Method for controlling a process for making a pump, paper or board

Abstract

A method, apparatus and computer program control a process of making pulp, paper or paperboard, dosing of chemicals in the process to a fiber suspension being automatically controlled by a rule engine. A rule engine receives a plurality of measurements related to a process and maintains a plurality of rule sets, including an active rule set. Each rule set defines how the dosing depends on the measurement. The rules engine controls the dosing of the chemical agent based on the measurements and the active rule set, records at least some of the measurements and the actual dosing of the chemical agent, and receives at least one rule set candidate. Simulating a dose of a chemical agent with the received at least one rule set candidate based on an early measurement; and outputting a simulation result to optimize the performance of the chemical agent in the process.

Description

Technical Field

[0001] The present disclosure generally relates to a method for controlling a process for making pulp, paper or paperboard. Background Art

[0002] This section provides useful background information but does not constitute an admission that any of the techniques described herein represent the state of the art.

[0003] The production of pulp, paper and paperboard is each based on natural polymers usually obtained from wood. In each case, pulp is either manufactured or used to form a fibrous aqueous suspension of wood fibers, which are mainly cellulose and hemicellulose. Mechanical pulp may also include a large amount of lignin.

[0004] As a natural product, the fiber suspension can be fermented by microorganisms, so the process will be disturbed by quality due to the undesirable microbial action. Biocides can be added to the process to control microbial activity. In addition, various chemical additives (such as retention aids, strength agents, defoamers, fixatives, dyes, hydrophobization of paper with sizing agents, dispersants, bleaching agents, pH control agents and fillers) can be added to the fiber suspension to control the various properties of finished pulp, paper or paperboard. Depending on the specific embodiment, the agent added to the fiber suspension can be added directly to the fiber suspension or added via an intermediate substance (such as water). No matter which chemical agent is used, it is generally expected to optimize its performance in the process to avoid excessive use, and to achieve the goals required for process conditions or final product quality through the correct dosage, such as opacity, brightness, printability, color, water resistance or oil resistance, tensile strength, square quality uniformity, paperboard hygiene, process productivity (few line breaks), low microbial activity in the process, stable process pH and conductivity, low amount of mucus or other deposits on the machine surface, etc.

[0005] Conditions in a process are constantly changing, so adaptive control of the dosing of chemicals is generally considered beneficial. However, for good performance, adaptive dosing control of chemicals needs to account for multiple concurrent changes, some of which may have opposing effects. In addition, it is difficult or impossible to obtain online measurements of some desired properties of the produced material. Therefore, it is particularly desirable to enable a control process that allows rapid and accurate adjustment of chemical dosing in order to subsequently correct identified deficiencies in dosing control. Summary of the invention

[0006] The attached claims define the scope of protection. Any examples and technical descriptions of devices, products and / or methods not covered by the claims in this specification and / or drawings are not intended as embodiments of the present invention, but are intended as background technology or examples useful for understanding the present invention.

[0007] According to a first example aspect, there is provided a method as defined in accompanying claim 1 .

[0008] According to a second example aspect, there is provided an apparatus as defined in appended claim 14. The apparatus may comprise a communication interface configured to receive measurement results and at least one processor configured to cause the apparatus to perform the method of the first example aspect.

[0009] According to a third example aspect, a computer program is provided, the computer program comprising computer executable program code, which, when executed by at least one processor, causes the computer to perform at least the method of the first example aspect.

[0010] According to a fourth example aspect, there is provided a computer program product comprising a non-transitory computer readable medium having stored thereon the computer program of the third example aspect.

[0011] According to a fifth example aspect, there is provided an apparatus comprising means for performing the method of any preceding aspect.

[0012] Any of the foregoing storage media may include digital data storage devices, such as data optical disks or floppy disks; optical storage devices; magnetic storage devices; holographic storage devices; magneto-optical storage devices; phase change memory; resistive random access memory; magnetic random access memory; solid electrolyte memory; ferroelectric random access memory; organic memory; or polymer memory. The memory medium may be formed as a device having no other substantial functions besides storing memory, or may be formed as part of a device having other functions, including but not limited to the memory of a computer; a chipset; and a subassembly of an electronic device.

[0013] Different non-binding example aspects and embodiments have been described above. The embodiments above are only used to explain selected aspects or steps that can be used in different implementations. Some embodiments may be presented with reference to only certain example aspects. It should be appreciated that the corresponding embodiments may also be applicable to other example aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Some example embodiments will be described with reference to the accompanying drawings, in which:

[0015] Figure 1 schematically illustrates a process tool according to an example embodiment;

[0016] Figure 2 A block diagram showing an apparatus according to an example embodiment; and

[0017] Figure 3a and Figure 3b A flow chart according to an example embodiment is shown. DETAILED DESCRIPTION

[0018] In the following description, the same reference symbols denote the same elements or steps.

[0019] Figure 1 A process equipment 100 for performing a pulp, paperboard or paper making process is schematically shown, including a chemical dosing feeder 110 configured to add a chemical to a fiber suspension. The process equipment 110 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 according to which the dosing feeder 110 is controlled to add chemicals. The rule engine inputs measurement results and controls dosing according to the rule set. In an example embodiment, the rule engine 120 is implemented by a computing entity, such as a dedicated server; a virtualized server; a cloud computing or a computer with further tasks. In an example embodiment, the rule engine includes a logic component and one or more rule sets. The logic component is configured to control the operation of the rule engine 120 according to the active rule set currently being used by the rule engine 120.

[0020] like Figure 1 As shown in , the rules engine 120 can store multiple rule sets 122 and use one of the rule sets 122 as an active rule set that defines how the rules engine 120 controls the dosing of the chemical formulation.

[0021] In an example embodiment, the dosing of the chemical formulation includes batch dosing. In an example embodiment, the dosing of the chemical formulation includes continuous dosing. Moreover, the pulp, paperboard or paper making process itself can be or include a batch process and / or a continuous process.

[0022] Dosing can be applied at a dosing point at any one or more locations in the pulp, paper or board making process, such as in a pipeline, in a tank, in a large storage tower, in a fractionator, in a thickener, in a headbox and / or in a dewatering section. In controlling dosing, the amount and / or timing of dosing can be adjusted. Additionally, or alternatively, the distribution of dosing between two or more dosing locations can be adjusted.

[0023] In some example embodiments, dosing is controlled by adjusting how much of a given chemical is applied to the fiber solution. In example embodiments, dosing may also or alternatively be adjusted by changing the composition or concentration of the chemical. For example, increased effect may be obtained by either increasing the dosing or dosing some (one or more) more effective chemicals. One or more rules in a rule set may define how dosing is performed for different desired effect levels. One or more rules in a rule set may define how dosing is performed for different paper or paperboard grades in production.

[0024] The process may constitute the entire pulp, paper or board making process or a part thereof. In an example embodiment, the process comprises buffering the fiber suspension. The buffering may be performed, for example, using an intermediate storage device comprised by the process equipment. Figure 1 In the example embodiment, the storage device is a tank 130. In the example embodiment, the storage device is a storage tower.

[0025] The sensors 140 may include two or more sensors measuring the same quantity, optionally with the same scale. The sensors may include a plurality of different sensors measuring different properties or different scales of the same property.

[0026] Sensor 140 may also include one or more state sensors configured to indicate the corresponding state of process equipment such as pumps, motors, or valves. For example, the storage mixing pump or the shredder motor state may be provided by a state sensor. In an example embodiment, the state sensor is hardware. In another example embodiment, the state sensor is implemented by software. For example, the state sensor may be formed by a process automation system that outputs the state of the process equipment or an actuator that operates the process equipment.

[0027] In an exemplary embodiment, the pulp, paperboard or papermaking process discussed includes shredding and storing paper mill waste suspension (i.e., broke) in a storage device. The suspension can be a water suspension with a relatively low dry content. Depending on the embodiment, the suspension is stored in a tank with an intermittent or continuous supply. In an exemplary embodiment, the tank provides intermittent or continuous output to downstream equipment. For example, let us assume that the suspension is stored in a cylindrical straight-through storage device, which has an input at the top and an output connection at the bottom. Depending on the throughput rate, the average transfer time of the suspension is, for example, 2 to 20 hours. However, a dead zone may occur near the output end, where the previously stored suspension is replaced at a speed far below the average speed. In one example, the dosage of the biocide is adjusted so that the microbial fermentation activity in the tower is as low as possible. The microbial fermentation activity can depend on a variety of factors, including but not limited to the dry weight content in the suspension, the uniformity of the suspension, the storage mixing efficiency, the inventory of the suspension, the temperature, the time since the last cleaning of the process equipment, the time since the last complete emptying of the storage tank, and the microbial activity in other parts of the production process. In this context, the term stock refers to the production time that can be covered by the material (here the fiber suspension) in the storage device.

[0028] In the simplest case, the dosage of chemicals is proportional to a parameter (such as the inflow of suspension). Slightly more advanced control combines more parameters, such as storage tower inventory and the pH of the suspension. Assuming that the chemical is a biocide, the more inventory means the longer the time for microbial growth, and the pH drop is an indicator of increased microbial fermentation, and both parameters can be used to adjust the dosage rate of the biocide. However, there can also be multiple other parameters that have an impact on the process and should be considered in the dosage of chemicals. In such cases, it is laborious and difficult to define process automation rules to control dosage, particularly for each possible scenario. In practice, it may be impossible to predict all scenarios, particularly when starting chemical dosage. It may only be observed after the fact that the dosage fails because of excessive increase in chemical consumption or because of process failure resulting in poor product quality.

[0029] Normal process automation is relatively easy to adjust by changing the coefficients (such as offsets and multipliers) applied to each measurement in the formula that defines the dosage. However, consistent with the difficulty of predicting scenarios in process development mentioned above, it is extremely difficult to even predict which parameters should be used to control the dosage. Perhaps some new measurements should be added, or new combined parameters that combine multiple measurements with each other and / or with time should be formed. For example, for the dosage of chemicals, it may be meaningful how much time has passed since the more recent of the two events (a) the latest cleaning of the process equipment and b) the time when the surface level of the suspension reaches one or more specific levels in the storage device. Alternatively, time, temperature, pH, previously measured microbial activity and some residence time-based parameters of the suspension may be meaningful for determining the dosage. In this case, the residence time-based parameter may be defined for the 10% volume fraction of the suspension that has the longest residence time in the storage device, the mean, the average value, or any combination of such derived parameters. Moreover, it may be meaningful to track parameters such as brightness, pH, conductivity, or inventory over time and identify rapid changes by comparing the latest value with the average value in the past. Such combined parameters may be formed using any function, including but not limited to addition, subtraction, multiplication, power, variance, standard deviation, logarithm, lookup table based functions, and any combination of these functions.

[0030] In an example embodiment, different parameters are used to control dosing in different situations. For example, the rule set 122 in use may include a range or scenario defined for dynamically changing the control of dosing when using a specific rule set 122. In an example embodiment, the rule set may define one or more abort rules that are configured to change the operation of the active rule set. The operation may be changed by performing an additional start or stop of dosing. In an example embodiment, dosing is aborted, for example, when a set limit of the maximum dosing amount is reached within a given time. In an example embodiment, a possible trigger for aborting dosing is that a measured value reaches a set limit indicating that a sufficient dosing response has been reached or, for example, reaches a set limit for a low flow situation in a given process line. In an example embodiment, in the case of batch dosing, additional dosing is triggered via abort rules, for example, when a process parameter reaches a set threshold limit indicating that an additional dosing batch is needed quickly. For example, the parameter may be a low process pH, a low oxidation state of the process (e.g., redox potential ORP or redox), high conductivity, a large inventory, a different production level start-up, or a set time limit reached by the residence time in a storage tank.

[0031] It is quite impractical to optimize the dosage empirically by gradually changing the process automation. When a rare process scenario is properly solved, a new scenario will appear. Therefore, it is expected to form, adjust and test various rule sets 122 based on early measurement results related to the process by simulating how the dosage will respond to changes in the process with early measurement results. The simulation optionally also estimates one or more characteristics in the product. Alternatively, the simulation promotes experts to evaluate appropriate changes in the active rule set so that undesirable quality problems or excessive chemical use can be avoided. Advantageously, the simulation can make it possible to illustrate how one or more alternative new rule sets control the dosage according to actual historical process conditions. Therefore, experts can simulate the dosage control using any rule set with past measurement results according to expectations for verification, and then put the desired rule set into production as a new active rule set. Therefore, the verification of process control improvements can be significantly accelerated. Therefore, the manufacture of pulp, paper or paperboard can be improved faster and more flexibly than before. Therefore, quality problems, production interruptions and / or excessive chemical use can be reduced.

[0032] In an exemplary embodiment, the chemical formulation includes a biocide, a defoamer, a retention chemical, a retention polymer, a strength chemical, a fixative, a dye, a hydrophobic agent, a dispersant, a bleach, and / or a pH control agent.

[0033] In an example embodiment, process-related measurements include any of the following: state of an actuator; flow rate of process material; temperature; time; fill rate of a storage device; process interruption time; process conditions; characteristics of the process product (such as moisture, brightness, board strength, opacity variation, track interruption frequency of manufactured paper or board); chemical consumption; air content, velocity of the process flow, and / or conductivity.

[0034] In an example embodiment, the rule engine 120 controls at least one or two dosing points. In an example embodiment, the rule engine 120 controls up to 10, 8, 6 or 5 dosing points. In an example embodiment, the rule engine 120 is coupled to the doser 110. In another example embodiment, another controller is located between the rule engine 120 and the doser 110 or multiple dosers 110. The controller can be, for example, a programmable logic controller PLC. In an example embodiment, the rule engine 120 can operate offline in the absence of a network connection. In another example embodiment, the controller is configured to achieve control of the process offline.

[0035] Figure 2 A block diagram of an apparatus 200 is shown according to an example embodiment. The apparatus 200 comprises a communication interface 210; a processor 220; a user interface 230; and a memory 240.

[0036] In an embodiment, the communication interface 210 includes wired and / or wireless communication circuitry, such as Ethernet; wireless LAN; Bluetooth; GSM; CDMA; WCDMA; LTE; and / or 5G circuitry. The communication interface may be integrated into the device 200 or provided as part of an adapter, card, etc. that may be attached to the device 200. The communication interface 210 may support one or more different communication technologies. The device 200 may also or alternatively include more than one communication interface 210.

[0037] In this document, a processor may refer to a central processing unit (CPU); a microprocessor; a digital signal processor (DSP); a graphics processing unit; an application specific integrated circuit (ASIC); a field programmable gate array; a microcontroller; or a combination of such elements.

[0038] The user interface may include circuitry for receiving input from a user of device 200, for example, via a keyboard; a graphical user interface shown on a display of device 200; speech recognition circuitry; or an accessory device; such as headphones; and providing output to the user via, for example, a graphical user interface or a speaker.

[0039] The memory 240 includes a working memory 242 and a persistent memory 244 configured to store computer program code 246 and data 248, such as Figure 1 The rule set 122 of the apparatus 200 may be a memory 240. The memory 240 may include one or more of the following: read-only memory (ROM); programmable read-only memory (PROM); erasable programmable read-only memory (EPROM); random access memory (RAM); flash memory; data disk; optical storage device; magnetic storage device; smart card; solid-state drive (SSD); etc. The device 200 may include multiple memories 240. The memory 240 may be configured as a part of the device 200 or as an accessory inserted into a slot, port, etc. of the device 200 by a user or another person or robot. The memory 240 may be used for the sole purpose of storing data or may also be used for other purposes (such as processing data).

[0040] Those skilled in the art recognize that, in addition to Figure 2 In addition to the elements shown in , the device 200 may also include other elements, such as a microphone; a display; and additional circuit systems, such as input / output (I / O) circuit systems; memory chips; application-specific integrated circuits (ASICs); processing circuits for specific purposes, such as source encoding / decoding circuit systems; channel encoding / decoding circuit systems; encryption / decryption circuit systems; and the like.

[0041] In an example embodiment, the apparatus 200 is a computer cloud-implemented apparatus. In an example embodiment, the apparatus 200 is a virtual device implemented by cloud computing, one or more computer servers, and / or one or more server clusters.

[0042] In an example embodiment, apparatus 200 includes or is an edge device.

[0043] Figure 3a and Figure 3b A flow chart according to an example embodiment is shown which illustrates a method for controlling a process for making pulp, paper or paperboard, the method comprising various possible steps, including some optional steps, while further steps may also be included and / or some of the steps may be performed more than once:

[0044] 301. The chemical preparation dosage in the process is added to the fiber suspension by the rule engine automatically controlled;

[0045] 302. A rule engine receives a plurality of measurement results related to a process;

[0046] 303. A plurality of rule sets are maintained by a rule engine, including an active rule set, each rule set defining how dosing depends on a measurement result;

[0047] 304. The dosing of chemical agents is controlled by a rule engine based on measurement results and action rules;

[0048] 305. Recording at least some of the measurements by the rule engine

[0049] and the actual dosage of chemical preparations;

[0050] 306. Receive at least one rule set candidate;

[0051] 307. Based on the earlier measurement results, simulate the dosing of the chemical agent using at least one rule set candidate received;

[0052] 308. Outputting simulation results to optimize the performance of chemicals in a process, for example, graphically or to a storage medium;

[0053] 309. Use of biocides, defoamers, retention chemicals, retention polymers, strength chemicals, fixatives, dyes, hydrophobic agents, dispersants, bleaching agents and / or pH control agents as chemical agents;

[0054] 310. Selection of new rule sets based on simulation;

[0055] 311. Adjusting the control of dosing by activating a new rule set selected by the rule engine;

[0056] 312. Transfer a ruleset for controlling metering and early measurement results to the twin model, where the twin model and the rule engine can be implemented by the same computing entity, and where the twin model can refer to a replicated twin of the rule engine;

[0057] 313. Derive one or more parameters from the ruleset, e.g., from one or more measurement results and optionally from time;

[0058] 314. Control the disposition of errors in the measurement results by one or more rules in the active ruleset;

[0059] 315. Define one or more chemical agent metering start triggers by the ruleset, such as conditions for one or more parameters and / or time;

[0060] 316. Define one or more chemical agent metering end triggers by the ruleset, such as conditions for one or more parameters and / or time;

[0061] 317. Define one or more chemical agent dosing adjustment criteria by the ruleset;

[0062] 318. Perform a simulation in a computer cloud entity;

[0063] 319. Visualize the metering with at least one ruleset candidate in the simulation,

[0064] 320. For metering, visualize one or more of the measurement results on which the metering depends;

[0065] 321. Output a graphical representation of the control sequence of the rule engine adjusted when applied to the recorded previous measurement results.

[0066] A significantly simplified example is described next. The process under discussion is controlling the metering of a biocide into a fiber suspension storage tower to produce partially recycled paper. The inflow of the fiber suspension is metered directly, and the suspension includes partially recycled pulp and partially virgin fibers. The rule engine initially controls the metering based on two measurement results (inflow rate x1 and pH x1) of the suspension after the storage tower. The ruleset has the following rules:

[0067] R1: d = i · x1, for setting the metering per unit of inflowing fiber suspension (such as volume or mass unit), where d defines the metering and i defines the appropriate basic metering level per volume;

[0068] R2: If x2 < k, then set d: d · (1 + j · (k - x2)); where x2 is the pH, and j and k are coefficients.

[0069] This rule set is based on the assumption that a baseline dosing is required that is proportional to the influx of the suspension, but sometimes changes in the composition of the suspension result in an increase in biological activity, which is indicated by a decrease in the pH of the suspension. Therefore, the second rule adds dosing when the pH drops below k (such as 6.5), as scaled by the term j.

[0070] When parameters and actual dosing are recorded, the historical data can be used to graphically illustrate how the dosing of chemicals has been controlled during the process according to the rule set. However, a need arises to improve the rule set. For example, it can be noted that with higher surface levels (larger inventory in the storage tower), increased residence time results in stronger microbial fermentation. Therefore, if the surface level exceeds a given threshold, a stronger dosing can be added. For example, it can also be noted that when the tank has been emptied, some paper quality degradation has occurred. The first rule set candidate has the same rules R1 and R2 as before, and adds rules.

[0071] R3: If x3>l, then set d:d·m; where x3 is the surface level of the tank, l is the surface level threshold, and m is the dosing boost constant, where m is, for example, 1.15.

[0072] A second rule set candidate is defined, which has rules R1 and R2, and rule

[0073] R4: If x3>l, then set d: d·(1+m·(x3-l)).

[0074] In the simulation, two candidate rule sets are applied to historical observations (a selected time period, such as a week) and see how the dosing behavior changes according to the rule set in question. As output, the corresponding curve graph can be displayed and the chemical consumption and / or other characteristics can be provided to the expert user who develops the dosing control. Then it is easy to see how and where different rule sets react to different changes in process conditions, and how to adjust the dosing. Here, see how the first candidate rule set triggers the gradual increase of dosing when the surface level exceeds the surface level threshold l. If the surface level fluctuates below and above this threshold level in some historical data, the dosing of the first candidate rule set will change in a series of rapid steps. For the second candidate rule set, the simulation leads to a gradual change in dosing, so it is unlikely to cause significant interference. Then select the desired rule set, which is now the second candidate rule set in this example, and provide this choice to the rule engine. Then, the active rule set is replaced by the rule engine to adopt the new rule set for dosing control to control the manufacturing process. Therefore, overdosing can be avoided for safety reasons. Instead, dosing can be carried out for situations that are really needed, so the total consumption of chemicals can be minimized and the process or product quality can be made more stable than before. As dosing margins are reduced, the accuracy of dosing control becomes increasingly important. In some example embodiments, this outstanding need is addressed by supporting agile verification of new rule set candidates without experimenting with the production process itself.

[0075] Because process scenarios can vary in widely and unexpected ways, it is practically difficult to predict all the demands on a rule set. Sometimes a good rule set fails in unexpected situations. Especially when using derived parameters, it can be difficult to predict how a given modification of a rule set will operate under different scenarios. By using historical data and simulating different rule set candidates with this data, production control can be improved, with significantly shorter response times and more efficiency than before. Dosing errors or inaccuracies and the resulting process disturbances can be avoided or mitigated.

[0076] In an example embodiment, one or more rules in an active rule set control the handling of errors in a measurement result. For example, a rule may define that an ORP measurement result is reliable within a range between -100mV and +400mV. The rule may verify the measurement result within this range. Conversely, if the measurement result is -120mV, then this measurement result verification rule defines the measurement result as unreliable. When the measurement result remains invalid, the rule may replace the measurement value with an error hiding value (such as a predefined safety value). The safety value may be selected from a given 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, if the measurement result has dropped to and exceeded the endpoint of the valid range, the safety value may be dynamically defined as the value closest to the measurement result and within the valid range.

[0077] Other or additional measurements, such as conductivity combined with surface level or the time that has passed since the storage tower was last cleaned, may also be used in other rule set candidates.

[0078] In an example embodiment, the efficiency of the active rule set is monitored. For example, the actual achieved dosing of chemicals can be recorded. The monitored data can be compared to one or more inefficient operation criteria that trigger an inefficient operation action, wherein the inefficient operation rule set can be at least temporarily employed in place of the active rule set when the active rule set does not appear to be functioning properly. Efficiency can refer to how well the active rule set works in process control, for example, as a measure of chemical consumption and / or some quality measurement result.

[0079] The inefficient operation criteria may be at least partially or completely global, applicable to all rule sets. Alternatively, or in addition, the inefficient operation criteria may be defined in one or more rule sets. The inefficient operation criteria defined in a rule set may take precedence over the global inefficient operation rules.

[0080] Inefficient operation criteria can define one or more comparisons of monitored data, such as a given item (or parameter) exceeding, meeting or falling below a limit value. Items can be defined based on one or more different measurements. Items can be defined based on real-time data. Items can be defined based on data accumulated over a comparison period lasting at least 10 minutes, 30 minutes, 1 hour, 4 hours, 12 hours, 24 hours, 48 ​​hours, or 168 hours. Items can be or include average values, means, standard deviations, variances, sums, or any combination thereof. For example, an item can be the average concentration of a chemical in a suspension over the past 6 hours, or the amount of a chemical dosed, or the sum of any (derived) measurements during the comparison period. The comparison period can be a continuous day, week, or other repeating period.

[0081] Inefficient operating criteria may be defined for each of multiple simultaneously used rule sets, for example where there are multiple different process control systems each using its own rule set.

[0082] A fallback rule set may be defined for each of multiple concurrently used rule sets, for example where there are multiple different process control systems each using its own rule set.

[0083] Inefficient operation rules can define inefficient operation actions, such as stopping or reducing chemical dosing within a given correction period. Inefficient operation actions can include sending an alarm to the operator. The correction period can last at least 10 minutes, 30 minutes, 1 hour, 4 hours, 12 hours or 24 hours.

[0084] The inefficient operation criteria may include one or more criteria for process stability.

[0085] Any of the above methods, method steps, or combinations thereof may be controlled or performed using hardware, software, firmware, or any combination thereof. The software and / or hardware may be local, distributed, centralized, virtualized, or any combination thereof. Furthermore, any form of computing, including computational intelligence, may be used to control or perform any of the above methods, method steps, or combinations thereof. Computational intelligence may refer to, for example, artificial intelligence, neural networks, fuzzy logic, machine learning, genetic algorithms, evolutionary computing, or any combination thereof.

[0086] As a technical effect, the dosing of a chemical formulation can be optimized using a measurement history associated with a process with a simulated rule engine, such that the responses of different rule engines can be determined instantaneously to optimize the performance of the chemical formulation. The simulation can include calculating one or more estimated properties of the final product.

[0087] Various embodiments have been presented.It should be appreciated that in this document, the words comprise, include, and contain are each used as open-ended expressions without exclusivity.

[0088] The foregoing description provides a complete and informative description of the best mode presently contemplated by the inventors for carrying out the invention by way of non-limiting examples of specific implementations and embodiments. However, it should be clear to those skilled in the art that the invention is not limited to the details of the foregoing embodiments, but may be implemented in other embodiments or in different combinations of embodiments using equivalent means without departing from the characteristics of the invention.

[0089] In addition, some of the features of the example embodiments disclosed above can be used advantageously without the corresponding use of other features. Therefore, the foregoing description should be regarded as merely illustrating the principles of the present invention, rather than limiting the present invention. Therefore, the scope of the present invention is limited only by the attached patent claims.

Claims

1. A method for controlling a process for making pulp, paper or paperboard, comprising: The chemical preparation dosage in the process is added to the fiber suspension by automatic control (301) by the rule engine; Receiving (302) a plurality of process-related measurement results by a rules engine; Maintaining (303) a plurality of rule sets by a rule engine, including an active rule set, each rule set defining how dosing depends on a measurement result; Controlling (304) dosing of the chemical formulation by a rule engine based on the measurement results and an active rule set; recording (305) by the rules engine at least some of the measurements and the actual dosage of the chemical; receiving (306) at least one rule set candidate; simulating (307) dosing of a chemical agent using the received at least one rule set candidate based on the earlier measurement results; outputting (308) simulation results to optimize performance of chemicals in the process; Selection of a new set of rules is obtained (310) based on the simulation; as well as The control of dosing is adjusted (311) by activating the selected new rule set by the rule engine.

2. The method of claim 1, wherein a rule set for controlling dosing and early measurement results are transmitted (312) to the twin model.

3. The method of claim 1 or 2, wherein the rule set is configured to derive (313) one or more parameters from one or more measurements and optionally from time.

4. The method of any of the preceding claims, further comprising controlling (134) handling of errors in the measurement results by one or more rules in an active rule set.

5. The method of any one or more of the preceding claims, further comprising performing simulations by a rules engine.

6. The method of any one or more of the preceding claims, further comprising defining (315) by a rule set one or more chemical dosing initiation triggers.

7. The method of any one or more of the preceding claims, further comprising defining (316) one or more chemical dosage end triggers by a rule set.

8. The method of any one or more of the preceding claims, comprising performing the simulation (318) in a computer cloud entity.

9. The method of any one or more of the preceding claims, further comprising outputting (321) a graphical representation of the control sequence of the rules engine adapted when applied to recorded previous measurements.

10. The method of any one or more of the preceding claims, wherein the suspension comprises cellulose and hemicellulose and has a dry matter of at least 0.5, 1, 2, 5, 10 or 15 weight percent.

11. The method according to any one or more of the preceding claims, wherein the suspension comprises cellulose and hemicellulose and the suspension has a dry matter of at most 1, 2, 5, 10 or 20 weight percent.

12. The method of any one or more of the preceding claims, wherein the chemical formulation comprises a biocide, a defoamer, a retention aid, a retention polymer, a strength agent, a fixative, a dye, a hydrophobic agent, a dispersant, a bleach and / or a pH control agent.

13. The method of any one or more of the preceding claims, further comprising: Monitor the effectiveness of active rule sets; Determine the triggering of inefficient operating criteria; as well as Inefficient operating actions are taken when triggering of inefficient operating criteria is determined, rather than continuing to control the process according to an active rule set.

14. An apparatus (200) comprising means (210, 220, 240, 246) for performing the method of any of the preceding claims.

15. A computer program comprising a computer executable program code (246) configured to, when executed, cause a computer (200) to perform the method of any one of claims 1 to 13.

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