Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F3/00—Biological treatment of water, waste water, or sewage
  • C02F3/006—Regulation methods for biological treatment
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
  • G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
  • G05B23/0235—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
  • G06Q50/06—Energy or water supply
• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F2209/00—Controlling or monitoring parameters in water treatment
  • C02F2209/001—Upstream control, i.e. monitoring for predictive control
• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F2209/00—Controlling or monitoring parameters in water treatment
  • C02F2209/005—Processes using a programmable logic controller [PLC]
  • C02F2209/006—Processes using a programmable logic controller [PLC] comprising a software program or a logic diagram
• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F2209/00—Controlling or monitoring parameters in water treatment
  • C02F2209/005—Processes using a programmable logic controller [PLC]
  • C02F2209/008—Processes using a programmable logic controller [PLC] comprising telecommunication features, e.g. modems or antennas
• • C—CHEMISTRY; METALLURGY
  • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
  • C02F2303/00—Specific treatment goals
  • C02F2303/14—Maintenance of water treatment installations
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/20—Pc systems
  • G05B2219/25—Pc structure of the system
  • G05B2219/25428—Field device
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/20—Pc systems
  • G05B2219/26—Pc applications
  • G05B2219/2605—Wastewater treatment

Definitions

• the present invention relates to the field of water treatment.
• the present invention is concerned with the detection of operating anomalies in water treatment stations.
• the anomalies detected may be the following:
• the detection of an abnormal phenomenon for example foaming.
• this anomaly detection can be of interest in more than one way: on the one hand, it is difficult (if not impossible) for the on-site operator to monitor all of the station's parameters in real time.
• An anomaly detection algorithm coupled with relevant measurements of various station parameters would then be particularly advantageous to allow the operator to very quickly alert the operator of a problem on the station, a problem that he can then address in the as fast as we can.
• the suppliers of equipment and consumables are most often not present on the station.
• a tool could be extremely useful for alerting them in the event of a failure of one of the devices (or deviation from “normal” operation announcing a failure) or even warning them in the event of overconsumption (or use non-optimal) of one of the consumables (for example a gas), and to allow them to follow in real time the probability of an anomaly of the station (and alert and help if necessary remotely the operator on site for the optimization of its process).
• the solutions currently proposed in this industry or in the literature do not advocate the use of sensors, they advocate the performance of laboratory analyzes, carried out at regular intervals, for example every week or every month, to allow to detect an anomaly. It is understood that then the frequency of detection of anomalies is low, this method can only cause to miss a good number of important events occurring in the station.
• the problem in this case is that the operator generally has at his disposal a very large amount of data due to many sensors, sensors that have a lot of variability, and following all these sensors independently of each other can lead to a lot of triggering. false alarms while missing the “real” anomalies.
• an increase in the electric current of the pump allowing the aeration of the basins can have two causes: an increase in the pump speed (if, for example, the gas flow is increased) or a failure of the motor.
• the measurement of the electric current alone is therefore insufficient to provide a reliable alarm, while if it is coupled with other measurements such as for example the oxygen demand of the aeration basin, the dissolved oxygen concentration, or the oxygen flow rate , and thanks to a suitable anomaly detection tool, a reliable alarm can be generated.
• some water treatment stations operate in a very variable way depending on the upstream process.
• the objective of the present invention is therefore to propose a new method for detecting anomalies occurring in such an effluent treatment installation, a method which is based on an algorithm.
• the algorithm can process data of a wide variety of types, such as sensor measurements, machine status (in service / shutdown), production number, sensor calibration data, laboratory analysis files., each input data item being associated with a probability of occurrence which allows the analysis of complex scenarios.
• the algorithm makes it possible to process a large amount of data. With the expected increase in the number of sensors and data available, it is essential to have a tool that can aggregate all this data, even in very large numbers.
• the number and nature of the data used as input is not limited.
• a descriptive model is complex to implement, it requires development and validation steps for each configuration of the wastewater treatment plant and possibly for each operating mode.
• the algorithm proposed here is on the contrary a statistical tool, it is not very complex to implement and only requires a learning step when setting up. It can then be enriched with new data over the life of the station, for example with the addition of new sensors or new sources of information relating to the station or its environment.
• It allows a complex analysis in real time: by associating a large number of weak signals (ie small variations of different parameters which do not trigger alerts when we look at them separately from each other) we can reveal a more important problem .
• a probability law links the various signals in order to analyze them in a global way and thus respect the dependence between certain events.
• the algorithm results in a single indicator that reflects the general condition of the station: a probability. If this probability is high, it means that the combination of data fed back into the algorithm is very probable, which means that the operation is normal. If the probability is low, it means that either a single or a combination of data from the algorithm have a low probability of occurrence and this low probability of occurrence alerts on an anomaly on the station.
• the input data to the model can be of various kinds. It can be for example:
• the sensors can be installed at various locations in the station, for example at equipment level (pumps, turbines, etc.), or even directly in aeration basins, or at inlet or outlet pipes. exit from the station ... etc ....
• operation may be different depending on the season (station shutdown in summer, colder temperatures on average in winter, etc.), the day of the week (no production upstream from certain stations on weekends ), or even by the hour (less effluent entering the municipal wastewater treatment plant during the night) etc ... For this it may be interesting to correlate the data of the station to the hour, to the day of the week or the season.
• Dissolved Oxygen Sensor This can be an electrochemical or optical sensor, but preferably an optical sensor is used. This sensor placed in the aeration basin allows a measurement of the dissolved oxygen concentration.
• This measurement can be particularly relevant simultaneously with a strong aeration of the basin, since the rate of growth during aeration (and of decrease when stopping the aeration) of the dissolved oxygen concentration are respectively (coupled to the flow rate of aeration) good indicators of the proper functioning of the ventilation equipment on the one hand, and of the oxygen demand of the activated sludge in the aeration basin on the other.
• Electrochemical probe such as pH or redox probes, preferably probes comprising 3 electrodes will be used in order to compensate for the impact of certain interfering ions, as well as probes fitted with temperature sensors in order to compensate for the impact of temperature.
• the use time of the probe, as well as the temporal variation of the measurement can also be followed because they are indicators of the good functioning of the probe, and therefore of the reliability of the measurement.
• Electrochemical probes have a membrane that is permeable for certain chemical species only, so they can measure the concentrations of ammonium, nitrate, or other chemical species that we are trying to degrade in the station.
• probes comprising 3 electrodes and temperature compensation will be preferred.
• Spectral probe There are more and more probes on the market for measuring the organic load, the nitrogen load or the quantity of matter in suspension by spectrophotometry. Several types of probes can be used: measurement of the absorbance at one or more wavelengths, measurement of the fluorescence peak over a greater or lesser range of wavelengths. In all the In this case, the optical measurement can be correlated with the concentration of organic or nitrogen pollution. Preference will be given to probes allowing the measurement of the absorbance spectrum over a wide range, thus making it possible on the one hand to compensate for the effect of turbidity, and on the other hand to construct more robust correlations. This probe will advantageously be placed upstream and / or downstream of the aeration basin.
• the concentrations of different chemical species can be obtained by online analyzers.
• the analyzer is advantageously located near the basin, and a sample is taken at regular intervals for analysis.
• Turbidity A probe allowing the measurement of turbidity can be used, or any other measurement that can be correlated with turbidity, or with the concentration of suspended solids (backscattered light, light scattered at 60 °, absorbed light, etc.) .
• This probe can be placed upstream of the aeration basin, or downstream, or directly in the aeration basin.
• Conductivity is a fairly reliable and easily measurable indicator of water quality. It can in some cases be correlated with chemical oxygen demand (COD - in municipal water for example). It can be measured by a conductive or inductive method, or any other method to estimate the conductivity. This probe can be placed in the pipes upstream or downstream of the station, or in the aeration basin directly.
• Gas flow the flow of oxygen (or air) injected into the aeration basin can be monitored.
• a flow meter (preferably a thermal mass flow meter) is placed upstream of the gas injection equipment in the basin.
• Vibration sensor The vibrations are measured on the water gas injection equipment.
• the sensor is preferably placed at the level of the gear motor (or alternatively at the level of the motor).
• the tracked signal can be either the vibration spectrum, or a deviation from a vibration spectrum generated in normal operation.
• Amperometric clamp placed at the power supply of the ventilation equipment allows the measurement of the motor current. This clamp can be accompanied by a temperature measurement.
• Ultrasonic or electromagnetic flowmeters can be used at various points in the station: upstream for the effluent flow entering, downstream for the outgoing treated water flow, at the sludge recirculation level ...
• any sensors making it possible to monitor a parameter of particular interest in the wastewater treatment plant considered not mentioned above may be added.
• the present invention proposes to implement an algorithm for the interpretation of the data, making it possible to calculate what is the probability that the sensors give the value that they display. If this probability is high, it is considered that there are no anomalies, if this probability is low the algorithm detects an anomaly.
• a probability law is calculated for all the sensors
• This second probability being very low, it is indicative of an anomaly.
• This second probability being very low, it is indicative of an anomaly.
• X j can all be measurements made in the station (for example among the examples of sensors listed above).
• the algorithm has taken into account the dependence of the variable x 1 on the variable x 2 . Having a high O 2 concentration can be explained by the low concentration of pollutants at the inlet. Therefore, no anomalies are detected.
• the algorithm shows very low probability values which are very easily explained in the following periods:
• the algorithm shows much lower probability values than in the training period. These low probabilities can be explained by a change of station operator.
• the present invention therefore relates to a method of operating a water treatment installation, which comprises a phase of detecting anomalies in the operation of the station, characterized in that the phase of detecting anomalies comprises the setting.
• the data acquisition and processing system is also able to communicate as follows: - it is able to communicate with a dematerialized / hosted IT system (“cloud”);
• the server is programmed to receive the data, store them in databases, convert this data into a format suitable for viewing, and process this data according to recommendations;
• a digital medium such as for example a tablet, a telephone, a computer.

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• 2019
  • 2019-03-22 EP EP19305357.6A patent/EP3712735A1/de not_active Withdrawn
• 2020
  • 2020-02-12 AU AU2020246873A patent/AU2020246873A1/en active Pending
  • 2020-02-12 CA CA3132906A patent/CA3132906A1/en active Pending
  • 2020-02-12 US US17/441,633 patent/US20220153618A1/en active Pending
  • 2020-02-12 WO PCT/EP2020/053650 patent/WO2020193000A1/fr active Application Filing
  • 2020-02-12 JP JP2021556796A patent/JP2022526143A/ja active Pending
  • 2020-02-12 CN CN202080021809.6A patent/CN113574485A/zh active Pending
  • 2020-02-12 EP EP20703280.6A patent/EP3942374A1/de active Pending

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