Method for controlling the manufacturing process of pulp, paper or board

Abstract

A method, an apparatus, and a computer program for controlling a process for manufacturing pulp, paper, or board. Dosing of a chemical agent into a fiber suspension during the process is automatically controlled by a rule engine. The rule engine receives a plurality of measurement values related to the process and maintains a plurality of rule sets including an active rule set. Each rule set defines how the dosing depends on the measurement values. The rule engine controls the dosing of the chemical agent based on the measurement values and the active rule set, records at least a part of the measurement values and the actual dosing amount of the chemical agent, and receives at least one rule set candidate. Based on the previous measurement values, the dosing of the chemical agent is simulated using the received at least one rule set candidate, and a simulation result for optimizing the performance of the chemical agent in the process is output.

Description

Technical Field

[0001] The disclosure of the present application (hereinafter referred to as the present disclosure) generally relates to methods for controlling the manufacturing processes of pulp, paper, or board. Background

[0002] Note that this section describes useful background information, but it is not admitted that the techniques described herein represent the state of the art.

[0003] The production of pulp, paper, and board is each typically based on natural polymers obtained from wood. In each case, pulp is produced or used to form an aqueous suspension of lignocellulosic fibers consisting mainly of cellulose and hemicellulose. Mechanical pulp can also contain significant amounts of lignin.

[0004] Since the fibrous suspension, which is a natural product, can be fermented by microorganisms, the manufacturing process is vulnerable to quality impairment due to the action of unwanted microorganisms. To control the activity of microorganisms, biocidal chemicals may be administered to the manufacturing process. Further, various chemical additives such as retention aids, strength agents, defoamers, fixing agents, dyes, sizing agents for hydrophobizing paper, dispersants, bleaching agents, pH adjusters, fillers, etc. may be added to the fiber suspension to control various properties of the finished pulp, paper, or board. The administration to the fiber suspension may be carried out directly on the fiber suspension or via an intermediate substance such as water. Regardless of what the chemical agent is, generally, it is desirable to optimize the performance of the chemical agent in the process while achieving the goals required in the process conditions or final product quality and avoiding overuse. These goals include opacity, brightness, printability, color, water resistance or grease resistance, tensile strength, square mass uniformity, board hygiene, process productivity (low number of breaks), low microbial activity during the process, stable process pH and conductivity, and low amount of slime or other deposits on machine surfaces.

[0005] Since the situation within the process is constantly changing, it is well understood that it is beneficial to adaptively control the dosing of chemical agents. However, for obtaining good performance, the adaptive dosing control of chemical agents needs to take into account multiple simultaneous changes, some of which may have opposing effects. Also, it is difficult or impossible to obtain online measurements of some desired properties of the produced substances. Therefore, it is particularly desirable to enable rapid and accurate adjustment of the chemical agent dosing control process and to correct the identified drawbacks in dosing control. Abstract

[0006] The appended claims define the scope of protection. Matters not covered by the claims among the examples of devices, products and / or methods and the technical descriptions in the description and / or drawings of this specification are presented not as embodiments of the present invention, but as background art or examples helpful for understanding the present invention.

[0007] According to a first exemplary aspect, a method as defined in appended claim 1 is provided.

[0008] According to a second exemplary aspect, an apparatus as defined by appended claim 14 is provided. This apparatus may comprise a communication interface configured to receive measurement values and at least one processor configured to cause the apparatus to execute the method of the first exemplary aspect.

[0009] According to a third exemplary aspect, a computer program having computer-executable program code that, when executed by at least one processor, causes at least a computer to execute the method of the first exemplary aspect is provided.

[0010] According to a fourth exemplary aspect, a computer program product having a non-volatile computer-readable medium storing the computer program of the third exemplary aspect is provided.

[0011] According to a fifth exemplary aspect, an apparatus is provided that includes means for performing the method of any of the preceding aspects.

[0012] The computer-readable medium described above may be composed of a digital data storage device such as a data disk or a floppy disk, an optical storage device, a magnetic storage device, a holographic storage device, a magneto-optical storage device, a phase change memory, a resistive random access memory, a magnetic random access memory, a solid electrolyte memory, a ferroelectric random access memory, an organic memory, or a polymer memory. These storage media may be mounted on a vice without having functions substantially other than the storage function. Further, the storage media may be formed as part of a device having other functions, and as a non-limiting example, may be a memory of a device such as a computer, a chip set, or a sub-assembly of an electronic device.

[0013] Although various aspects and embodiments have been introduced, these are not presented to limit the scope of the invention. These embodiments have only been used to explain specific aspects and steps that can be used in various implementations. Depending on the embodiment, it may be presented only with reference to specific exemplary aspects. It should be understood that the corresponding embodiments are also applicable to other aspects.

Brief Description of the Drawings

[0014] Some embodiments will be described with reference to the following accompanying drawings.

Figure 1

Figure 2

Figure 3

[0015] In the following description, like reference numerals indicate like elements or steps.

[0016] FIG. 1 schematically shows a process apparatus 100 in which a pulp, board, or papermaking process is performed. The process apparatus 100 includes a chemical agent dispenser 110 configured to administer a chemical agent to a fiber suspension. The process apparatus 100 also includes a rule engine 120, a tank 130, and a plurality of sensors 140. The process rule engine 120 includes a rule set 122 and a communication interface 124. The rule set 122 includes rules, and the dispenser 110 is controlled by these rules to administer the chemical agent. The rule engine inputs measured values and controls the administration according to the rule set. In some embodiments, the rule engine 120 is implemented by a computing system such as a dedicated server, a virtualized server, cloud computing, or a general-purpose computer. In some embodiments, the rule engine includes logical components and one or more rule sets. The logical components are configured to control the operation of the rule engine 120 according to the active rule set currently used by the rule engine 120.

[0017] As depicted in FIG. 1, the rule engine 120 can store a plurality of rule sets 122. The rule engine 120 can also use one of the rule sets 122 as the active rule set that defines how the rule engine 120 controls the administration of the chemical agent.

[0018] In some embodiments, the administration of the chemical agent includes being performed by batch administration. In some embodiments, the administration of the chemical agent includes being performed by continuous administration. Further, the process of manufacturing pulp, board, or paper itself may be a batch process and / or a continuous process, or may include a batch process and / or a continuous process.

[0019] Dosing (administration / introduction) can be performed at any one or more dosing points (dosing points / introduction points) in the pulp, paper, or board manufacturing process. For example, it can be performed at dosing points such as within a pipeline, within a tank, within a large storage tower, within a fractionator, within a concentrator, within a headbox, and / or within a dewatering section. When controlling dosing, the amount and / or timing of dosing may be adjusted. Additionally, or alternatively, the distribution of dosing may be adjusted between two or more dosing positions.

[0020] In some embodiments, dosing is controlled by adjusting how much of a given chemical agent is administered to the fiber solution. Additionally, or alternatively, in some embodiments, dosing may be adjusted by changing the composition or concentration of the chemical agent. For example, the effect can be increased by increasing the dosage or by administering one or more more effective chemical substances. One or more rules in the rule set may define how to perform dosing for different desired effect levels. One or more rules within the rule set may define how dosing is to be performed for various paper grades or board grades during manufacturing.

[0021] The phrase "with the above (manufacturing) process" may mean the entire manufacturing process of pulp, paper, or board, or only a part thereof. In some embodiments, the process includes buffering the fiber suspension. Buffering can be performed, for example, using an intermediate storage included in the process equipment. In FIG. 1, the storage is tank 130. In some embodiments, the storage is a storage tower.

[0022] Sensor 140 may include two or more sensors that measure the same quantity, and in some embodiments these have the same scale. The sensors may also be composed of a plurality of different sensors that measure different characteristics or different scales of the same characteristic.

[0023] Further, the sensor 140 may have one or more status sensors configured to indicate various states of the process device (such as the states of pumps, motors, or valves). For example, the states of a storage mixing pump or a shredder motor may be provided by the status sensors. In some embodiments, the status sensors are part of the hardware. In some embodiments, the status sensors are implemented by software. For example, the status sensors may be formed by a process automation system that outputs the states of the process device or an actuator that drives the process device.

[0024] In some embodiments, the pulp, board, or papermaking process of the present application includes a suspension of shredded accumulated papermill waste (broke). This suspension is also stored. The suspension can be an aqueous suspension with a relatively low dry content. In some embodiments, the suspension is supplied intermittently or continuously and stored in a tank. In some embodiments, the tank supplies the suspension stored in the tank to downstream equipment intermittently or continuously. For the sake of illustration, assume that the suspension is stored in a cylindrical through-storage tank with an inlet for the suspension at the top and an outlet connection at the bottom. Depending on the processing speed, the average transfer time of the suspension is, for example, 2 to 20 hours. However, a dead zone may appear near the outlet end, where the previously stored suspension is exchanged at a much slower rate than the average. In some embodiments, biocides are administered so that the fermentation activity of microorganisms in the tower is as low as possible. The microbial fermentation activity may depend on multiple factors. For example, it may depend on the dry weight content in the suspension, the homogeneity of the suspension, the storage mixing efficacy, the inventory of the suspension, the temperature, the time since the previous cleaning of the process equipment, the time since the storage tank was last completely emptied, the microbial activity in another part of the manufacturing process, etc., and may also depend on yet other factors. As used herein, the term "inventory" means the manufacturing time that can be filled by the material being stored (here, the fiber suspension).

[0025] In the simplest case, the dosing of the chemical agent is proportional to one parameter, such as the inflow rate of the suspension. For more advanced control, more parameters are combined, such as the inventory of the storage tower and the pH of the suspension. Assuming that the fermentation activity is the biocide, a large inventory means that the microorganisms have more time to grow. Also, a decrease in pH is an indicator that the fermentation of the microorganisms is progressing. Both of these parameters can be used to adjust the dosing rate of the biocide. However, there may be more parameters that affect the process and should be considered for chemical agent dosing. In such cases, it is difficult to define process automation rules for controlling the dosing (administration). In particular, it is time-consuming and difficult to define process automation rules for all possible scenarios. In practice, it may not be possible to predict all scenarios, especially at the start of chemical agent dosing. It may also be found retrospectively that the dosing has failed, with an excessive increase in the consumption of the chemical agent or the process has failed resulting in poor quality products.

[0026] Normal process automation can be adjusted relatively easily by changing coefficients such as offsets and multipliers applied to each measured value of the dosage calculation formula. However, similar to the difficulty of predicting the scenarios in the aforementioned process development, it is extremely difficult to predict which parameters should be used to control the dosage. Perhaps some new measured values should be added. Or perhaps new combined parameters formed by combining multiple measured values with each other and / or with time should be formed. For example, the elapsed time since the newer of two events, a) the most recent cleaning of the process device, and b) the time when the surface level of the suspension reached one or more specific levels in the storage tank, may be meaningful for the administration of the chemical solution. Or perhaps some parameter based on time, temperature, pH, previously measured microbiological activity, and residence time of the suspension may be meaningful for determining the dosage. In such a case, the residence-time-based parameter may be defined for the 10% volume portion of the suspension that has resided for the longest time during storage. Or perhaps it may be defined for the average or any combination of such derived parameters. Also, tracking parameters such as luminance, pH, conductivity, or inventory over time and identifying abrupt changes by comparing the latest value with the past average value would be meaningful for determining dosing. Such combined parameters can be formed by any function, including addition, subtraction, multiplication, exponentiation, variance, standard deviation, logarithm, look-up table-based functions, and any combination thereof.

[0027] Depending on the embodiment, various parameters are used to control dosing in various situations. For example, the rule set 122 in use may include a defined range or scenario for dynamically changing the control of administration while the rule set 122 is in use. Depending on the embodiment, the rule set may define one or more stop rules configured to change the operation of the active rule set. The operation may be changed by performing an additional start or stop of dosing. Depending on the embodiment, dosing is stopped, for example, when the set limit of the maximum dose is reached within a given time. Depending on the embodiment, a measurement reaching a set limit value indicating the attainment of a sufficient dosing response, or a measurement reaching a set limit value for a low flow situation in a particular process line, for example, may be a trigger for stopping dosing. Depending on the embodiment, in the case of batch dosing, additional dosing is triggered through the stop rule. For example, when the process parameters reach a set threshold limit value indicating the urgent need for an additional dosing batch, the additional dosing is started. This parameter may be, for example, that the pH of the process is low, that the oxidation state of the process (such as oxidation-reduction potential ORP or redox process) is low, that the conductivity is high, that the inventory is high, that another production grade has been started, or that the residence time in the storage tank has reached the set limit time.

[0028] Optimizing the dosage empirically by gradually changing process automation is rather unrealistic. New scenarios may occur before a rare process scenario is properly addressed. Therefore, it is desirable to form, adapt, and test various rule sets 122 based on previous measurements made on the process by simulating how dosing would occur in response to process changes with those previous measurements. In some embodiments, the simulation further estimates one or more characteristics in the product. Alternatively, the simulation facilitates the expert evaluation of appropriate changes in the active rule set and enables the avoidance of undesirable quality issues or excessive chemical usage. Advantageously, the simulation enables showing how one or more alternative new rule sets would have controlled the dosing under actual past process conditions. Thus, the expert can use past measurements to simulate and verify dosing control with any rule set as desired and adopt the desired rule set as the new active rule set for production. In this way, the verification of process control improvements can be significantly accelerated. As a result, the production of pulp, paper, and board can be improved much faster and more sensitively than before. As a result, quality issues, production interruptions, and / or excessive chemical usage can be reduced.

[0029] In some embodiments, the chemical agent includes a biocidal chemical, an antifoaming agent, a retention chemical, a retention polymer, a strength chemical, a fixing agent, a dye, a hydrophobing agent, a dispersing agent, a bleaching agent, and / or a pH adjuster.

[0030] In some embodiments, the measurements related to the process include any of the state of the actuator, the flow rate of the process material, temperature, time, the filling rate of the reservoir, the break time of the process, process conditions, the characteristics of the product of the process (such as the moisture content, brightness, board strength, change in opacity, track break frequency, etc. of the produced paper or board), the consumption of chemical substances, the air content, the speed of the process flow, and / or the electrical conductivity.

[0031] In some embodiments, the rule engine 120 controls at least one or two dousing points. In some embodiments, the rule engine 120 controls at most 10, 8, 6, or 5 dousing points. In some embodiments, the rule engine 120 is coupled to the dozer 110. In some embodiments, another controller exists between the rule engine 120 and one or more dozers 110. The controller may be, for example, a programmable logic controller (PLC). In some embodiments, the rule engine 120 is operable offline when there is no network connection. In some embodiments, the controller is configured to perform process control in an offline situation.

[0032] FIG. 2 is a block diagram of an apparatus 200 according to an exemplary embodiment. The apparatus 200 includes a communication interface 210, a processor 220, a user interface 230, and a memory 240.

[0033] The communication interface 210 includes, in some embodiments, wired and / or wireless communication circuits such as Ethernet, Wi-Fi, Bluetooth, GSM, CDMA, WCDMA, LTE, and / or 5G circuits. The communication interface may be integrated into the apparatus 200 or mounted as part of an adapter, card, etc. attachable to the apparatus 200. The communication interface 210 may support one or more different communication technologies. Also, the apparatus 200 may include one or more communication interfaces 210.

[0034] As used herein, the term processor may represent a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microcontroller, or a combination of these elements.

[0035] The user interface may include circuitry for receiving input from a user of the apparatus 200 via, for example, a keyboard, a graphical user interface displayed on the display of the apparatus 200, a voice recognition circuit, or an accessory such as a headset. Further, it may include circuitry for providing output to the user via, for example, a graphical user interface or a speaker.

[0036] The memory 240 includes a working memory 242 and a non-volatile memory 244 configured to store computer program code 246 and data 248 such as the rule set 122 of FIG. 1. The memory 240 may include one or more of a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a random access memory (RAM), a flash memory, a data disk, an optical storage device, a magnetic storage device, a smart card, a solid-state drive (SSD), etc. The apparatus 200 may include a plurality of memories 240. The memory 240 may be configured as part of the apparatus 200 or as an attachment inserted into slots or ports of the apparatus 200 by a user, another person, or a robot. The memory 240 may be for the sole purpose of storing data or may further serve other purposes such as processing data.

[0037] Those skilled in the art will understand that, in addition to the elements shown in FIG. 2, the apparatus 200 may include other elements such as a microphone, a display, etc., and may include application-specific processing circuitry such as input / output (I / O) circuitry, memory chips, application-specific integrated circuits (ASICs), source encoding / decoding circuitry, channel encoding / decoding circuitry, encryption / decryption circuitry, etc.

[0038] In some embodiments, the apparatus 200 is an apparatus implemented by a computer cloud. In some embodiments, the apparatus 200 is a virtual apparatus implemented by cloud computing, one or more computer servers, and / or one or more server clusters.

[0039] According to some embodiments, the apparatus 200 is or comprises an edge device.

[0040] Figures 3a and 3b show a flowchart according to an exemplary embodiment for explaining a method for controlling a process for manufacturing pulp, paper, or board. This flowchart shows various steps and some optional steps, but the method may include additional steps and / or some of the steps may be executed multiple times. 301. Automatically control the dosing of a chemical agent into a fiber suspension during the process by a rule engine. 302. Receive a plurality of measurement values related to the process by the rule engine. 303. Maintain, by the rule engine, a plurality of rule sets including an active rule set. Each rule set defines how the dosing depends on the measurement values. 304. Control the administration of the chemical agent based on the measurement values and the active rule set by the rule engine. 305. Record, by the rule engine, at least a part of the measurement values and the actual dosage of the chemical agent. 306. Receive at least one rule set candidate. 307. Based on the previous measurement values, simulate the dosing of the chemical agent using the received at least one rule set candidate. 308. Output the simulation results for optimizing the performance of the chemical agent in the process, for example, graphically or to a storage medium. 309. Use, as the chemical agent, a biocidal chemical agent, an antifoaming agent, a retention chemical agent, a retention polymer, a strength chemical agent, a fixing agent, a dye, a hydrophobizing agent, a dispersant, a bleaching agent, and / or a pH adjuster. 310. Obtain a selection of a new rule set based on the simulation. Adapt the control of the dousing to the situation by activating the selected new rules set by the rule engine. Transport the rule set and the previous measurement values used for the control of the dousing to the twin model. Here, the twin model and the rule engine may be implemented by the same computing entity. Also, the twin model may represent a replication of the rule engine. Derive one or more parameters from, for example, one or more measurement values, and also time in some embodiments, using the rule set. Control the error handling of the measurement values by one or more rules of the active rule set. Define one or more chemical agent administration start triggers (such as one or more parameter and / or time conditions) using the rule set. Define one or more chemical agent administration end triggers (such as one or more parameter and / or time conditions) using the rule set. Define one or more chemical agent dosage adaptation criteria using the rule set. Execute a simulation on a computer cloud entity. Visualize the dousing (administration) using at least one rule set candidate in the simulation. Visualize one or more measurement values on which the dousing depends, together with the dousing. Output a graphical representation of the control sequence when the situation-adapted rule engine is applied to the recorded past measurement values.

[0041] Next, a significantly simplified example will be described. The problem process is to control the dosing of a biocide into a storage tower for a fibrous suspension when manufacturing partially recycled paper. The dosing is carried out directly for the inflow of a fiber suspension consisting partly of waste paper pulp and partly of virgin fibers. First, the rule engine controls the dosing based on two measured values of the suspension after the storage tower, the inflow rate (x1), and the pH (x2). The rule set includes the following rules. Rule 1: d = i·x1 This is for setting the dosage per unit amount (such as volume or mass) of the incoming fiber suspension. Here, d defines the dosage, and i defines an appropriate basic dosage level per volume. Rule 2: If x2 < k, then set d to d·(1 + j·(k - x2)) Here, x2 is the pH, and j and k are coefficients.

[0042] This rule set is based on the assumption that a basic dosage is required in direct proportion to the inflow of the suspension. However, changes in the composition of the suspension may cause an increase in biological activity. An increase in biological activity is indicated by a decrease in the pH of the suspension. Therefore, according to the second rule, additional dosing is carried out when the pH becomes less than k. k may be, for example, 6.5. The amount to be added is scaled by j.

[0043] When the parameters and the actual dosage are recorded, historical data can be used to graphically show how the chemical dosing was controlled during the process according to the rule set. However, there is a need to improve the rule set. For example, it may be noticed that the higher the surface level (surface height) (the more inventory in the storage tower), the longer the residence time and the stronger the microbial fermentation. Therefore, when the surface level exceeds a predetermined threshold, stronger dosing may be added. For example, it may be noticed that paper quality deterioration is occurring when the tank is empty.

[0044] The first rule set candidate has Rule 1 and Rule 2 as before, and additionally has Rule 3. Rule 3: If x3 > l, set d to d · m Here, x3 is the surface level of the tank, l is the threshold of the surface level, m is the dosing enhancement constant, and m is, for example, 1.15.

[0045] The second rule set candidate has Rule 1 and Rule 2, and has Rule 4. Rule 4: If x3 > l, set d to d · (1 + m · (x3 - l))

[0046] In the simulation, two rule set candidates are applied to past observation results (selected period, e.g., one week), and how the effect of doping changes according to the applied rule set is observed. As output, the corresponding graph can be displayed, and the consumption amount of the chemical agent and / or other characteristics can be provided to expert users who develop doping control. Then, it is possible to easily see where and how different rule sets react to different changes in process conditions and how doping is adjusted. Here, it is shown how the first rule set candidate can trigger a gradual intensification of doping when the surface level exceeds the surface level threshold l. In certain past data, when the surface level fluctuated below and above this threshold level, the dosage of the first rule set candidate is considered to have changed in a series of abrupt steps. In the second rule set candidate, the simulation results show that the dosage changes gradually and there are no major disruptions. Next, the desired rule set is selected, in this example the second rule set candidate is selected, and this selection is provided to the rule engine. Then, the active rule set is replaced by the rule engine in order to adopt the new rule set in the doping control for controlling the manufacturing process. Therefore, it is possible to avoid over-doping that is too excessive in an attempt to be on the safe side. Instead, doping can be carried out only in truly necessary situations, so that the total consumption amount of the chemical agent can be minimized and the quality of the process and product can be made more stable than before. In reducing the margin in doping, the accuracy of doping control becomes increasingly important. Depending on the embodiment, this increasing need is addressed by supporting the sensitive verification of new rule set candidates without experimenting on the production process itself.

[0047] Since the situation of the process can change greatly in an unexpected manner, it is extremely difficult to predict all the requirements for the rule set. Sometimes, a good rule set may fail in unexpected situations. Especially when using derived parameters or when modifying a certain rule set, it is very difficult to predict how it will behave under different situations. By using past data and simulating various rule set candidates with these data, the response time in production management can be significantly shortened, and production management can be improved much more effectively than before. Dosing errors, inaccuracies, and the resulting process disruptions may also be avoided or reduced.

[0048] In some embodiments, one or more rules of the active rule set control the handling of measurement errors. For example, one rule can define that the ORP measurement is reliable in the range from -100 mV to +400 mV. This rule can enable measurements within this range. Conversely, when the measured value is -120 mV, this measurement verification rule defines that the measured value is not reliable. While the measured value is considered invalid, the rule may replace the measured value with an error masking value such as a predefined safety value. The safety value may be selected from a predetermined point in the middle of the range. In another example, the safety value is determined based on the latest valid value. The safety value may be defined dynamically. For example, when the measured value decreases to the end point of the valid range and further decreases beyond it, the safety value may be dynamically defined as the value closest to the measured value and existing within the valid range.

[0049] Other measured values and additional measured values, such as conductivity linked to the surface level and the elapsed time since the final cleaning of the storage tower, can also be used in other rule set candidates.

[0050] Depending on the embodiment, the active rule set is monitored for efficiency. For example, the actual chemical dosing may be recorded. The monitored data may be compared with one or more fallback criteria to trigger a fallback action where, if the active rule set appears not to function properly, a fallback rule set is at least temporarily adopted in place of the active rule set. Efficiency may mean, for example, how well the active rule set functions in process control from the perspective of chemical consumption and / or quality measurements.

[0051] The fallback criteria may be at least partially or wholly global and applicable to all rule sets. Alternatively, or additionally, there may be fallback criteria defined within one or more rule sets. The fallback criteria defined within a rule set may take precedence over global fallback rules.

[0052] The fallback criteria may define one or more comparisons for the monitored data such that a given period (or parameter) exceeds, meets, or falls below a limit value. This period may be defined based on one or more different measurements. This period may be defined based on real-time data. The period may be defined based on data accumulated over a comparison period of at least 10 minutes, 30 minutes, 1 hour, 4 hours, 12 hours, 24 hours, 48 hours, or 168 hours. The period may be any of an average, mean value, standard deviation, variance, total, or any combination thereof, or be composed of these. For example, the period may be the average concentration of a chemical in a suspension during the most recent 6 hours of a comparison period, or the dosage of a chemical, or the total of any (derived) measurement. The comparison period may be the ongoing day, week, or other repeated period.

[0053] In cases such as where there are multiple different process control systems each using its own rule set, the fallback criteria may be defined for each of the multiple rule sets used simultaneously.

[0054] In cases such as where there are multiple different process control systems each using its own rule set, a fallback rule set may be defined for each of the multiple rule sets used simultaneously.

[0055] The fallback rules may define a fallback action, such as stopping the chemical agent administration or reducing the chemical agent dosage during a predetermined correction period. The fallback action may include alerting an operator. The correction period may continue for at least 10 minutes, 30 minutes, 1 hour, 4 hours, 12 hours, or 24 hours.

[0056] The fallback criteria may include one or more criteria regarding the stability of the process.

[0057] The foregoing method, method steps, or combinations thereof can be controlled or executed using hardware, software, firmware, or combinations thereof. The software and / or hardware can be local, distributed, centralized, virtualized, or any combination thereof. Further, any form of computer system including computing intelligence may be used to control or execute any of the foregoing method, method steps, or combinations thereof. The computing intelligence may mean, for example, any one of artificial intelligence, neural network, fuzzy logic, machine learning, genetic algorithm, evolutionary computation, or any combination thereof.

[0058] As a technical effect, the measurement history regarding the process can be used together with a simulated rule engine to optimize the dosing of chemical agents. In particular, in order to optimize the performance of chemical agents, the responses of various rule engines can be determined immediately, and the dosing of chemical agents can be optimized. The simulation may include calculating one or more estimated characteristics of the final product.

[0059] Various embodiments have been introduced. Phrases such as "having", "comprising", and "including" should be construed as open-ended and do not exclude the presence of other elements.

[0060] The above description has provided a complete and useful explanation of the best mode for carrying out the present invention, as currently contemplated by the inventors, using specific implementations and non-limiting examples of embodiments. However, as will be apparent to those skilled in the art, the details of the above embodiments do not limit the present invention, and other embodiments can be implemented using equivalent means or combinations of various embodiments without departing from the features of the present invention.

[0061] Furthermore, the features of the exemplary embodiments disclosed above may be used without using corresponding other features. However, the above description should be regarded as merely an example for explaining the principle of the present invention and not as limiting it. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for controlling the manufacturing process of pulp, paper, or board, The dosing of chemical agents into the fiber suspension during the process is automatically controlled by a rule engine; The rule engine receives multiple measurements relating to the process; The rule engine maintains multiple rule sets, including the active rule set; This includes, however each of the multiple rule sets defines how dosing depends on the measurement, and the method further, The rule engine controls the administration of the chemical agent based on the measured value and the active rule set; The rule engine records at least a portion of the measured values ​​and the actual dose of the chemical agent; It must accept at least one candidate rule set; Based on the previously measured values, simulate the dosing of the chemical agent using the received candidate rule set; To output simulation results for optimizing the performance of the chemical agent in the process; Based on the aforementioned simulation, obtain a selection of a new rule set; By activating the newly selected rule set by the rule engine, the dosing control is adapted to the situation; Methods that include...

2. The method according to claim 1, comprising transporting the rule set used for dosing control and the aforementioned measured values ​​to a twin model.

3. The method according to claim 1, wherein the rule set is configured to derive one or more parameters from one or more measurements and, in some embodiments, also using time.

4. The method according to claim 1, comprising controlling error handling of measurement values ​​by one or more rules of the active rule set.

5. The method according to claim 1, comprising performing the simulation by the rule engine.

6. The method according to claim 1, comprising defining one or more triggers for initiating the administration of a chemical agent by the rule set.

7. The method according to claim 1, comprising defining one or more triggers for termination of chemical agent administration by the rule set.

8. The method according to claim 1, comprising performing the simulation by a computer cloud system.

9. The method according to claim 1, further comprising outputting a graphical representation of a control sequence if a situation-adapted rule engine were applied to recorded historical measurements.

10. The method according to claim 1, wherein the fiber suspension comprises cellulose and hemicellulose, and also comprises at least 0.5, 1, 2, 5, 10, or 15% by weight of dried material.

11. The method according to claim 1, wherein the fiber suspension contains cellulose and hemicellulose, and also contains at most 0.5, 1, 2, 5, 10, or 20% by weight of the dried product.

12. The method according to claim 1, wherein the chemical agent comprises a biocide, an antifoaming agent, a retention chemical, a retention polymer, a strengthening chemical, a fixative, a dye, a hydrophobic agent, a dispersant, a bleaching agent, and / or a pH adjuster.

13. To monitor the aforementioned active rule set; Deciding to use a fallback criterion; As a result of deciding to use the aforementioned fallback criteria, instead of continuing to control the process according to the active rule set, a fallback action will be performed; The method according to claim 1, further comprising:

14. An apparatus comprising means for performing the method described in any one of claims 1 to 13.

15. A computer program comprising computer executable program code, which, when executed, causes a device to perform the method according to any one of claims 1 to 13.

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