Method for controlling a sewage treatment plant, in particular for sewage sludge conditioning and dewatering, computer program product, control device, and sewage plant having such a control device

Abstract

The invention relates to a computer-implemented method for controlling a sewage treatment plant (2), in particular for sewage sludge conditioning and dewatering, comprising the steps of: (S300) reading in a value indicative of a supplied dry mass (TM%), (S400) reading in a value indicative of a supplied sludge amount (QSchl), (S500) reading in a value indicative of a turbidity (T) of the centrate water, (S600) reading in a value indicative of a centrate water volume (QZW) if the value indicative of the turbidity (T) of the centrate water is below a threshold value (SW), and (S700) determining a value for a solids proportion of a discharged, dewatered sludge (TM) by evaluating the values indicative of the supplied dry mass (TM%) and the supplied sludge amount (QSchl) and the centrate water volume (QZW).

Description

[0001] Method for controlling a wastewater treatment plant, in particular for sewage sludge conditioning and dewatering, computer program product and control unit, as well as a wastewater treatment plant with such a control unit

[0002] The invention relates to a computer-implemented method for controlling a wastewater treatment plant, in particular for sewage sludge conditioning and dewatering, a computer program product and a control unit for controlling such a method, as well as a wastewater treatment plant with such a control unit.

[0003] Sewage sludge dewatering is a crucial step in wastewater treatment, directly impacting operating costs and environmental impact. Decanter centrifuges are commonly used to reduce the water content of sewage sludge, thereby decreasing the volume and weight of the material to be disposed of. The efficiency and cost-effectiveness of this process depend on a variety of factors, including the cost of flocculants, such as polymers, required for sludge conditioning, the disposal costs of the dewatered sludge, the energy costs for operating the decanter centrifuges, and the water costs for flocculant preparation.

[0004] Currently, the efficiency of these wastewater treatment plants depends heavily on the expertise and dedication of the operating personnel. However, even the most committed operators cannot provide 24-hour online monitoring, thus limiting the possibilities for continuous process optimization. In many cases, wastewater treatment plants therefore operate with fixed, manual settings that do not respond optimally to changing operating conditions and economic factors. This leads to unnecessarily high costs and potentially inefficient operation.

[0005] To avoid overloading the decanter centrifuge, a target quantity can be determined based on known input parameters, e.g. for a thin sludge pump of the wastewater treatment plant.

[0006] To ensure that the correct amount of flocculant is used, the centrate water must be monitored. Firstly, it must be free of solids, and secondly, the amount of flocculant used should not be excessive. Underdosing leads to an increased solids load being returned to the wastewater treatment plant, thus increasing the effort required for wastewater treatment. Furthermore, the dewaterability of the sewage sludge decreases, resulting in higher disposal costs. The required amount of flocculant can generally be calculated. Centrate water values ​​below 100% indicate underdosing, while values ​​above 100% indicate overdosing.

[0007] The centrate water is free of solids. However, this is not a measure of a high dry matter content in the dewatered sludge. Typically, the motor load (e.g., torque and / or current draw) of a centrifuge drive is measured, and the dosage is adjusted accordingly. However, a high motor load is not necessarily indicative of a higher dry matter content in the dewatered sludge.

[0008] However, with improved energy efficiency of decanter centrifuges, this measurement becomes less precise.

[0009] Known methods allow for direct measurement of the solids discharge from the decanter centrifuge using a solids sampling device in combination with microwave dry matter measurement. However, the measurement results are only available with a time delay, meaning that any dosing adjustment based on this data lags behind the requirements.

[0010] Therefore, there is a need to show ways in which improvements can be achieved here.

[0011] The present invention solves this problem through the subject matter of the independent claims.

[0012] This task is solved by a computer-implemented method for controlling a wastewater treatment plant, in particular for sewage sludge conditioning and dewatering, with the following steps:

[0013] Reading a value indicative of an added dry mass,

[0014] Reading a value is indicative of the amount of sludge supplied.

[0015] Reading a value indicative of the turbidity of the centrate water, reading a value indicative of a centrate water volume if the value indicative of the turbidity of the centrate water is less than a threshold value, and

[0016] Determining a value for the solids content of a discharged, dewatered sludge by evaluating the values ​​indicative of the added dry mass and the added sludge quantity as well as the centrate water volume.

[0017] A value for the solids content of the discharged dewatered sludge is determined by mass balancing. For this purpose, a centrate water measurement is performed to indicate the centrate water volume, and a sludge measurement is performed to indicate the sludge quantity, both using respective flow meters.

[0018] The threshold value is chosen such that the centrate water can be considered free of solids. In other words, the threshold value is set so low that it corresponds to the lower detection limit of a turbidity sensor that measures the turbidity of the centrate water.

[0019] For example, a control unit includes a computing unit, i.e., a computer, on which the procedure is implemented.

[0020] The value indicative of turbidity is, for example, a value indicating the attenuation of light intensity due to scattering and / or absorption by a liquid flow passing through the turbidity sensor. In other words, the values ​​detected by the turbidity sensor are indicative of the solids content of the liquid flow. The turbidity sensor can be configured for inline measurement.

[0021] The values ​​indicative of the dry matter input, the sludge input, and the centrate water volume can be pre-known and manually entered into a control unit of the system by a user. The turbidity value is preferably detected by the turbidity sensor and transmitted electronically to the control unit, which corresponds to reading the value. This transmission of values ​​to the control unit can be carried out using standard wired or wireless methods.

[0022] This eliminates the need for known measures, such as measuring motor load, e.g., torque or current consumption of a decanter centrifuge drive, because a high motor load is not necessarily indicative of a higher dry matter content of the dewatered sludge.

[0023] This provides real-time data that can be used to operate a wastewater treatment plant more efficiently and to improve sewage sludge conditioning and dewatering.

[0024] According to one embodiment, the step of determining the value for the solids content of the discharged, dewatered sludge by evaluating the values ​​indicatively for the added dry mass, which is the absolute mass of the solids in the sludge after subtraction of the water, where the value is a proportion of the total mass of the sludge, and for the added sludge quantity, which is the sum of dry mass and water, also used as an absolute indicative value, as well as for the centrate water volume, which is the volume of the liquid that is produced during the dewatering of the sludge in the wastewater treatment plant and that remains after solids have been separated, comprises forming a product of the values ​​for the added sludge quantity and the added dry mass, which is divided by the difference between the values ​​for the added sludge quantity and the centrate water volume.It may be possible to calculate the difference between the values ​​for the amount of sludge added and the volume of centrate water. This makes it particularly easy to determine the solids content of a discharged, dewatered sludge.

[0025] According to a further embodiment, at least one operating parameter is determined according to a selected operating mode by evaluating at least the specified value for a solids content of a discharged, dewatered sludge. The selected operating mode specifies a quality or optimization criterion according to which the at least one operating parameter is optimized. One operating mode can be an ECO mode, in which the operation of the wastewater treatment plant is optimized based on the lowest operating costs, including minimal flocculant consumption, lowest electricity costs, and lowest disposal costs. Another operating mode can be a Poly-Min mode, in which the decanter centrifuge is controlled in such a way as to minimize flocculant consumption. A further operating mode can be a TM mode, in which the dry solids content of the dewatered sludge is maximized.Another operating mode can be a fully automatic mode, in which all relevant operating parameters are automatically and continuously adjusted, ensuring that the operation of the wastewater treatment plant is always optimally adapted to the current conditions.

[0026] According to another embodiment, a control unit determines at least one operating parameter according to a selected operating mode by evaluating at least the determined value for a solids content of a discharged, dewatered sludge. Through the use of artificial intelligence (AI), such control units can be designed to exhibit intelligent behavior, for example, through machine learning. Deep learning (also known as multi-layered learning or deep learning) refers to a machine learning method that uses artificial neural networks (ANNs) with numerous hidden layers between an input layer and an output layer. Such artificial neural networks have a plurality of artificial neurons, which, in the case of deep neural networks, are arranged in numerous hidden layers between the input and output layers.Artificial neural networks of this type are trained with training data during a training phase before being put into operation. The training can include optimizing the artificial neural network, for example, through supervised learning. The artificial intelligence can learn from examples and generalize them after the training phase. For example, in machine learning, algorithms build a statistical model based on the training data. This means that they don't simply memorize examples, but rather recognize patterns and regularities in the training data. In this way, the artificial intelligence can also evaluate unknown data (transfer of learning).

[0027] The training data for the AI ​​unit can be operational data from the wastewater treatment plant or comparable or identical wastewater treatment plants, recorded during their operation. This operational data serves as input for the training data.

[0028] The Kl unit may have been trained using training data comprising a variety of training operating parameters. These parameters are derived by using training turbidity values, dry matter values, sludge volume values, centrate water volume values, and, for example, other operating parameters listed below, as input, and training solids content values ​​as output. A larger amount of training data can increase the accuracy of the predictions made by the Kl unit's algorithm.

[0029] The training data obtained in this way has classifications or labels, e.g., according to the corresponding operating mode (BM), such as ECO mode, Poly-Min mode, TM mode, or fully automatic mode. Alternatively, the training data can be automatically classified by a classifier and assigned to the respective operating modes (BM).

[0030] After the training phase, the KL unit can read and evaluate the specified value for the solids content of the discharged, dewatered sludge and the operating values ​​in order to operate the wastewater treatment plant according to the selected operating mode BM with the operating parameter BP or operating parameter data set.

[0031] The training solid fractions corresponding to the inputs can essentially be viewed as label cavitation values, serving as a key to evaluating the training data and comparing it to outputs determined during training for a given input. The data can be labeled manually or automatically.

[0032] According to one embodiment, the labels are manually assigned to the solids content data collected on the centrifuge or another centrifuge of substantially equivalent design prior to training the Kl unit.

[0033] In one embodiment, the input data can further include (ambient and / or internal) temperature data, humidity data, process data such as rotational speed or pressure data, with which the AI ​​unit was preferably also trained according to the training described above. The training data also includes equivalent training, labeling, validation, and test signal data of the aforementioned types, acquired in the same manner. Alternatively or additionally, this data is used to increase the reliability of the machine learning tool and thus of the method according to the invention. The additional data can be used, in particular, to compensate for fluctuations in one of the two monitored factors, especially temperature or possibly ambient air pressure.

[0034] It is particularly advantageous if additional parameters of the supplied sludge are also utilized by the Cl unit in the manner described above, either through regular manual input of laboratory values ​​or online measurements using sensors for phosphates (PO4), ammonium ions (NH4), nitrates (NO3), polycyclic aromatic hydrocarbons (PAHs), pH values, calcium, magnesium and / or heavy metals such as Cd, Pb, Cr, Cu, Zn, and / or Hg.

[0035] Values ​​already recorded in the wastewater treatment plant influent can also be recorded by the KL, such as COD (Chemical Oxygen Demand), TOC (Total Organic Carbon), TSS (Total Suspended Solids (as a unit of measurement for turbidity), N / P values ​​(Nitrogen / Phosphorus values), BOD (Biological Oxygen Demand) or FOG (Fat Oil Grease).

[0036] Furthermore, the polymeric flocculant (pFM) used, with its properties (e.g., cationic / anionic charge density, crosslinking type, type no., manufacturer), can be fed into the KL unit so that the KL unit can evaluate the data accordingly.

[0037] By monitoring the wastewater inflow and the sludge values, the Kl can suggest the selection of the pFm without having to carry out extensive tests with various pFm.

[0038] As is typical for the training of AI units, the training, labeling, validation and test data used for these purposes should preferably have the same or nearly the same probability distribution to ensure optimal training and validation of the machine learning tool.

[0039] In another embodiment, the AI ​​unit uses a different algorithm based on a classification algorithm. In particular, the algorithm can be based on linear regression, Naive Bayes, nearest neighbor, a decision tree, or a support vector machine.

[0040] As the KL unit becomes more sophisticated, the exact solids content can be determined not only when there is no turbidity, but also when there is slight turbidity in the centrate water, and this content can be deducted from the solids discharge of the decanter centrifuge in the calculation.

[0041] Furthermore, the invention includes a computer program product and a control unit for controlling such a process, as well as a sewage treatment plant with such a control unit.

[0042] The invention will now be explained with the aid of a drawing. The drawing shows:

[0043] Fig. 1 shows a schematic representation of the components of a wastewater treatment plant.

[0044] Fig. 2 shows a schematic representation of a control unit for controlling the sewage treatment plant shown in Fig. 1.

[0045] Fig. 3 shows a schematic representation of a process flow for the operation of the wastewater treatment plant shown in Fig. 1.

[0046] Reference is first made to Fig. 1.

[0047] The diagram shows components of a wastewater treatment plant 2, such as a multi-stage wastewater treatment plant, which in the present embodiment is designed at least for sewage sludge conditioning and dewatering.

[0048] The components of the wastewater treatment plant 2 are shown: a decanter centrifuge 8, a sludge storage tank 10 (in the present embodiment a thin sludge storage tank), a sludge transfer pump 12, a flow sensor 14, a solids sensor 16, a working solution storage tank 18, a transfer pump 20, a second flow sensor 22, a turbidity sensor 24, a pressure sensor 26, a third flow sensor 28 and a control valve 30, as well as a solids outlet 32.

[0049] In the present embodiment, the turbidity sensor 24 is configured to detect values ​​for the attenuation of light intensity due to scattered light formation and / or absorption caused by a liquid flow passing through the turbidity sensor 24. In other words, the values ​​detected by the turbidity sensor 24 are indicative of the solids content of the liquid flow. Thus, the turbidity sensor 24 is designed for inline measurement.

[0050] The turbidity sensor 24 records a value for the turbidity T of the centrate water.

[0051] The solids sensor 16 can be a microwave dry matter sensor or a turbidity sensor, with which a value for the solids content of the sludge being fed in and dewatered can be recorded.

[0052] In the present embodiment, the first flow sensor 14, the second flow sensor 22, and the third flow sensor 28 (also called flowmeters) are configured to detect values ​​for the flow rate of a liquid stream. In this embodiment, the first flow sensor 14 and / or the second flow sensor 22 and / or the third flow sensor 28 are each configured to detect values ​​for a volumetric flow rate. In a different embodiment, the first flow sensor 14 and / or the second flow sensor 22 and / or the third flow sensor 28 can each be configured to detect values ​​for a mass flow rate.

[0053] During operation, the decanter centrifuge 8 receives, for example, thin sludge or sludge to be dewatered from the sludge storage tank 10 via the sludge feeder pump 12. The quantity of sludge supplied, Qschi, is measured by the flow sensor 14, and the dry matter content of the thin sludge or sludge to be dewatered is measured by the solids sensor 16.

[0054] Furthermore, at the inlet side of the decanter centrifuge 8, flocculant working solution is fed from the working solution reservoir 18 by means of the feed pump 20. The quantity of flocculant added Q GL P FM is detected by the second flow sensor 22.

[0055] Furthermore, during operation, a turbidity value T of the centrate water is measured at the outlet of the decanter centrifuge 8 by a turbidity sensor 24, which in this embodiment is arranged in a bypass 34. Additionally, a pressure value is measured by the pressure sensor 26. Furthermore, in this embodiment, a third flow sensor 28 measures the centrate water volume Qzw.

[0056] By changing the position of the control valve 30, it can be ensured that a minimum fill level is maintained in the area of ​​the third flow sensor 28, thus guaranteeing an air-free space and ensuring that the third flow sensor 28 functions correctly and provides error-free readings for the centrate water volume Qzw. The position of the control valve 30 can be changed depending on the pressure value detected by the pressure sensor 26. A low pressure value indicates that the minimum fill level has been undershot, whereupon the control valve 30 is closed further to reduce the cross-sectional area and cause the liquid level in the area of ​​the third flow sensor 28 to rise above the minimum fill level.

[0057] In the present embodiment, the flocculant working solution contains a flocculant, such as a polymer. In contrast to the present embodiment, the flocculant working solution may also contain a different flocculant.

[0058] Reference is now also made to Fig. 2.

[0059] The diagram shows a control unit 4 with a control unit 6. For the tasks and functions described below, the control unit 4 with the control unit 6 can have appropriately designed hardware and / or software components.

[0060] In the present embodiment, the control unit 4 is designed to read in a value indicative of a supplied dry mass TM%, which is known.

[0061] Furthermore, in the present embodiment, the control unit 4 is designed to read in the value for the sludge quantity Qschi detected by the flow sensor 14 and the value indicative of a turbidity T of the centrate water.

[0062] Furthermore, in the present embodiment, the control unit 4 is configured to compare the measured turbidity value T with a predetermined threshold value SW. If the indicative value for the turbidity T of the centrate water is less than the threshold value SW, the control unit 4 also reads the centrate water volume Qzw measured by the third flow sensor 28.

[0063] This ensures that the measured value for the centrate water volume Qzw is only used if the filtrate water is essentially free of solids. Otherwise, solid particles would distort the turbidity measurement and the subsequent mass balance analysis would be inaccurate.

[0064] Finally, in the present embodiment, the control unit 4 is designed to determine a value for a solids content of a discharged, dewatered sludge TM by evaluating the values ​​indicatively for the supplied dry mass TM% and the supplied sludge quantity Qschi as well as the centrate water volume Qzw.

[0065] For this purpose, the control unit 4 in the present embodiment is designed to form a product of the values ​​for the supplied sludge quantity Qschi and the supplied dry mass TM%, which is divided by the difference of the values ​​for the supplied sludge quantity Qschi and the centrate water volume Qzw:

[0066] Discharge TS =Qschl XTM % x 100 Qschl-Qzw

[0067] Furthermore, in the present embodiment, the control unit 4 is designed to determine at least one operating parameter BP or operating parameter data set with a plurality of operating parameters BP according to a selected operating mode BM by evaluating the determined value for a solids content of a discharged, dewatered sludge TM.

[0068] The operating modes BM, which can be selected by an operator of the wastewater treatment plant 4, e.g. via a graphical user interface of an HMI, can be, for example, an ECO mode, a Poly-Min mode, a TM mode or a fully automatic mode.

[0069] The ECO mode is optimized to ensure operation based on the lowest possible operating costs, including minimal flocculant consumption, lowest electricity costs, and lowest disposal costs. This results in an environmentally sound operating method.

[0070] The Poly-Min mode is optimized to control the decanter centrifuge 8 in such a way as to minimize flocculant consumption, which usually results in lower dewatering efficiency.

[0071] The TM mode is optimized to maximize the dry matter content of the dewatered sludge, resulting in higher disposal efficiency, but may lead to higher flocculant and energy consumption.

[0072] The fully automatic mode ensures fully automated operation of the decanter centrifuge 8. The control and adjustment of all relevant operating parameters BP is automatic and continuous, ensuring that the operation of the wastewater treatment plant 4 is always optimally adapted to the current conditions. This limits the operation of the wastewater treatment plant 4 to purely monitoring functions, so that the operator only needs to monitor the system performance and intervene only when necessary. This reduces the need for qualified personnel and enables efficient operation even in the event of a shortage of specialized operators.

[0073] In the present embodiment, the control unit 4 is designed to read in the following operating values ​​BW via interfaces:

[0074] Added sludge quantity Qschi, e.g. a thin sludge quantity, in m³ 3 / h, measured with the flow sensor 14, supplied and known dry matter TM% or solids load contained in the sewage sludge, e.g. thin sludge, permissible solids load of the decanter centrifuge 8, e.g. in Mg / h, quantity of the working solution with flocculants supplied for conditioning the sludge, e.g. in m 3 / h, measured with the flow sensor 22,

[0075] The concentration of the working solution pFM conc, e.g. in %, is e.g. between 0.2% and 1%.

[0076] Consumption of flocculant pFM Dos in kg per Mg dry matter in % DM, determined based on the amount added and the concentration of the working solution, which

[0077] Measurement of the centrate water volume Qzw with the third flow sensor 28 as well as monitoring for clean, solids-free centrate water and to avoid overdosing with flocculants by measuring the turbidity T with the turbidity sensor 24.

[0078] In the present embodiment, the control unit 4 is also designed to determine the solids content of the discharged, dewatered sludge TM.

[0079] To avoid overloading the decanter centrifuge 8, a target quantity for the sludge pump 12 is calculated based on the aforementioned input parameters.

[0080] With a permissible solids load of the decanter centrifuge 8 of, for example, 0.5 Mg dry matter / h and 3.5% dry matter, for example in the thin sludge, the permissible conveying rate or sludge quantity supplied Qschi is 0.5 Mg dry matter / h / 3.5% = 14.28 m³ 3 / h.

[0081] A value for a quantity of flocculant Q GL can then be used. PFM is determined. This is the product of the values ​​for the added sludge quantity Qschi and the added dry matter TM%, as well as a read-in value indicative of a consumed quantity of flocculant pFM Dos, divided by a read-in value indicative of a concentration of a flocculant working solution pFm Konz. In other words, the required quantity of flocculant Q GL P FM then results in:

[0082] For example, a sludge quantity Qschi of 15 m 3 / h, 3.5% for the added dry matter TM%, 12 kg / Mg for the dry matter of the amount of flocculant consumed pFM Dos and 0.30% for the concentration of the flocculant working solution pFm Conz results in a value of Q GL P FM = 2.1 m 3 / h.

[0083] Furthermore, in the present embodiment, the control unit 4 is designed to read in the following additional operating values ​​BW via interfaces:

[0084] Flocculant costs: Cost per kg of flocculant required for conditioning the sewage sludge,

[0085] Disposal costs: Cost per kg of dewatered sewage sludge,

[0086] Electricity costs: Variable costs for operating decanter centrifuge 8, depending on the time of day and day of the week.

[0087] Water costs: Costs for water required to prepare the flocculant working solution,

[0088] Throughput capacity: Operating time of the decanter centrifuge 8 depending on the throughput capacity, which can vary between 200 kg and 1400 kg per hour,

[0089] Amount of sewage sludge available for dewatering: Daily / weekly / monthly and annually amount of sewage sludge to be dewatered,

[0090] Flocculant consumption and dewatering efficiency:

[0091] Torque or motor load of the decanter centrifuge 8, e.g. in % or Nm,

[0092] Differential speed from a drum to a discharge screw of the decanter centrifuge 8, e.g. in 1 / min,

[0093] Sludge disposal costs per kg of dewatered sludge

[0094] Density of sewage sludge, e.g. in kg / dm³ 3

[0095] Other operating parameters BP of the decanter centrifuge 8, such as storage temperature, vibrations, operating hours, etc.

[0096] These operating values ​​BW can be read and evaluated by the control unit 6 to determine the operating parameter BP or an operating parameter data set for the operation of the wastewater treatment plant 4, in particular the decanter centrifuge 8. Operating parameters BP can include speed specifications for the decanter centrifuge 8, quantity specifications for consumables or materials, such as the quantity of flocculant working solution, the sludge quantity, the centrate water volume Qzw, the water quantity, and / or the electricity quantity.

[0097] The operating parameter BP can also include specifications for operating times. For example, the availability of renewable energies can be taken into account when operating decanter centrifuge 8. This allows the use of decanter centrifuge 8 to be specifically maximized during times when there is a high availability of green energy from solar, wind, or hydropower. This not only contributes to further reducing energy costs but also supports the environmentally friendly operation of wastewater treatment plant 4 by minimizing the share of fossil energy sources. For instance, operation can be prioritized during times of day when there is a surplus of solar energy, or during strong winds when wind energy is abundant. At the same time, operating times during periods of high fossil energy production can be minimized to reduce environmental impact.

[0098] For this purpose, AI unit 6, which, for example, contains an artificial neural network, was trained with training data during a training phase before AI unit 6 was put into operation. Such neural networks, for example, have a plurality of artificial neurons, which, in the case of deep neural networks, are arranged in numerous intermediate layers between an input layer and an output layer.

[0099] Training can involve optimizing the artificial neural network, for example, using supervised learning, such as backpropagation. Alternatively, training can also be performed using unsupervised learning.

[0100] To obtain training data for the training of the Cl-Unit 6, operational data from wastewater treatment plant 8 or comparable wastewater treatment plants are recorded during operation. The training data obtained in this way can be pre-labeled, e.g., according to the operating mode (BM), such as ECO mode, Poly-Min mode, TM mode, or fully automatic mode. Alternatively, the training data can be automatically classified by a classifier and assigned to the respective operating modes (BM).

[0101] After the training phase, the KL unit 6 reads and evaluates the determined value for a solids content of the discharged, dewatered sludge TM and the operating values ​​BW in order to operate the wastewater treatment plant 4 according to the selected operating mode BM with the operating parameter BP or operating parameter data set.

[0102] Reference is now also made to Fig. 3.

[0103] In the first step, S100, training data (TD) is acquired and labeled for training the AI ​​unit 6, and then used to train the AI ​​unit 6. After step S100, the AI ​​unit 6 is ready for operation.

[0104] In a further step, S200 selects an operating mode BM.

[0105] In a further step S300, the control unit 4 reads an indicative value for the supplied dry matter TM%.

[0106] In a further step S400, the control unit 4 reads an indicative value for a supplied amount of sludge Qschi.

[0107] In a further step S500, the control unit 4 reads an indicative value for the turbidity T of the centrate water.

[0108] In a further step S600, the control unit 4 reads an indicative value for a centrate water volume Qzw if the indicative value for the turbidity T of the centrate water is less than the threshold value SW.

[0109] In a further step S700, the control unit 4 determines a value for the solids content of the discharged, dewatered sludge TM by evaluating the values ​​indicatively for the added dry mass TM% and the added sludge quantity Qschi as well as the centrate water volume Qzw.

[0110] For this purpose, a product is formed from the values ​​for the amount of sludge supplied Qschi and the amount of dry matter supplied TM%, which is divided by the difference between the values ​​for the amount of sludge supplied Qschi and the centrate water volume Qzw.

[0111] In a further step S800, the control unit 4 with the trained KL unit 6 determines the operating parameter BP or an operating parameter data set according to the selected operating mode BM by evaluating the determined value for a solids content of a discharged, dewatered sludge TM and the read-in operating values ​​BM.

[0112] As the Kl unit 6 becomes more fully trained, the exact solids content can be determined even with slight turbidity T of the centrate water, whereby this content can be subtracted from the solids discharge at the solids discharge 32 of the decanter centrifuge 8.

[0113] In contrast to the present embodiment, the sequence of steps can also be different. Furthermore, several steps can be executed simultaneously. Additionally, in contrast to the present embodiment, individual steps can be skipped or omitted.

[0114] The control unit 4 with the control unit 6 provides the operating parameter BP or an operating parameter data set, which allows the wastewater treatment plant 4 to be operated more efficiently and improves sewage sludge conditioning and dewatering.

[0115] Reference symbol list

[0116] 2 Wastewater treatment plant

[0117] 4 Control unit

[0118] 6 classroom units

[0119] 8 Decanter centrifuge

[0120] 10 mud template

[0121] 12 Sludge pump

[0122] 14 Flow sensor

[0123] 16 solids sensor

[0124] 18 Instructions for use

[0125] 20 Pump

[0126] 22 Flow sensor

[0127] 24 turbidity sensors

[0128] 26 Pressure sensor

[0129] 28 Flow sensor

[0130] 30 control slides

[0131] 32 Solid waste outlet

[0132] 34 Bypass

[0133] BM operating mode

[0134] BP operating parameters

[0135] BW operating values ​​pFM Dos consumed amount of flocculant pFm Conc Concentration of flocculant working solution

[0136] Q GLpFM Amount of flocculants

[0137] Qschi supplied amount of sludge

[0138] Qzw centrate water volume

[0139] SW threshold

[0140] T Turbidity

[0141] TM Mud

[0142] TM% added dry matter

[0143] S100 step

[0144] S200 step

[0145] S300 step

[0146] S400 step

[0147] S500 step

[0148] S600 step

[0149] S700 step

[0150] S800 step

Claims

Patent claims 1. Computer-implemented method for controlling a wastewater treatment plant (2), in particular for sewage sludge conditioning and dewatering, comprising the steps: (S300) Reading a value indicative of added dry matter (DM%), (S400) Reading a value indicative of a supplied sludge quantity (Qschl), (S500) Reading a value indicative of turbidity (T) of the centrate water, (S600) Reading a value indicative of a centrate water volume (Qzw) when the value indicative of the turbidity (T) of the centrate water is less than a threshold value (SW), and (S700) Determining a value for the solids content of a discharged dewatered sludge (DM) by evaluating the values ​​indicative of the dry matter content (DM%) and sludge quantity (Qschi) added, as well as the centrate water volume (Qzw).

2. Computer-implemented method according to claim 1, wherein step (S700) comprises determining the value for the solids content of the discharged dewatered sludge (TM) by evaluating the values ​​indicatively for the added dry matter (TM%) and the added sludge quantity (Qschi) as well as the centrate water volume (Qzw), forming a product of the values ​​for the added sludge quantity (Qschi) and the added dry matter (TM%), which is divided by the difference of the values ​​for the added sludge quantity (Qschi) and the centrate water volume (Qzw).

3. Computer-implemented method according to claim 1 or 2, comprising the further step of: (S800) Determine at least one operating parameter (BP) according to a selected operating mode (BM) by evaluating at least the determined value for a solids content of a discharged, dewatered sludge (TM).

4. Computer-implemented method according to claim 3, wherein the step (S800) determining at least one operating parameter (BP) according to a selected operating mode (BM) is carried out by evaluating at least the determined value for a solids fraction of a discharged, dewatered sludge (TM) from a KL unit (6).

5. Computer-implemented method according to one of the preceding claims, wherein the value indicative of turbidity is a value for attenuation of light intensity by scattering and / or absorption by a liquid flow passed through the turbidity sensor.

6. Computer-implemented method according to one of the preceding claims, wherein the dry mass supplied is an absolute mass of the solids of the sludge after subtraction of the water and the indicative value for this is a proportion of the total mass of the sludge, 7. Computer-implemented method according to one of the preceding claims, wherein the amount of sludge supplied is the sum of dry mass and water, which is used as an absolute indicative value therefor.

8. Computer-implemented method according to any of the preceding claims, wherein the centrate water volume is the volume of the liquid that is produced during the dewatering of the sludge in the sewage treatment plant and that remains after solids have been separated.

9. Computer-implemented method according to one of the preceding claims, wherein a computer-implemented Cl unit is used for evaluation in step S700 and wherein the Cl unit was trained on training data comprising a plurality of sets of training operating values ​​obtained by using training turbidity values, dry mass values, sludge quantity values, and centrate water volume values ​​as input and Training solid content values ​​are obtained as output, with the labels being solid content values ​​corresponding to the inputs.

10. Computer program product, configured to perform a method according to any one of claims 1 to 9.

11. Control unit (4) for controlling a wastewater treatment plant (2), in particular for sewage sludge conditioning and dewatering, wherein the control unit (4) is configured to read indicative values ​​for a dry matter content (DM%) supplied, to read indicative values ​​for a sludge quantity (Qschi), to read indicative values ​​for a turbidity (T) of the centrate water, and to read indicative values ​​for a centrate water volume (Qzw) if the indicative value for the turbidity (T) of the centrate water is less than a threshold value (SW), and to determine a value for a solids content of a discharged, dewatered sludge (DM) by evaluating the indicative values ​​for the dry matter content (DM%) supplied and the sludge quantity supplied (Qschi) as well as the (Qzw).

12. Control unit (4) according to claim 11, wherein the control unit (4) is configured to determine the value for the solids content of the discharged, dewatered sludge (TM) by evaluating the values ​​indicative for the supplied dry matter (TM%) and the supplied sludge quantity (Qschi) as well as the centrate water volume (Qzw), forming a product of the values ​​for the supplied sludge quantity (Qschi) and the supplied dry matter (TM%) and dividing by the difference of the values ​​for the supplied sludge quantity (Qschi) and the centrate water volume (Qzw).

13. Control unit (4) according to claim 11 or 12, wherein the control unit (4) is configured to determine at least one operating parameter (BP) according to a selected operating mode (BM) by evaluating at least the determined value for a solids fraction of a discharged, dewatered sludge (TM).

14. Control unit (4) according to claim 13, wherein the control unit (4) has a KL unit (6) configured to control at least one operating parameter (BP) according to a selected operating mode (BM) by evaluating at least the determined value for a solids content of a discharged, dewatered sludge (DM).

15. Wastewater treatment plant (2), in particular for sludge conditioning and -drainage, with a control unit (4) for controlling the sewage treatment plant (2) according to one of claims 11 to 14.

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