the first ladder 122. Other actuator parameters for a heater may also be obtained, for example.

[0055] The associated values ​​or combinations of values ​​may be numerical, Boolean, alphanumeric, or other (discrete, substantial, rational, or composite) values. A Boolean value might indicate, for example, whether a particular valve is open or closed, while an alphanumeric value might relate to a status message or error message from the machine.

[0056] A timestamp can be associated with an actuator parameter at the moment the value is acquired. Alternatively or additionally, individual timestamps can be associated with individual parameter values. Timestamps, along with the throughput rate of material through the system, can be used to assign parameter values ​​to specific, defined quantities, particularly in continuous processing. Values ​​can be registered at the moment a value changes or changes beyond a predetermined threshold. Alternatively or additionally, values ​​can be acquired at periodic intervals or continuously.

[0057] In step 216, the processing parameters are sensed, for example, by the first processing sensor module 156. In step 218, the values ​​obtained through sensing the processing parameters are registered, for example, in memory 104. The processing parameters may be wort temperature, clarity, conductivity, or any other value, or a combination thereof, as considered above. The processing parameters may also be external parameters such as temperature, humidity, etc., or a combination thereof, inside the production hall, outside the production hall, outdoors, etc., or a combination thereof.

[0058] A timestamp can be associated with a processing parameter at the moment the value is obtained. Alternatively or additionally, individual timestamps can be associated with individual parameter values. Timestamps, along with the throughput rate of material through the system, can be used to assign parameter values ​​to specific, defined quantities, particularly in continuous processing. Values ​​can be registered at the moment the value changes or changes beyond a predetermined threshold. Alternatively or additionally, values ​​can be obtained at periodic intervals or continuously.

[0059] In step 220, it is verified whether the identified quantity has been processed by an applicable processing step. If the processing step is a batch process, the processing step may terminate when it reaches an endpoint. Such an endpoint may be defined as the elapsed time interval, the temperature value reached, another value of the registered value, or one or more specific parameters reached, or a combination thereof. If the processing step is a continuous process, the processing step for the identified quantity may be defined as the quantity having been processed or having left the reactor in which the processing step was performed, when it has reached a fully defined quantity.

[0060] If the processing step is not yet complete, the process returns to step 208 to add (or not add) additional raw materials, obtain further values ​​for various parameters, and register them. If the processing step is completed as considered above, the procedure proceeds to step 222. In step 222, it is checked whether the entire process has been completed for the defined quantity. If the entire process is not completed, the procedure returns to step 206 for the subsequent processing step, via step 250, which is defined for the next processing step for the defined quantity.

[0061] Once all processing is complete, the procedure proceeds to step 224, in which the final product of all processing is analyzed. Such analysis may be physical, chemical, biological, other, or a combination thereof. Physical analysis relates to determining physical parameters of the final product, such as clarity, color, conductivity, foam hardness, other, or a combination thereof. Chemical analysis may relate to determining acidity, sugar content, content of specific substances or types of substances, such as esters, acids, other, or a combination thereof, carbon dioxide content, other, or a combination thereof. Biological analysis may relate to determining the presence of amounts of common yeast, specific yeast, other, or a combination thereof. Preferably, the composition of the final product is determined, i.e., the amount of a specific component (substance) per unit volume or unit mass of the final product is determined. The results may be stored in memory 104 in step 226.

[0062] In step 230, registered values ​​of various parameters are analyzed and, optionally, processed. Such analysis may relate to the detection of outliers by comparing them with thresholds, threshold bands, intervals, or other combinations thereof. Processing may relate to the removal of outliers, the time-dependent derivative or integral, the determination of other values ​​or combinations thereof, or the processing of values ​​over time as a function of the values ​​of other parameters, other, or combinations thereof. Such analysis and processing may result in additional values ​​of the analyzed parameter or other parameters.

[0063] In step 228, it is checked whether all identified quantities have been processed and whether identified parameter values ​​have been obtained and registered. If this is not the case, the procedure returns to step 204, or to step 206 if further quantities are defined. If all data to be analyzed is obtained from the identified quantities to be evaluated, the procedure proceeds to step 232.

[0064] In step 232, quality parameters can be obtained for each quantity. Such quality parameters may be objective parameters relating to physical, chemical, or biological parameters, such as the amount of a particular substance in the sample from which the objective parameters are taken. In this sense, "objective" means that the value of the parameter can be obtained by measuring or counting values ​​such as mass, time, volume, conductivity, and temperature, as opposed to "subjective" values ​​relating to human perception.

[0065] If the foam is sufficiently firm, i.e., if it holds a certain volume for a predetermined amount of time, the parameter has a first Boolean value; otherwise, the parameter has a second Boolean value. Similarly, the acidity and presence of a specific component per unit or amount relative to a threshold, and its amount, can constitute objective quality parameters. Instead of Boolean values, numerical values ​​may be used, for example, on a scale of 0 to 9 or 1 to 10.

[0066] The values ​​of relative or subjective quality parameters can be obtained through testers, for example, by tasting the final product, smelling the final product, or otherwise evaluating the final product using their senses.

[0067] Step 232 may also be placed before step 230, but if the beverage material from all processing steps is to be evaluated in particular for quality and subjective quality, it may be more feasible to compare quality when all quantities are available.

[0068] In subsequent processes, the relationships between various parameters are evaluated based on the parameter values. As will be considered, multiple values ​​may be associated with a parameter, indicating the values ​​of its subparameters. Such relationships can be determined, for example, by calculating the correlation between the values ​​of the parameter and its subparameters. The values ​​of the subparameters may be for the same parameter or other parameters. The relationships between parameters can be determined within one defined quantity (within a batch) or across multiple defined quantities (between batches).

[0069] In addition to calculating correlations, other algorithms may also be used to determine relationships between parameters, and additionally or alternatively, neural networks or other machine learning algorithms may be employed to determine relationships between various parameters and parameter sets on the input side and multiple input sides, based on various parameter values. In yet another embodiment, additionally or alternatively, least squares methods may be used to fit the parameter relationships to curves that may be linear, quadratic, cubic, exponential, other curves, or combinations thereof.

[0070] More specifically, in step 234, the relationships between component parameters relate to parameters obtained during processing. These latter parameters may be processing parameters, raw material parameters, others, or combinations thereof. In step 236, the relationship between component parameters and parameters for which values ​​were obtained during processing is determined. In step 238, the relationship between quality parameters (objective, subjective, or both) and component parameters is determined. In step 240, the relationship between component parameters and actuator parameters is determined. Further relationships between parameters for which values ​​are obtained may also be evaluated and determined.

[0071] Based on the determined relationships and how strong they are (for example, a correlation of 0 indicates a very weak relationship, while a correlation of 1 or -1, or a correlation close to either of these values, indicates a strong relationship), a parameter is obtained that yields the value with the strongest relationship to the quality parameter. Such a parameter may be an actuator parameter, a processing parameter, a raw material parameter, or a combination thereof.

[0072] In step 246, the results of the process (relationships, relationship strength, most relevant relationships, relationships most relevant to quality, objective, subjective, or both, statistical values, others, or a combination thereof) are provided to the user or another system for data processing. In this way, optimal values ​​for raw material parameters, actuator parameters, processing parameters, other parameters, or combinations thereof can be determined and provided to the user or control device. One or more parameters may be used to automatically optimize the machine settings. The process then ends in step 246.

[0073] The process can be refined and optimized through iteration by performing the processes described above, and can be implemented as an iterative process to further improve and optimize the process and process parameters, either automatically or manually. For optimization, known or new algorithms or other techniques may be used, but are not limited to least squares, regression, interpolation, extrapolation, the use of trained neural networks, or other artificial intelligence used in machine learning, or a combination of two or more of these techniques.

[0074] The process discussed so far is the brewing process. As discussed above, the mashing, cooking, and cooling processes have been discussed in detail, and the steps of flowchart 200 and its variations can be applied to these processes. The same steps of flowchart 200 and its variations can be applied to the fermentation, clarification, carbonization, filtration, and other processing steps of the brewing process.

[0075] Additionally or alternatively, the procedures and their variations may be applied to wastewater treatment, cleaning of brewing equipment, and bottling processes for filling bottles, barrels, and cans.

[0076] In summary, the above concerns how the quality of natural products such as beer, bread, and wine is determined by many parameters. These include processing parameters that may or may not be changed, external parameters, and raw material parameters. In complex processes and many processing steps, it is very difficult to relate parameters to quality wherever possible. These include data on processing control, values ​​of product and process-related parameters, values ​​that can be interpreted as indicators of quality, substances and their quantities found in the product (either final or intermediate), other parameters, or combinations thereof. Relationships between the values ​​obtained for parameters can be determined that can be used for process optimization. This can be done for one or more processing steps, one or more parameters within the process, and one or more quality parameters. (Note) (Note 1) A method for determining the relationship between at least one of a set of processing parameters of a first processing step, a set of raw material parameters, and at least one product component parameter for a first product quantity that has undergone a first processing step as part of the manufacturing process, in an electronic processing analysis device, Obtaining processing data from a processing data acquisition control system, including at least one first processing parameter value of the processing parameters of the set of processing parameters, wherein the first processing parameter value is related to the first processing step of the first product quantity; and obtaining raw material data from the processing data acquisition control system, including at least one first raw material parameter value of the raw material parameters of the set of raw material parameters, wherein the first raw material value is related to the raw materials that form the basis of the first product quantity; at least one of these, Identifying at least one product component potentially present in the first product quantity described above, To obtain at least one first product component parameter value for the first product quantity relating to the first component, A method comprising determining a relationship score that indicates the relationship between at least one of the first processing parameter values ​​and the raw material parameter value, and the relationship between the raw material parameter value and the product component parameter value, based on at least one of the first processing parameter values ​​and the first raw material parameter value on the one hand and the product component parameter value on the other. (Note 2) The system further includes at least one of the following: obtaining processing data from a processing data acquisition control system, which includes at least one first processing parameter value relating to the first processing step for a second product quantity; and obtaining raw material data from the processing data acquisition control system, which includes at least one first raw material parameter value relating to the raw materials that form the basis for the second product quantity. The method according to Appendix 1, wherein determining the relationship score between at least one of the processing parameter values ​​and the raw material parameter value on the one hand, and the relationship score between the raw material parameter value and the product component parameter value on the other hand, is also based on at least one of the second processing parameter value and the second raw material parameter value. (Note 3) Obtaining a plurality of processing parameter values ​​for the first processing step of the first product quantity, and obtaining a plurality of raw material parameter values ​​for a plurality of raw materials that form the basis of the first product quantity, at least one of these, Based on the plurality of processing parameter values, and on the other hand the plurality of raw material parameter values ​​and the product component parameter values, the relationship between each of the plurality of processing parameter values ​​and on the other hand each of the plurality of raw material parameter values ​​and on the other hand the relationship between each of the product component parameter values, The method according to Appendix 1 or 2, further comprising selecting a predetermined number of at least one of the plurality of processing parameters and the plurality of raw material parameters, which have a relationship score that shows the strongest relationship with the product component parameter value. (Note 4) The method according to any one of the appendices 1 to 3, wherein the manufacturing process includes batch processing, and the first product quantity includes at least one batch. (Note 5) The first processing parameter value has an associated first processing parameter timestamp indicating the start point of the first processing step, The method according to any one of appendices 1 to 4, wherein the first raw material parameter value has at least one associated with it a first component timestamp indicating the moment in time at which the first raw material is added to the first product quantity. (Note 6) The method according to any one of Appendices 1 to 5, wherein the manufacturing process is a continuous process, further comprising obtaining a throughput time interval of the first processing step that indicates the first amount of product having gone through the first processing step. (Note 7) The method according to any one of the appendices 1 to 6, wherein the first processing parameter value is one of the control value and the measured value. (Note 8) The method according to any one of Appendix 1 to 7, wherein the first raw material parameter value is one of the weight of the first raw material, the volume of the first raw material, and the quality of the first raw material. (Note 9) Obtain objective quality relationships between objective quality score parameter data for at least one product parameter, The method according to any one of the appendices 1 to 8, further comprising determining an objective quality score value for the first quantity based on the quality relationship and the value of at least one product parameter. (Note 10) To obtain a first quality score value for the first quantity, To obtain a second quality score value for the second quantity, The method according to any one of the Appendices 2 to the extent dependent on Appendice 2, further comprising: a first quality score value, a second quality score value, a first quantity of at least one product component parameter value, and a second quantity of at least one product component parameter value, and a quality relationship between the product component parameter and the quality score. (Note 11) To obtain a first quality score value for the first quantity, To obtain a second quality score value for the second quantity, The method according to any one of the Appendices 3 to 10, to the extent dependent on Appendice 2, further comprising: a first quality score value, a second quality score value, at least one processing parameter value of the first quantity, and a quality relationship between the processing parameter and the quality score based on at least one processing parameter value of the second quantity. (Note 12) To obtain a first quality score value for the first quantity, To obtain a second quality score value for the second quantity, The method according to any one of the Appendices 3 to 11, to the extent that it is dependent on Appendice 2, further comprising: a first quality score value, a second quality score value, at least one raw material parameter value in the first quantity, and a quality relationship between the raw material parameter and the quality score based on the at least one raw material parameter value in the second quantity. (Note 13) The method according to any one of the appendices 1 to 12, wherein the aforementioned relationship score indicates the correlation between applicable parameters. (Note 14) The method according to any one of the appendices 1 to 13, wherein the obtained first processing parameter value and the obtained first raw material parameter value are provided to a neural network, and the relation score is provided by the neural network. (Note 15) The above first processing parameter value is, temperature, Conductivity, color, Transparency to visible light, Relative weight, acidity, sugar content, viscosity, Added energy, A method according to any one of the appendices 1 to 14, which is one of the temperature control methods for a machine that performs the first processing step. (Note 16) The aforementioned product component parameters are as follows: Ethanol content, Methanol content, sugar content, A method according to any one of the methods described in Appendix 1 to 15, which is at least one of the carbon dioxide content. (Note 17) The first processing step is as follows: To cool, To heat, Stirring, Fermentation Filtration, Mashing, The method described in any one of the appendices 1 to 16, which is at least one of the methods of packaging. (Note 18) An electronic process analysis device for determining, with respect to a first quantity of product that has undergone a first processing step as part of the manufacturing process, the relationship between at least one of a set of processing parameters of the first processing step and, on the one hand, a set of raw material parameters and on the other hand, the relationship between at least one product component parameter, It is an input, - Obtaining processing data from a processing data acquisition control system, including at least one first processing parameter value of the processing parameters of the set of processing parameters, wherein the first processing parameter value is related to the first processing step of the first product quantity; and obtaining raw material data from a processing data acquisition control system, including at least one first raw material parameter of the raw material parameters of the set of raw material parameters, wherein the first raw material value is related to the raw materials that form the basis of the first product quantity, at least one of these. - Identifying at least one product component potentially present in the aforementioned first product quantity, - An input configured to perform the following: obtaining at least one first product component parameter value for the first product quantity relating to the first component, A processing unit configured to determine a relationship score indicating the relationship between at least one of the first processing parameter values ​​and the raw material parameter value, and the relationship between the raw material parameter value and the product component parameter value, based on at least one of the first processing parameter values ​​and the first raw material parameter value on the one hand and the product component parameter value on the other, An electronic process analysis device comprising: an output configured to provide data relating to at least one of the aforementioned relationships or relationship scores indicating the relationship. [Explanation of Symbols]

[0077] 100 Systems 102 Central Processing Unit 104 memory 106 Touchscreen 108 Control Data Bus 108 Data Bus 110 breweries 112 Mashing Tank 114 Boiling Tank 116 Cooler 118 Storage 122 First ladder 124 Second ladder 126 First Electric Motor 128 Second Electric Motor 132 First raw material supply line 132 First brewing conduit 134 Second brewing conduit 136 Second brewing conduit 138 Second raw material supply line 152 First raw material sensor module 154 Second raw material sensor module 156 First processing sensor module 158 Second processing sensor module 162 First Actuator Sensor Module 164 Second actuator sensor module 166 First conduit sensor module 168 Second conduit sensor module 172 Cooling Sensor Module 174 Sample Sensor Module

Claims

1. A method (200) for selecting at least one predetermined amount of a plurality of processing parameters for a first product quantity of beer that has undergone a first processing step as part of a brewing process, performed by an electronic processing analysis device (102), based on determining a first relationship between a set of processing parameters of the first processing step and at least one first product component parameter, and / or a second relationship between a set of raw material parameters and at least one first product component parameter, Identifying at least one first product component potentially present in the first product quantity (224), This includes obtaining at least one first product component parameter value for the first product amount relating to the first product component (226), At least one of the following steps: obtaining processing data from a processing data acquisition control system, which includes a plurality of processing parameter values ​​relating to the first processing step for the first product quantity (214, 216, 218); and obtaining raw material data from the processing data acquisition control system, which includes a plurality of raw material parameter values ​​relating to a plurality of raw materials forming the basis for the first product quantity (210); A step (234, 240) of determining a first relationship score indicating a first relationship between each of the plurality of processing parameter values ​​and the first product component parameter value, and / or a second relationship score indicating a second relationship between each of the plurality of raw material parameter values ​​and the first product component parameter value, based on the plurality of processing parameter values, the plurality of raw material parameter values ​​and the first product component parameter value, A method further comprising the step (242) of selecting a predetermined amount of at least one of a plurality of processing parameters and a plurality of raw material parameters, which has a relationship score that shows the strongest relationship with the first product component parameter value.

2. The process further includes at least one of the following steps: obtaining processing data from a processing data acquisition control system, which includes at least one second processing parameter value relating to the first processing step for a second product quantity; and obtaining raw material data from the processing data acquisition control system, which includes at least one second raw material parameter value relating to the raw materials that form the basis for the second product quantity (210, 214, 216, 218). The method according to claim 1 (100), wherein the step (234, 240) for determining the first relation score and / or the second relation score is also based on at least one of the second processing parameter value and the second raw material parameter value.

3. The method according to claim 1 (200), wherein the brewing process includes a batch process, and the first product quantity includes at least one batch.

4. The first processing parameter value has a first processing parameter timestamp associated with it that indicates the starting point of the first processing step, The method according to claim 1 (200), wherein the first raw material parameter value has at least one associated with a first component timestamp indicating the moment in time at which the first raw material is added to the first product quantity.

5. The method according to claim 1 (200), wherein the brewing process is a continuous process, further comprising a step of obtaining a throughput time interval of the first processing step that indicates the amount of the first product that has gone through the first processing step.

6. The method according to claim 1 (200), wherein the first processing parameter value is one of the control value and the measured value.

7. The method according to claim 1 (200), wherein the first raw material parameter value is one of the weight of the amount of the first raw material, the volume of the first raw material, and the quality of the first raw material.

8. A step (236) to obtain an objective quality relationship between numerical or Boolean quality score parameter data and at least one product component parameter, obtained by measuring or counting the values, The method according to any one of claims 1 to 7 (200), further comprising the step of determining an objective quality score value obtained by measuring or counting the value of the first product quantity based on the quality relationship and the value of the at least one product component parameter.

9. A step (232) to obtain a first quality score value for the first product quantity, A step (232) to obtain a second quality score value for the second product quantity, The method according to claim 2 (200), further comprising the step (238) of determining a quality relationship between the first product component parameter and the quality score based on the first quality score value, the second quality score value, the at least one first product component parameter value for the first product quantity, and the at least one second product component parameter value for the second product quantity.

10. A step (232) to obtain a first quality score value for the first product quantity, A step (232) to obtain a second quality score value for the second product quantity, The method according to claim 2 (200), further comprising the step (238) of determining a quality relationship between the processing parameter and the quality score based on the first quality score value, the second quality score value, at least one processing parameter value of the first product quantity, and at least one processing parameter value of the second product quantity.

11. A step (232) to obtain a first quality score value for the first product quantity, A step (232) to obtain a second quality score value for the second product quantity, The method according to claim 2 (200), further comprising the step (238) of determining a quality relationship between the raw material parameter and the quality score based on the first quality score value, the second quality score value, at least one raw material parameter value of the first product quantity, and at least one raw material parameter value of the second product quantity.

12. The method according to claim 1 (200), wherein the relation score indicates the correlation between the parameters for which the relation score was determined.

13. The method according to claim 1 (200), wherein the obtained first processing parameter value and the obtained first raw material parameter value are provided to a neural network, and the relation score is provided by the neural network.

14. The first processing parameter value is temperature, Conductivity, color, Transparency to visible light, Relative weight, acidity, sugar content, viscosity, Added energy, The method according to claim 1 (200), which is one of the temperature control methods for a machine that performs the first processing step.

15. The first product component parameter is as follows: Ethanol content, Methanol content, sugar content, The method according to claim 1 (200), wherein the carbon dioxide content is at least one of the following.

16. An electronic process analysis device for selecting at least one predetermined amount of a plurality of processing parameters with respect to a first product amount of beer that has undergone a first processing step as part of the brewing process, based on determining the relationship between at least one of a set of processing parameters of the first processing step and, on the one hand, a set of raw material parameters and on the other hand, a relationship between at least one first product component parameter, It is an input, - Identifying at least one first product component potentially present in the first product quantity, - To obtain at least one first product component parameter value for the first product quantity relating to the first product component, - An input configured to perform at least one of the following: obtaining processing data from a processing data acquisition control system, which includes a plurality of first processing parameter values ​​related to the first processing step of the first product quantity; and obtaining raw material data from the processing data acquisition control system, which includes a plurality of raw material parameter values ​​related to a plurality of raw materials that form the basis of the first product quantity. Based on the plurality of first processing parameter values, the plurality of raw material parameter values, and the first product component parameter value, a first relationship score indicating a first relationship between each of the plurality of first processing parameter values ​​and the first product component parameter value, and / or a second relationship score indicating a second relationship between each of the plurality of raw material parameter values ​​and the first product component parameter value is determined. A processing unit (102) configured to select a predetermined amount of at least one of the processing parameters and raw material parameters having a relationship score that shows the strongest relationship with the first product component parameter value, An electronic process analysis device (102) comprising: an output configured to provide data relating to at least one of the relationships or relationship scores indicating the relationship.

17. A computer program comprising computer-executable code that, when executed by one or more processors of the electronic process analysis device (102) described in claim 16, causes the electronic process analysis device (102) to perform the method described in any one of claims 1 to 7.

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