Estimating risk level in a water-containing process

Abstract

It is disclosed to estimate or predict a runnability or end product quality risk level of a pulp or paper making process. The method comprises measuring a hydrophobicity value of a sample originating from the same aqueous process stream. A hydrophobicity measurement signal is generated from the measured hydrophobicity values as a function of time. A risk level is calculated for the process. At least one mathematical index is calculated based on the hydrophobicity measurement signal and optionally on the amount of particles in the sample, other characteristics of the aqueous stream and / or production data. The mathematical index and optionally the amount of particles, other characteristics and / or production data are used as risk indicator inputs in the calculation. Based on the risk level calculated for the pulp or paper making process, the runnability and / or end product quality risk level of the pulp or paper making process is indicated.

Description

Technical Field

[0001] This invention relates to estimating the risk level of a water-containing process, and more specifically to predicting the risk level of a pulping, board-making, or papermaking process or subprocess. Background Technology

[0002] Examples of fields requiring the measurement of solids contained in liquids include forestry, where pulp samples or filtrates (such as wire water, white water, thickener filtrate, or other similar pulp filtrates or circulating water) need to be monitored to control the entire process. Similarly, in oil and mining processes, and in water treatment industries (such as water reuse, seawater desalination, and cooling water treatment), the liquids used often contain solids that need to be measured and monitored. Online methods provide immediate or near-immediate information about suspensions.

[0003] Many such suspensions contain particles, the number and size distribution of which have a considerable impact on upcoming process stages. For example, agglomeration has indeed proven to be a major threat to paper machine deposition and related operational problems. Liquids and filtrates in the pulp industry also exhibit a strong tendency to flocculate, making the analysis of solids in liquid streams challenging.

[0004] The system can be based on field-flow grading, where grading is performed by guiding the sample into one or more concave decomposition channels and applying a liquid flow with a non-constant time velocity distribution through the channels. In this way, the solid matter of the sample is gradually removed along with the liquid flow from the concave channels to provide sample fractions. This method allows for the measurement of particle size and / or mass distribution in filtrate or pulp samples.

[0005] Operational problems (such as paper defects in papermaking machines) may be related to strong agglomeration of hydrophobic particles on the wet end. Therefore, it may be necessary to monitor the particle count and hydrophobicity of particles or particle groups (such as agglomerates). Summary of the Invention

[0006] The following is a simplified summary of the features disclosed herein to provide a basic understanding of some exemplary aspects of the invention. This summary is not a broad overview of the invention. It is not intended to identify key / essential elements of the invention or to define its scope. Its sole purpose is to present some of the concepts disclosed herein in a simplified form as a prelude to a more detailed description.

[0007] According to one aspect, the subject matter of the independent claim is provided. Embodiments are defined in the dependent claims.

[0008] One or more examples of embodiments are set forth in more detail in the accompanying drawings and the following description. Other features will become clear from the description and drawings, as well as from the claims. Attached Figure Description

[0009] The invention will now be described in more detail with reference to the accompanying drawings and preferred embodiments, wherein...

[0010] Figure 1 An exemplary method is illustrated;

[0011] Figure 2 An exemplary device is illustrated;

[0012] Figures 3 to 5 An exemplary risk model is illustrated;

[0013] Figure 6 Examples of measured values ​​and daily averages of the hydrophobicity of colloids in a headbox are shown;

[0014] Figures 7 to 9 An example of risk level calculation is shown. Detailed Implementation

[0015] The following embodiments are exemplary. While the specification may refer to "an," "a," or "some" embodiments in several places, this does not necessarily mean that each such reference points to the same embodiment(s), or that the feature applies only to a single embodiment. Individual features of different embodiments may also be combined to provide other embodiments. Furthermore, the words "comprising," "including," and "containing" should be understood not to limit the described embodiments to consisting only of those features already mentioned, and such embodiments may also include features / structures not specifically mentioned.

[0016] In an embodiment, a method for estimating and / or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process is disclosed. The method includes providing samples of at least one aqueous stream from the pulping, board-making, or papermaking process and measuring the hydrophobicity values ​​of at least two samples from the same aqueous stream originating from the pulping, board-making, or papermaking process. For the at least one aqueous stream, a hydrophobicity measurement signal is generated, representing the measured hydrophobicity value as a function of time. The hydrophobicity measurement signal is processed to calculate the operational risk level and / or the final product quality risk level of the pulping, board-making, or papermaking process, wherein the processing includes calculating at least one mathematical index based at least on the hydrophobicity measurement signal generated for the at least one aqueous stream and optionally based on the amount of particles in the at least one aqueous stream as a function of time, other characteristics of the at least one aqueous stream as a function of time, and / or production data as a function of time. The calculated mathematical index and, optionally, the amount of particles in the at least one water-bearing flow as a function of time, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time are used as inputs to calculate the operational risk level and / or the final product quality risk level. Based on the operational risk level and / or the final product quality risk level calculated for the pulping, board-making, or papermaking process, the operational risk level and / or the final product quality risk level are indicated.

[0017] In another embodiment, a method for estimating or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process is disclosed. The method includes providing samples from at least one aqueous stream of the pulping, board-making, or papermaking process; classifying the samples into fractions based on particle mass and / or size; and measuring the hydrophobicity values ​​of the fractions from at least two samples originating from the same aqueous stream. The hydrophobicity measurement signal for the fractions of the at least one aqueous stream is generated from the measured hydrophobicity values ​​as a function of time. The hydrophobicity measurement signal is processed to calculate the operational risk level and / or the final product quality risk level of the pulping, board-making, or papermaking process, wherein the processing includes calculating at least one mathematical index based at least on the hydrophobicity measurement signal generated for the fractions of the at least one aqueous stream and optionally based on the amount of particles in the at least one aqueous stream as a function of time, other characteristics of the at least one aqueous stream as a function of time, and / or production data as a function of time. The calculated mathematical index and optionally, the amount of particles in the at least one water-bearing flow as a function of time, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time are used as inputs to calculate the operational risk level and / or the final product quality risk level. Based on the operational risk level and / or the final product quality risk level calculated for the pulping, board-making, or papermaking process, the operational risk level and / or the final product quality risk level are indicated.

[0018] In this embodiment, a hydrophobic dye may be added to a sample or fraction to stain the particles within it. Fluorescence intensity values ​​can be measured using optical measurements of the sample or fraction to generate a hydrophobicity measurement signal. The resulting measurement signal represents the hydrophobicity of the particles in the aqueous flow.

[0019] The calculated mathematical index may include the cumulative moving sum of hydrophobicity, the cumulative time of hydrophobicity, the moving median of hydrophobicity, the moving maximum of hydrophobicity, and / or the moving average of hydrophobicity.

[0020] The calculated mathematical index can include the sum of the hydrophobicity values ​​of the samples measured during the selected time period, the average of the sample hydrophobicity values ​​measured during the selected time period, the median of the sample hydrophobicity values ​​measured during the selected time period, and / or the maximum of the sample hydrophobicity values ​​measured during the selected time period. The time period can be several hours, several days, or a week.

[0021] The calculated mathematical index can include the difference between the product of continuously measured hydrophobicity values ​​and gain values. Gain values ​​have values ​​greater than 0. Gain values ​​can be specific constant values ​​for a particular process flow. Examples of suitable gain values ​​include 0.5, 1, 2, 10, etc.

[0022] The calculated mathematical index can include the difference between consecutive averages of hydrophobicity values ​​measured over a selected time period. For example, changes between consecutive hydrophobicity values, or changes at the hourly, daily, and / or weekly levels (i.e., differences between consecutive hourly averages, consecutive daily averages, and consecutive weekly averages) can be used to monitor the direction and magnitude of the change. Samples can be classified into one or more of the following fractions: fibrous fraction, agglomerate fraction, flocculent fraction, fine-particle fraction, and colloidal fraction.

[0023] The calculated mathematical exponent can be the hydrophobic measurement signal multiplied by a gain value. The gain value has a value greater than 0. The gain value can be a specific constant value for a particular process flow. Examples of suitable gain values ​​include 0.5, 1, 2, 10, etc.

[0024] In the embodiments, the other characteristics of the water-containing flow are one or more of pH, conductivity, charge, temperature, COD (chemical oxygen demand) and particle size, consistency, and ash consistency.

[0025] In one embodiment, a risk level can be calculated by inputting risk indicator inputs to a risk indicator, the risk indicator being selected from one or more of mathematical models, equations, statistical models, regression-based models, and / or rule-based models. In another embodiment, a risk level can be calculated by inputting risk indicator inputs to a risk indicator, wherein the risk indicator includes a threshold for at least one risk indicator input and / or a weighting coefficient for at least one risk indicator input.

[0026] In an embodiment, the method includes predicting operational risks and / or final product quality risks caused by changes in process conditions of the pulping, board-making, or papermaking processes.

[0027] In this embodiment, based on the operational risk level and / or the final product quality risk level, the concentration of hydrophobic contaminants in one or more aqueous streams of the pulping, board-making, or papermaking process is controlled by manually and / or automatically adjusting the dosage of at least one chemical entering one or more aqueous streams of the pulping, board-making, or papermaking process, and / or manually and / or automatically adjusting the dosage of wash water entering one or more aqueous streams of the pulping, board-making, or papermaking process. Additionally / alternatively, optimization of freshwater use, activation of water circulation, machine washing, machine washing sequence, and / or improvement of pulp washing may be performed or enabled based on the predicted risk level.

[0028] Risk indicators can be selected or created based on historical operational data about the pulping, board making, or papermaking process or subprocess, and / or risk indicators can be process-specific or subprocess-specific.

[0029] The predicted operational risk level and / or the predicted final product quality risk level can be output to the user.

[0030] As used herein, the term "final product" may refer to paper, board, paperboard, pulp, pulp filtrate, and / or dried pulp.

[0031] The method may include informing the user whether the operational risk level and / or the final product quality risk level exceed predefined values. If the operational risk level and / or the final product quality risk level exceed the predefined values, an alarm may be issued. The device may issue at least two types or levels of alarms for the user to receive. The first alarm level may be a yellow warning (indicating a slight increase in the risk level), and the second alarm level may be a red alarm (indicating a high risk level).

[0032] In the embodiment, the moving cumulative sum of hydrophobicity is the daily cumulative sum of hydrophobicity, the cumulative time of hydrophobicity is the time interval during which hydrophobicity exceeds a predefined hydrophobicity value, the moving median of hydrophobicity is the daily median of hydrophobicity, and / or the moving average of hydrophobicity is the daily average of hydrophobicity.

[0033] In this embodiment, the moving cumulative sum of hydrophobicity is the cumulative sum of sample hydrophobicity values ​​measured over the past 24 hours, the moving median of sample hydrophobicity values ​​measured over the past 24 hours, and / or the moving average of sample hydrophobicity values ​​measured over the past 24 hours. The time period for calculating the moving mathematical index can be several hours (e.g., 8 hours or 12 hours), several days, or a week.

[0034] In one embodiment, an apparatus for estimating or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process or subprocess is disclosed, the apparatus including components for performing the method steps.

[0035] In an embodiment, the apparatus includes components for obtaining samples from at least one aqueous stream in a pulping, board-making, or papermaking process; measuring components for measuring the hydrophobicity values ​​of at least two samples from the same aqueous stream in the pulping, board-making, or papermaking process; and processing components for generating a hydrophobicity measurement signal for the at least one aqueous stream as a function of time. The processing unit is configured to process the hydrophobic measurement signal to calculate the operational risk level and / or final product quality risk level of the pulping, board-making, or papermaking process by calculating at least one mathematical index based on the hydrophobic measurement signal generated for the at least one water-bearing flow and optionally as a function of time the amount of particles in the at least one water-bearing flow, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time. The processing unit is configured to use the calculated mathematical index, and optionally as a function of time the amount of particles in the at least one water-bearing flow, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time, as risk indicator inputs for calculating the operational risk level and / or final product quality risk level, and to indicate the operational risk level and / or final product quality risk level of the pulping, board-making, or papermaking process based on the risk level calculated for the pulping, board-making, or papermaking process.

[0036] In an embodiment, the apparatus includes components for obtaining a sample from at least one aqueous stream in a pulping, board-making, or papermaking process; a classifier for classifying the sample into fractions based on particle mass and / or size; a measuring component for measuring the hydrophobicity values ​​of fractions from at least two samples originating from the same aqueous stream; and a processing component for generating a hydrophobicity measurement signal for the fractions of the at least one aqueous stream as a function of time. The processing unit is configured to process the hydrophobic measurement signal to calculate an operational risk level and / or a final product quality risk level for the pulping, board-making, or papermaking process by calculating at least one mathematical index based on a hydrophobic measurement signal generated for the at least one water-bearing flow fraction and optionally based on the amount of particles in the at least one water-bearing flow as a function of time, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time. The processing unit is configured to use the calculated mathematical index and optionally the amount of particles in the at least one water-bearing flow as a function of time, other characteristics of the at least one water-bearing flow as a function of time, and / or production data as a function of time as risk indicator inputs for calculating the operational risk level and / or the final product quality risk level, and to indicate the operational risk level and / or the final product quality risk level of the pulping, board-making, or papermaking process based on the risk level calculated for the pulping, board-making, or papermaking process.

[0037] In an embodiment, the processing unit is configured to calculate a risk level by inputting a risk indicator input to a risk indicator selected from one or more of mathematical models, equations, statistical models, regression-based models, and / or rule-based models.

[0038] In one embodiment, the processing unit is configured to calculate a risk level by inputting a risk indicator input to a risk indicator, wherein the risk indicator includes a threshold for at least one risk indicator input and / or a weighting coefficient for at least one risk indicator input.

[0039] In an embodiment, the apparatus is configured to predict operational risks and / or final product quality risks caused by changes in process conditions during pulping, board making, or papermaking processes.

[0040] In embodiments, the device is configured to control the concentration (e.g., mg / L) or amount (e.g., relative quantity or amount of contaminants, e.g., between 0 and 100%) of hydrophobic contaminants in one or more aqueous streams by manually and / or automatically adjusting the dosage of at least one chemical entering one or more aqueous streams and / or manually and / or automatically adjusting the dosage in the wash water entering one or more aqueous streams, based on operational risk levels and / or final product quality risk levels. Additionally / alternatively, freshwater use, activation of water circulation, machine washing, optimization of machine washing sequence, and / or improvement of pulp washing can be performed or enabled by the device based on predicted risk levels.

[0041] In embodiments, the apparatus is configured to select or create risk indicators based on historical operational data regarding the pulping, board making, and / or papermaking processes or subprocesses, and / or the risk indicators are process-specific or subprocess-specific.

[0042] In this embodiment, the device is configured to output the operational risk level and / or the final product quality risk level to the user.

[0043] In an embodiment, the device is configured to indicate to the user whether the operational risk level and / or the final product quality risk level exceed a predefined value.

[0044] In one embodiment, the device is configured to issue an alarm if the operational risk level and / or the final product quality risk level exceed a predefined value.

[0045] In embodiments, the methods and / or apparatus are used to monitor and control the chemical properties during pulping, board making, or papermaking processes. In embodiments, the methods and / or apparatus are used for chemical optimization or chemical stabilization during pulping, board making, or papermaking processes. In embodiments, the methods and / or apparatus are used to optimize and / or stabilize the wet end of the papermaking process. In embodiments, the methods and / or apparatus are used for the reuse of water streams during pulping, board making, or papermaking processes.

[0046] In embodiments, methods and / or apparatus are used to measure the hydrophobicity values ​​of samples from at least two different water-bearing streams originating from a pulping, board-making, or papermaking process; to generate a hydrophobicity measurement signal as a function of time for the difference between the hydrophobicity values ​​measured for the at least two water-bearing streams; and to process the hydrophobicity measurement signal to calculate an operational risk level and / or a final product quality risk level for the pulping, board-making, or papermaking process. This processing may include calculating at least one mathematical index based at least on the hydrophobicity measurement signals generated for the at least two water-bearing streams and optionally on the amount of particles in the at least two water-bearing streams as a function of time, other characteristics of the at least two water-bearing streams as a function of time, and / or production data as a function of time. The calculated mathematical index and, optionally, the amount of particles in the at least two water-bearing streams as a function of time, other characteristics of the at least two water-bearing streams as a function of time, and / or production data as a function of time can be used as risk indicator inputs to calculate the operational risk level and / or the final product quality risk level; and based on the operational risk level and / or the final product quality risk level calculated for the pulping, board-making, or papermaking process, the operational risk level and / or the final product quality risk level are indicated. In this case, hydrophobicity values ​​can be measured, for example, from headbox and wire water samples, and the difference between the hydrophobicity values ​​can be calculated to determine how much interfering material is retained in the final product (i.e., paper, paperboard, or dried pulp). A hydrophobicity measurement signal is generated as a function of time for the difference between the hydrophobicity values ​​measured for at least two water-bearing streams. The risk level can be calculated directly for the difference between the hydrophobicity values, or calculated for the hydrophobicity measurement signal using a mathematical index. Therefore, in this case, the hydrophobicity is measured at two different sampling points. For example, measurements can be performed before and after the addition of chemicals, before and after containers, tanks, towers and / or silos, before and after pH adjustment, and / or before and after filtration, and / or before and after any other type of solids separation operation. The difference between the obtained hydrophobic signals is calculated, and a risk level for this difference is obtained using a mathematical index.

[0047] Chemicals that can be monitored and / or controlled by means of this method and apparatus include (one or more) retention aids, (one or more) sediment control chemicals, (one or more) sizing agents, (one or more) fixing agents, (one or more) dispersants, (one or more) anti-sticking agents, (one or more) passivating agents, (one or more) polymers, (one or more) enzymes, (one or more) inorganic coagulants and / or (one or more) organic coagulants.

[0048] Examples of production data include production speed, machine speed, product grade, product basis weight, type and share of incoming pulp (including breakage, total retention, ash retention, ash consistency in headbox and / or wire water), total consistency in headbox and / or wire water, chemical dosage in paper machine, chemical dosage in paperboard machine, chemical dosage in wet end and / or other parts of the pulp / paper / paperboard manufacturing process, and paper / paperboard machine temperature.

[0049] Examples of water-containing flows to be analyzed and / or controlled include process flows or subprocess flows such as pulp filtrate, incoming pulp, wire water, headbox flow, wet end flow, waste line flow, and / or white water. Water-containing flows can originate from different subprocesses in the pulp / paper / paperboard manufacturing process.

[0050] If the pulping process is integrated with the papermaking process, then the term "papermaking process" can refer to both the pulping and papermaking processes. If the pulping process is integrated with the board-making process, then the term "board-making process" can refer to both the pulping and board-making processes.

[0051] In the embodiments, the operational risk level and / or final product quality risk level of the pulping, board-making, or papermaking sub-process are estimated and / or predicted, and the operational and / or final product quality risk levels estimated and / or predicted for the pulping, board-making, or papermaking sub-process are used to estimate and / or predict the operational risk level and / or final product quality risk level of the (overall) pulping, board-making, or papermaking process.

[0052] This invention relates to an online system for continuously monitoring hydrophobic particles in aqueous flows and pulp suspensions. The invention also relates to components for interpreting results and extracting risk indicator inputs based on particle counts and hydrophobicity of samples. Sample pretreatment and separation are also disclosed to achieve the desired outcome.

[0053] This invention relates to a system and method for analyzing liquid samples containing particles of solid matter, wherein the analysis is performed online by collecting the sample from a stream of liquid. Dyes can be added to the sample to stain the particles contained therein. The sample can be graded, pretreated, or left untreated. Thus, the particles in the sample can be separated into different particle groups, for example, based on particle mass or size (or both) by grading or by sedimentation or centrifugation.

[0054] The sample can be guided into a first flow chamber equipped with a dissociation unit, where a liquid flow of water is introduced at a specific velocity profile that classifies the sample particles into one or more particle groups. Initially, a low velocity is used, allowing smaller or lighter particle groups to pass through the dissociation unit first. Then, the liquid flow rate is gradually (e.g., stepwise) increased according to the velocity profile, with all particle groups passing through the dissociation unit according to the characteristics of each group and their retention time features. The particle groups flow into a second flow chamber with a substantially laminar flow, where at least one physical or chemical property of the stained particles in the particle group is measured using optical instruments and / or detectors to generate at least one measurement signal. The measurement signals are processed for each measured particle group to extract key variables describing the measured properties, and the measurements of each group are correlated with other process parameters and / or key variables of the entire sample. The chemical or physical properties of the sample to be tested can be one or more of the following: particle concentration, particle volume, particle surface area, particle size, turbidity, suspended solids concentration, absorbance, fluorescence, light scattering, and hydrophobicity.

[0055] This invention allows for the measurement of particle count and hydrophobicity for each group using optical sensors / measurements such as light scattering, particle counters, turbidity, absorbance, fluorescence, and suspended solids. This provides a robust and simple design for an online system, as it eliminates the need to analyze each particle individually.

[0056] The present invention also relates to the use of the method in a system for analyzing liquid samples containing particles of solid matter.

[0057] Figure 1 An exemplary method is illustrated. (Reference) Figure 1 The sample 101 can be provided directly from the process and / or subprocess to be monitored or controlled. The sample can be a batch sample of approximately 10 ml or a "plug" obtained using an automated sampling unit. Next, in step 102, the sample can be stained using a hydrophobic dye such as Nile Red. In this optional pretreatment stage, particles are prepared for measurement. Staining of the sample or particles of the sample is performed before or during the decomposition channel (i.e., during the optional grading step 103). The amount stained per milliliter of sample can be approximately 40 μl.

[0058] In optional step 103, the sample is fed into the decomposition channel for classification. The sample is driven into the channel relatively quickly, causing it to experience rapid local acceleration, which disrupts potential flocs in the sample. However, the sample feed velocity should not allow it to pass through the decomposition channel immediately. The liquid flow, typically an aqueous flow, is guided through the decomposition channel into a field flow classification channel (FFF) with basic laminar flow characteristics. The overall dilution of the sample in water can be approximately 1:10–1:200, preferably approximately 1:50–1:70. To separate the smallest particles from larger or heavier particles, the flow rate is initially low. In this way, particle separation is achieved in the channel, with lighter particles passing through the system first. To allow heavier particles to enter the water flow, the flow rate is gradually increased. Thus, the velocity is increased to a level that even captures the heaviest (or at least all of interest) particles. Therefore, the sample is effectively classified in the FFF channel. Flow rate profiles can be optimized for different types of liquids, for example, one for white water samples from paper mills and another for pulp samples.

[0059] In step 104, the desired characteristics of the fraction or sample are measured. Optical measurements can be performed in step 104, but alternative or additional measurement stages may also be present.

[0060] The decomposition and grading steps 103, and typically the measurement step 104, can occur at least partially simultaneously in a continuous configuration. However, if immediate online results are not required, it is also possible to recover samples or fractions for subsequent individual measurements.

[0061] Step 104 involves generating a hydrophobicity measurement signal that is a measured hydrophobicity value as a function of time.

[0062] In step 105, the hydrophobicity measurement signal is processed to calculate the risk level of the pulping, board-making, or papermaking process and / or subprocesses. This processing includes calculating at least one mathematical index based on the hydrophobicity measurement signal and optionally the amount of particles in the sample, other characteristics of the sample, and / or production data. In step 106, the calculated mathematical index and optionally the amount of particles in the sample and / or other characteristics of the sample are used as risk indicator inputs in the risk level calculation. Based on the risk level calculated for one or more pulping, board-making, or papermaking subprocesses, a predicted operational risk level and / or a predicted final product quality risk level for the pulping, board-making, or papermaking process is estimated and indicated. The indication may include outputting the predicted operational risk level and / or the predicted final product quality risk level to the user in step 107. In step 108, based on the predicted operational risk level and / or the predicted final product quality risk level, the concentration or amount of hydrophobic contaminants in the aqueous stream can be controlled by manually and / or automatically adjusting the dosage of at least one chemical entering the aqueous stream and / or manually and / or automatically adjusting the dosage of pulp washing water entering the aqueous stream.

[0063] Figure 1 The entire process described herein may take approximately 30 minutes, including grading and sample measurement, as well as cleaning of the sampling system. The time period can vary depending on the nature of the system and the sample, for example, 2–180 minutes, or typically 5–50 minutes.

[0064] Figure 2 An exemplary device is illustrated. (Reference) Figure 2 The device may include a classifier and a measuring unit 25 with one or more detectors. The classifier may include a freshwater source 20 and a sampling device 21. A pump 22 may be provided to drive the sample or water forward in the system using a suitable valve (not shown). The pump 22 may be positively connected to a first flow chamber, here the decomposition channel 23, and also connected to a second flow chamber, here the field flow classification (FFF) channel 24. A staining unit 26 with a dye reservoir (not shown) may be provided to feed an appropriate amount of dye to the sample before (or after) the particles are classified into groups. The device also includes a processing unit with, for example, a programmable logic controller (PLC) or an industrial computer, for automated operation and data collection of the device. The processing unit may also include a computer with suitable software to perform processing of the measurement signals to extract an estimated / predicted risk level, which is the main deliverable of the device. The computer may be included in the measuring unit 25 or inserted therein as a separate computer, optionally for remote monitoring. An automated cleaning system for various liquid-carrying parts of the device may also be provided.

[0065] A classifier can also be a type that separates particles into groups based on particle mass or size (or both), sedimentation, centrifugation, or filtration. Furthermore, samples can be classified with or without pretreatment.

[0066] This method is designed to monitor particles such as colloids, adhesives, wood tar, white tar, flocculents, fine particles, fibers, and / or agglomerated particles.

[0067] The quantities measured by the online system can be fluorescence intensity and turbidity. When a hydrophobic dye (such as Nile Red) is added to the sample, the fluorescence intensity is directly related to the hydrophobicity of the sample or fraction. Turbidity can be used to measure particle concentration. It should be noted that particle size and / or particle volume also affect turbidity. Instead of turbidity, light scattering can be measured and used to determine particle concentration.

[0068] Figure 3 , 4Figures 5 and 6 illustrate an example of a risk model where mathematical operations related to mathematical indices and rule-based risk indicators are used to predict or estimate the risk level in a papermaking process. Risk level 0 = low risk level. The higher the risk level value, the greater the risk of operational problems. HF = hydrophobicity.

[0069] exist Figure 3 In this context, it involves predicting / estimating the risk level of defects in the final product. Figure 3 In this context, mathematical calculations include the weekly cumulative total and the cumulative time when the measured value exceeds a threshold. Additionally, in... Figure 3 In this context, the rules used for rule-based risk indicators include:

[0070] Risk Indicator 1 (RI1)

[0071] - If the cumulative total is less than 3000, then the risk level is 0 (low).

[0072] - If the cumulative total is 3000-3500, then the risk level is 1 (medium).

[0073] - If the cumulative total is 3500-4000, then the risk level is 2 (high).

[0074] - If the cumulative total is >4000, then the risk level is 3 (very high);

[0075] Risk indicator 2 (RI2, threshold 1 = 19.9)

[0076] - If the cumulative time is less than 1 hour, then the risk level is 0 (low).

[0077] - If the cumulative time is 1-95 hours, then the risk level is 1 (medium).

[0078] - If the cumulative time is >95 hours, then the risk level is 2 (high);

[0079] Risk Indicator 3 (RI3, threshold 2 = 25)

[0080] - If the cumulative time is less than 1 hour, then the risk level is 0 (low).

[0081] - If the cumulative time is 1-99 hours, then the risk level is 1 (medium).

[0082] - If the cumulative time is >99 hours, then the risk level is 2 (high).

[0083] Further in Figure 3 In this context, the total risk level (i.e., the predicted / estimated risk level of defects in the final product) = RI1 + RI2 + RI3. Figure 3In the above, threshold 1 = 19.9 (high hydrophobicity), while threshold 2 = 25 (extremely high hydrophobicity).

[0084] exist Figure 4 In this context, the risk level of chemical-related operational problems is predicted / estimated. Figure 4 In this context, mathematical operations include the daily moving average, the cumulative time when the daily moving average exceeds a threshold, and the daily cumulative sum. Additionally, in... Figure 4 In this context, the rules used for rule-based risk indicators include:

[0085] Risk Indicator 1 (RI1)

[0086] - If the cumulative time of HF (daily moving average > 4) is 0, then the risk of operational problems is low (risk level = 0).

[0087] - If the cumulative HF time is greater than 0 but less than 50 hours, then the risk of operational problems increases (risk level = 1, moderate).

[0088] - If the cumulative HF time exceeds 50 hours, then the risk of operational problems is very high (risk level = 2, high);

[0089] Risk Indicator 2 (RI2)

[0090] - If the cumulative sum of HF is less than 90, then the risk of operational problems is low (risk level = 0).

[0091] - If the cumulative total of HF is greater than 90 but less than 125, then the risk of operational problems increases (risk level = 1, moderate).

[0092] - If the cumulative total of HF exceeds 125, then the risk of operational problems is very high (risk level = 2, high).

[0093] Further in Figure 4 In this context, the total risk level (i.e., the predicted / estimated risk level of chemical-related operational problems) = RI1 + RI2. Figure 4 In the middle, the threshold is 4.

[0094] exist Figure 5 In this process, the risk level of fouling breakage is estimated. Figure 5 In this context, mathematical calculations include the cumulative daily movement summation (= hydrophobic load). Additionally, in Figure 5 In this context, the rules used for rule-based risk indicators include:

[0095] Rules for hydrophobic loads

[0096] - If the hydrophobic load is >450, the risk of fouling breakdown increases (risk level).

[0097] =2),

[0098] - If the hydrophobic load is <450 and >350, then the risk of fouling breakdown is moderate (risk level = 1).

[0099] - If the hydrophobic load is <350, then the risk of fouling breakdown is low (risk level = 0);

[0100] Rules for ash retention

[0101] - If ash retention is <55%, the risk of dirt breakage increases (risk level).

[0102] =2),

[0103] - If the ash retention is 60-65%, then the risk of dirt breakage is moderate (=1).

[0104] - If ash retention is >65%, then the risk of dirt breakage is low (=0).

[0105] Further in Figure 5 In this case, the total risk level (i.e., the estimated risk level of fouling breakage) = 0.3*R1 + 0.7*R2.

[0106] In pulping / paper making process monitoring systems, the following key variables can be extracted from the measured data signals:

[0107] - Count of one or more particles: total count and count of each particle group from the turbidity signal;

[0108] - The size of one or more particles from the retention time of each particle group in the system (i.e., the time when the particles leave the classifier);

[0109] - Particle size distribution derived from turbidity and retention time(s);

[0110] - Particle hydrophobicity: hydrophobicity of each particle group from the fluorescence signal and total hydrophobicity;

[0111] - Hydrophobic distribution of particles derived from fluorescence signal and retention time(s).

[0112] Specific software toolkits can be used for the preprocessing of raw measurement data and the calculation of key variables of particle characteristics. Preprocessing may include signal filtering, averaging, derivation and / or baseline correction, or the use of any other basic mathematical operations and / or applicable functions to modify the raw measurement data. As an example of this process, a baseline can be removed from the raw measurement data of graded samples, and a cumulative sum can be calculated from the data. The cumulative sum of turbidity is related to the particle count, and the cumulative sum of fluorescence is related to the hydrophobicity of the particles. The hydrophobicity and count of each particle group are derived from data within a certain time interval. Each particle group has its own time interval in the second flow chamber. The total hydrophobicity and total count are derived from all data of the graded samples. The turbidity, particle size, and number in a sample group can be determined by measuring absolute or relative values. If relative measurements are used, then the processing unit used to process the raw measurement data for each particle group is calibrated for known samples.

[0113] In other words, the key variables in a particle swarm are generated by calculating the cumulative sum, derivative, integral, mean, maximum, and minimum values ​​of raw or preprocessed measurement data, or by using statistical operators to generate parameters such as skewness, bias, mode, median, quartiles, range, variance, kurtosis, percentiles of the data, or by modifying the key variables through any other basic mathematical operations and / or using applicable functions to attach physical / chemical properties to each swarm. The chemical or physical properties of the sample to be tested can be one or more of the following: particle concentration, particle volume, particle surface area, particle size, turbidity, concentration of suspended solids, absorbance, fluorescence, light scattering, and hydrophobicity. Raw or preprocessed measurement data can be mapped onto a coordinate system to extract other characteristics of the sample.

[0114] Optionally, specific software toolkits include components for calibration. The particle count and / or particle(s) size(s) can be calibrated to SI units using suitable mathematical equations (e.g., first-order and / or second-order equations).

[0115] Optionally, one or more key variables for each group or the entire sample may be used to monitor, control, and / or optimize the process (e.g., in a paper machine). For example, key variables may be used to monitor the operating parameters and characteristics of the paper machine, including monitoring particle aggregation tendencies and chemical behavior during the process.

[0116] Optionally, one or more key variables from each group or the entire sample are used to monitor the performance of the chemicals by controlling the chemicals (e.g., controlling the dosage of the chemicals) and optimizing the dosage or procedures of the chemicals (type of chemicals, dosage of chemicals, point of addition of chemicals in the process).

[0117] The embodiments relate to the sampling of liquids containing solid matter (such as aqueous suspensions or filtrates) in the forestry, oil and mining industries, as well as in water treatment, desalination, or water reuse processes, and in subsequent measurements of samples. More specifically, it relates to online analytical methods and systems utilizing grading techniques for sample streams. The inventive technology is versatile and can be widely applied in the pulp and paper industry, for example, in wet end monitoring, breakage treatment, viscosity control of recycled pulp, and chemical / mechanical pulp processing, including bleaching and drying sections of paper, paperboard, or tissue paper machines. It can be used for online monitoring of particulate groups such as colloids, white pitch, wood pitch, adhesives, fine powders, fillers, or agglomerates, and their hydrophobicity. The inventive online system enables real-time problem solving and optimization of chemical reactions in pulp, paper, or paperboard plants.

[0118] The implementation examples enable the prediction of operational risk levels, for example, for chemical-related operational problems, machine fouling, process breakage (such as chemical-related breakage, fouling breakage, deposit breakage, pore breakage, and / or increased washing breakage).

[0119] The embodiments enable the prediction of the final product quality risk level, for example, for chemical-related problems in the final product, defects in the final product, and / or different types of final product defects (such as holes and / or spots). Final product defects can be classified into different groups based on size and color.

[0120] Therefore, a predictive system / method is provided that indicates the risk level at which future operational / product quality events will occur. The predictive system / method is implemented by calculating a mathematical index (and optionally, as a function of time, the amount of particles in the at least one aquifer, other characteristics of the at least one aquifer, and / or production data, as a function of time) used as inputs to the risk indicator. The predictive system / method enables the capture of process trends to determine the level of operational / product quality risk. Trends in operational / product quality problems can thus be detected before they begin to occur, and chemical dosages can therefore be scheduled based on the predicted risk level. The predictive system / method enables more accurate predictions of the state of the process, suggesting actions to achieve optimal results, such as preventing operational / product quality problems, for example, through optimal chemical dosages. Chemical requirements can be defined more accurately. Operational / product quality problems can be eliminated or mitigated, resulting in more compliant products and less waste. This also makes it possible to estimate / predict how long a pulping, board-making, or papermaking process can operate before the operational / product quality risk level becomes too high (exceeding a predefined value) (e.g., operational / product quality issues begin to have too much impact on process operational / product quality).

[0121] Example 1

[0122] From June 10 to June 23, the papermaking process experienced deposition problems due to bitumen. The hydrophobicity of particles in the wet end was measured from sample streams taken from the headbox and wire.

[0123] Figure 6 The hydrophobicity of colloidal particles (wood pitch) in a headbox sample as a function of time is shown, along with the daily moving average of the measured hydrophobicity values ​​of the colloid.

[0124] Figure 7 The table shows the cumulative time (in hours) of the daily average hydrophobicity when hydrophobicity > 4, and the paper machine running problems. 0 = no running problems. 1 = running problems.

[0125] Figure 8 This shows the cumulative daily hydrophobicity of the colloids and the paper machine's operational issues. 0 = No operational issues. 1 = Operational issues.

[0126] Create two risk indicators.

[0127] Risk indicator 1 is cumulative time. If the cumulative time for hydrophobicity is 0, then the risk level is 0 (low). If the cumulative time is greater than 0 but less than 50 hours, then the risk level is 1 (medium). If the cumulative time is greater than 50 hours, then the risk level is 2 (high).

[0128] Risk indicator 2 is cumulative hydrophobicity. If the daily cumulative total of hydrophobicity is <90, then the risk level = 0 (low). If the daily cumulative total of hydrophobicity is higher than 90 but lower than 125, then the risk of operational problems increases (risk level = 1, moderate). If the daily cumulative total of hydrophobicity exceeds 125, then the risk of operational problems is very high (risk level = 2, high).

[0129] The final risk level of the paper machine's operational problems is calculated based on the two risk indicators, such that the predicted risk level = the output of risk indicator 1 + the output of risk indicator 2. The predicted risk level is expressed as an integer value from 0 to 4, where 0 = low risk and 4 = high risk.

[0130] Figure 9 This shows the predicted (estimated) risk level as a function of time, and the paper machine's operational issues. 0 = no operational issues. 1 = operational issues. (From...) Figure 9 As can be seen, the predicted (estimated) risk level began to warn of an increased risk of operational problems on June 8 (i.e., two days before the major sedimentation problem in the paper machine (starting from June 10)).

[0131] This invention has versatility and can be widely applied in the paper industry, including viscosity control of recycled pulp and mechanical pulp processing. It can be used for online monitoring of particle groups such as colloids, fine particles, fillers, or agglomerates, and their hydrophobicity. This online system enables real-time problem solving and optimization of chemical reactions in paper mills.

[0132] It should be understood that the embodiments of the invention disclosed herein are not limited to the specific structures, process steps, or materials disclosed herein, but extend to equivalents as will be recognized by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0133] The reference to "an embodiment" or "an embodiment" in this specification means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment.

[0134] As used herein, for convenience, multiple items, structural elements, constituent elements, and / or materials may be presented in a common list. However, these lists should be interpreted as if each member of the list were individually identified as a separate and distinct member. Therefore, without indication to the contrary, no individual member of such a list should be construed as being factually equivalent to any other member of the same list solely based on their presence in the common group. Furthermore, various embodiments and examples of the invention, along with alternatives to its various components, may be mentioned herein. It should be understood that such embodiments, examples, and alternatives should not be construed as factual equivalents of each other, but should be regarded as separate and autonomous representations of the invention.

[0135] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details, such as examples of length, width, shape, etc., are provided in the following description to provide a thorough understanding of embodiments of the invention. However, those skilled in the art will recognize that the invention can be practiced without one or more of these specific details, or using other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention.

[0136] While the foregoing embodiments illustrate the principles of the invention in one or more specific applications, it will be apparent to those skilled in the art that numerous modifications in form, use, and detail of the embodiments can be made without exercising inventive capabilities and without departing from the principles and concepts of the invention. Therefore, the invention is not intended to be limited except by the following claims.

[0137] It will be apparent to those skilled in the art that the inventive concept can be implemented in various ways as technology advances. The invention and its embodiments are not limited to the examples described above, but can be varied within the scope of the claims.

Claims

1. A method for estimating or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process, the method comprising: Provide a sample from at least one aqueous stream from the pulping, board-making, or papermaking process; Measure the hydrophobicity of at least two samples from the same water-containing flow originating from the pulping, board-making, or papermaking process; For the at least one water-bearing flow, a hydrophobic measurement signal is generated as a function of time for the measured hydrophobicity values ​​of the at least two samples; The hydrophobicity measurement signal of the measured hydrophobicity values ​​of the at least two samples is processed to calculate the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product, wherein the processing includes calculating at least one mathematical index based on at least the following: - A hydrophobic measurement signal for the at least two samples of the at least one water-bearing flow as a function of time, and - Optionally, the amount of particles in the at least one aquifer as a function of time, other characteristics of the at least one aquifer as a function of time, and / or production data as a function of time, wherein the other characteristics of the aquifer include one or more of pH, conductivity, charge, temperature, chemical oxygen demand, particle size, consistency, and ash consistency. The calculated mathematical index and, optionally, the amount of particles in the at least one aquifer as a function of time, other characteristics of the at least one aquifer as a function of time, and / or production data as a function of time are used as risk indicator inputs for calculating the operational risk level and / or the final product quality risk level; and Indicates the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product, based on the operational risk level and / or the quality risk level of the final product calculated for the pulping, board-making, or papermaking process.

2. The method of claim 1, wherein the method comprises: The samples are graded into fractions based on particle quality and / or size.

3. The method as described in claim 1 or 2, wherein, The method includes: Measure the hydrophobicity values ​​of samples from at least two different water-containing streams originating from the pulping, board-making, or papermaking process; For the difference between the hydrophobicity values ​​measured for the at least two water-bearing flows, a hydrophobicity measurement signal as a function of time is generated; The hydrophobicity measurement signal is processed to calculate the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product, wherein the processing includes calculating at least one mathematical index based on at least the following: - The hydrophobic measurement signal generated by the at least two water-bearing flows, and - Optionally, the amount of particles in the at least two aquifers as a function of time, other characteristics of the at least two aquifers as a function of time, and / or production data as a function of time. The calculated mathematical index and, optionally, the amount of particles in the at least two aquifers as a function of time, other characteristics of the at least two aquifers as a function of time, and / or production data as a function of time are used as risk indicator inputs for calculating the operational risk level and / or the final product quality risk level; and Indicates the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product, based on the operational risk level and / or the quality risk level of the final product calculated for the pulping, board-making, or papermaking process.

4. The method of claim 1 or 2, wherein the method comprises: Add hydrophobic dyes to the sample or fraction to stain the particles in the sample or fraction; as well as The fluorescence intensity of a sample or fraction is measured by optical measurement to generate a hydrophobicity measurement signal, which represents the hydrophobicity of particles in a water-bearing flow.

5. The method as described in claim 1 or 2, wherein, The calculated mathematical index includes the moving cumulative sum of hydrophobicity values, the cumulative time of hydrophobicity values, the moving median of hydrophobicity values, the moving maximum of hydrophobicity values, and / or the moving average of hydrophobicity values.

6. The method as described in claim 1 or 2, wherein, The calculated mathematical index includes the sum of hydrophobicity values ​​measured during the selected time period, the average of the hydrophobicity values ​​measured during the selected time period, the median of the hydrophobicity values ​​measured during the selected time period, and / or the maximum of the hydrophobicity values ​​measured during the selected time period.

7. The method as described in claim 1 or 2, wherein, The calculated mathematical index includes the difference between consecutive averages of hydrophobicity values ​​measured during the selected time period. The calculated mathematical index includes the difference between consecutive hydrophobicity values ​​multiplied by gain values, and / or The calculated mathematical index includes the hydrophobic measurement signal multiplied by the gain value.

8. The method of claim 1 or 2, wherein the method comprises: The sample is classified into one or more of the following fractions: fibrous fraction, agglomerate fraction, flocculent fraction, fine particle fraction, and colloidal fraction.

9. The method as claimed in claim 1 or 2, wherein, The risk level is calculated by inputting risk indicator inputs into the risk indicator, which includes a mathematical model.

10. The method as claimed in claim 1 or 2, wherein, The risk level is calculated by inputting risk indicator inputs into the risk indicator, which includes an equation.

11. The method as claimed in claim 1 or 2, wherein, The risk level is calculated by inputting risk indicator inputs into the risk indicator, which includes a statistical model.

12. The method as claimed in claim 1 or 2, wherein, The risk level is calculated by inputting risk indicator inputs into the risk indicator, which includes a model based on regression analysis.

13. The method as claimed in claim 1 or 2, wherein, Risk levels are calculated by inputting risk indicator inputs into a rule-based model.

14. The method as claimed in claim 1 or 2, wherein, The risk level is calculated by inputting risk indicator inputs into a risk indicator, wherein the risk indicator includes a threshold for at least one risk indicator input and / or a weighting coefficient for at least one risk indicator input.

15. The method as claimed in claim 1 or 2, wherein, The method includes predicting operational risks and / or final product quality risks caused by changes in process conditions during pulping, board making, or papermaking.

16. The method as claimed in claim 1 or 2, wherein, This method includes: based on operational risk level and / or final product quality risk level, The concentration of hydrophobic contaminants in one or more aqueous streams during the pulping, board-making, or papermaking process can be controlled by the following: Manual and / or automatic adjustment of the dosage of at least one chemical in one or more aqueous streams entering the pulping, board-making, or papermaking process, and / or The dosage of wash water in one or more water-containing streams entering the pulping, board-making, or papermaking process can be adjusted manually and / or automatically.

17. The method as claimed in claim 1 or 2, wherein, Risk indicators are selected or created based on historical operational data regarding the pulping, board-making, or papermaking processes, and / or Risk indicators are process-specific.

18. The method as claimed in claim 1 or 2, wherein, This method includes: outputting the operational risk level and / or the final product quality risk level to the user.

19. The method as claimed in claim 1 or 2, wherein, The method includes instructing users whether the operational risk level and / or the final product quality risk level exceed predefined values.

20. The method of claim 1 or 2, wherein, The method includes issuing an alarm if the operational risk level and / or the final product quality risk level exceed a predefined value.

21. The method of claim 5, wherein, The cumulative sum of hydrophobic movement is the daily cumulative sum of hydrophobicity. The cumulative hydrophobicity time is the time interval during which the hydrophobicity exceeds a predefined hydrophobicity value. The moving median of hydrophobicity is the daily median of hydrophobicity, and / or The moving average of hydrophobicity is the daily average of hydrophobicity.

22. An apparatus for estimating or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process, the apparatus comprising: A component used to obtain a sample from at least one aqueous stream in a pulping, board-making, or papermaking process; A measuring component used to measure the hydrophobicity of at least two samples from the same water-containing stream originating from a pulping, board-making, or papermaking process; A processing unit for generating a hydrophobic measurement signal for the at least one water-bearing flow as a function of time, representing the measured hydrophobicity value. The processing unit is configured to process the hydrophobicity measurement signal of the measured hydrophobicity values ​​of the at least two samples by calculating at least one mathematical index based on the following: to calculate the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product. - A hydrophobic measurement signal for the at least two samples of the at least one water-bearing flow as a function of time, and - Optionally, the amount of particles in the at least one aquifer as a function of time, other characteristics of the at least one aquifer as a function of time, and / or production data as a function of time, wherein the other characteristics of the aquifer include one or more of pH, conductivity, charge, temperature, chemical oxygen demand, particle size, consistency, and ash consistency. The processing unit is configured as follows: The calculated mathematical index, along with optionally, the amount of particles in the at least one aquifer as a function of time, other characteristics of the at least one aquifer as a function of time, and / or production data as a function of time, are used as risk indicator inputs for calculating the operational risk level and / or the final product quality risk level. Based on the risk level calculated for the pulping, board-making, or papermaking process, this indicates the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product.

23. The apparatus for estimating or predicting the operational risk level and / or the final product quality risk level of a pulping, board-making, or papermaking process as described in claim 22, the apparatus comprising: A classifier used to classify samples into grades based on particle quality and / or size.

24. The apparatus of claim 22 or 23, wherein, The measuring component is configured to measure the hydrophobicity values ​​of samples from at least two different water-containing streams originating from a pulping, board-making, or papermaking process; The processing unit is configured to generate a hydrophobicity measurement signal as a function of time for the difference between the hydrophobicity values ​​measured for the at least two water-bearing flows. The processing unit is configured to process the hydrophobicity measurement signal by calculating at least one mathematical index based on the following: to calculate the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product. - The hydrophobic measurement signal generated by the at least two water-bearing flows, and - Optionally, the amount of particles in the at least two aquifers as a function of time, other characteristics of the at least two aquifers as a function of time, and / or production data as a function of time. The processing unit is configured as follows: The calculated mathematical index, along with, optionally, the amount of particles in the at least two aquifers as a function of time, other characteristics of the at least two aquifers as a function of time, and / or production data as a function of time, are used as risk indicator inputs for calculating the operational risk level and / or the final product quality risk level; and Indicates the operational risk level of the pulping, board-making, or papermaking process and / or the quality risk level of the final product, based on the operational risk level and / or the quality risk level of the final product calculated for the pulping, board-making, or papermaking process.

25. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate the risk level by inputting a risk indicator input into a risk indicator, which is a mathematical model.

26. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate the risk level by inputting a risk indicator into a risk indicator, which is an equation.

27. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate the risk level by inputting a risk indicator into a statistical model.

28. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate the risk level by inputting a risk indicator into a model based on regression analysis.

29. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate the risk level by inputting a risk indicator into a rule-based model.

30. The apparatus of claim 22 or 23, wherein, The processing unit is configured to calculate a risk level by inputting a risk indicator input to a risk indicator, wherein the risk indicator includes a threshold for at least one risk indicator input and / or a weighting coefficient for at least one risk indicator input.

31. The apparatus of claim 22 or 23, wherein, The device is configured to predict operational risks and / or final product quality risks caused by changes in process conditions during pulping, board making, or papermaking processes.

32. The apparatus of claim 22 or 23, wherein, The device is configured based on operational risk level and / or final product quality risk level. The concentration of hydrophobic contaminants in one or more aqueous streams during the pulping, board-making, or papermaking process can be controlled by the following: Manual and / or automatic adjustment of the dosage of at least one chemical in one or more aqueous streams entering the pulping, board-making, or papermaking process, and / or The dosage of wash water in one or more water-containing streams entering the pulping, board-making, or papermaking process can be adjusted manually and / or automatically.

33. The apparatus of claim 22 or 23, wherein, The device is configured to select or create risk indicators based on historical operational data regarding the pulping, board-making, or papermaking process, and / or Risk indicators are process-specific.

34. The apparatus of claim 22 or 23, wherein, The device is configured to output the operational risk level and / or the final product quality risk level to the user.

35. The apparatus of claim 22 or 23, wherein, The device is configured to indicate to the user whether the operational risk level and / or the final product quality risk level exceed predefined values.

36. The apparatus of claim 22 or 23, wherein, The device is configured to issue an alarm if the operational risk level and / or the final product quality risk level exceed predefined values.

37. Use of the method of any one of claims 1-21 or the apparatus of any one of claims 22-36 in monitoring and controlling the chemical properties during pulping, board making or papermaking processes.

Images