Dynamic method for energy optimization associated with the cleaning of activated carbon

Abstract

The invention relates to a method for energy optimization of a cleaning process for activated carbon contained in at least one exhaust air treatment tank of an industrial site, the method comprising: modelling (100), for a predefined period of time, an initial use profile of the activated carbon, including at least: the triggering times, the number and the duration of the filtering, washing and drying phases, as a function of a flow rate estimation or the volume of exhaust air; the energy consumption associated with each phase during the use cycle; the quality of the activated carbon after each phase; applying (200) the use profile; dynamically optimizing (300) the energy consumption of the treatment cycles throughout the predefined period of time, comprising: a / estimating in real time (301) the quality of the activated carbon after each phase; b / identifying the origin (302) of an available energy source that meets a key criterion; c / adjusting (303) the triggering times and / or the number and / or the duration of each of the phases, as a function of the origin identified in step b / ; d / establishing an adjusted profile (304); e / performing steps 2 / to 3 / until the end of the predefined period.

Description

[0001] DESCRIPTION

[0002] Dynamic energy optimization process related to activated carbon cleaning

[0003] technical field

[0004] The invention relates to a dynamic method for optimizing energy consumption related to cleaning activated carbon contained in tanks.

[0005] State of the art

[0006] Activated carbon is widely used for the purification of contaminated water and air, due to its excellent adsorption properties for various inorganic, organic, gaseous, or any other substances dissolved or dispersed in liquids.

[0007] In industrial settings, regulations mandate the treatment of polluted air before it is released to the outside. Various treatment techniques can be implemented, including activated carbon filtration. For example, as illustrated in Figure 1, polluted air Av passes successively through activated carbon tanks Cl, C2. During this process, the activated carbon in the tanks adsorbs polluting molecules, thus reducing the pollutant content in the treated air At resulting from this filtration.

[0008] However, when treatment continues for a long period or involves a large volume of stale air, the activated carbon becomes saturated with adsorbed materials, causing it to lose its adsorption properties and rendering it inactive. Regenerating activated carbon, such as thermal regeneration, requires heating it to very high temperatures, often exceeding 600°C, to desorb the impurities.

[0009] To ensure their effectiveness and delay their inactivation and therefore their regeneration, the activated carbon is generally cleaned regularly. This treatment consists of washing the activated carbon in the tanks with steam and drying it with hot air. This cleaning process uses more moderate temperatures and allows for the desorption of light contaminants such as volatile organic compounds and the removal of accumulated moisture.

[0010] However, the production of water vapor and hot drying air results in significant energy consumption, and periodic and systematic programming of cleaning cycles may not be suitable to ensure optimal treatment of stale air.

[0011] Description of the invention

[0012] The invention aims to provide a solution to optimize the energy performance associated with each activated carbon cleaning cycle.

[0013] The invention notably proposes a solution for optimizing energy consumption related to the steam cleaning process using activated carbon contained in a tank or series of tanks, intended for treating polluted air in an industrial site. The invention proposes a dynamic method for optimizing energy consumption.

[0014] The invention relates to a method for optimizing the energy efficiency of cleaning activated carbon contained in at least one waste air treatment tank at an industrial site. The method comprises:

[0015] 1 / a modeling phase over a predefined period of time to determine an initial profile of activated carbon usage in the tank,

[0016] - the period of time extending over several treatment cycles including at least one phase of filtration of stale air using activated carbon, and at least one phase of cleaning the activated carbon consisting of a steam washing phase followed by a hot air drying phase;

[0017] - the initial usage profile including at least:

[0018] . the times of phase activation, the number and duration of each of the filtration, washing and drying phases, based on an estimate of the flow rate or the volume of stale air to be treated for each filtration phase; . the energy consumption (electrical and thermal) related to each washing phase and each drying phase during the usage cycle;

[0019] . the quality of the activated carbon or the lifespan of the activated carbon after each filtration phase and / or after each cleaning phase;

[0020] 2 / an application phase of the usage profile;

[0021] 3 / a phase of dynamic optimization of the energy consumption of the processing cycles throughout the said predefined time period, comprising the following steps:

[0022] a / real-time estimation at least of the quality of the activated carbon after each filtration phase and / or after each cleaning phase;

[0023] b / identification of the origin of an available energy source meeting at least one key criterion (e.g. cost) to generate water vapor and / or drying air;

[0024] c / adjusting at least the triggering times and / or the number and / or duration of each of the filtration and / or washing and drying phases, according to the origin identified in step b /

[0025] d / establishment of an adjusted usage profile based on these adjustments; and e / implementation of steps 2 / to 3 / until the end of said predefined time period.

[0026] In practice, step c / of adjustment may also include:

[0027] - adjusting the flow rate of stale air to be injected into the tank, depending on the quality of the activated carbon contained in the tank;

[0028] - the triggering times and the duration of the washing and drying phases of the activated carbon, adjusted according to the origin identified in step b / and a predefined key criterion associated with said origin (for example, cost).

[0029] For example, the activated carbon washing phases can be adjusted depending on whether the available usable energy for this washing phase is energy from recovery or is generated at a cost.

[0030] Advantageously, the condensate generated after the steam washing phases can be recovered and used to preheat water or other processes within the industrial site. This solution maximizes energy use, reduces natural resource consumption, and minimizes the need to treat the condensate as waste.

[0031] For example, the drying phases of activated carbon can be adjusted depending on whether the available energy usable for this drying phase is free energy or one that generates costs.

[0032] The quality or lifespan of activated carbon corresponds to its remaining adsorption capacity. The quality or lifespan of activated carbon can be estimated by measuring: the flow rate of polluted air passing through the activated carbon at each filtration stage; and the air quality exiting the tank after each filtration stage.

[0033] For example, different sensors can be used to perform this estimation. It is also possible to rely on predictive modeling of the lifespan of activated carbon based on historical data.

[0034] According to one embodiment, the dynamic optimization phase may further include:

[0035] - in step a:

[0036] . real-time measurement of pollutant concentrations in polluted air; . real-time acquisition of meteorological data; and - in step c:

[0037] . adjusting the flow rate of stale air to be injected into the tank according to the data collected in step a / to maximize the efficiency of the activated carbon while reducing the energy consumption of the fans used for injecting stale air.

[0038] In other words, the solution also incorporates dynamic management of the exhaust air flow rate, which reduces electricity consumption by preventing the ventilation system from running unnecessarily at full capacity. This dynamic regulation also ensures compliance with regulatory emission thresholds while minimizing the load on the activated carbon tanks. In one variant, step a / further includes qualitative data entered by an operator relating to human observations of odor nuisances in the environment outside the industrial site, and step c / further includes taking this qualitative data into account when adjusting the exhaust air flow rate to be injected. This qualitative data can be expressed as a weighting, score, etc.This variant allows for better adaptation of filtration according to perceived nuisances and the risks of pollutant dispersion depending on climatic conditions, which is essential for industries located near residential areas.

[0039] Advantageously, step c / of adjustment takes into account anomalies that may occur throughout the predefined time period. Thus, the dynamic optimization phase 3 / can further include:

[0040] - real-time anomaly detection, an anomaly corresponding to:

[0041] . a drift from the nominal conditions of the hot drying air entering the activated carbon tank, for example a drift in the temperature of the hot air, its humidity level, etc.; and / or

[0042] . a failure of pollutant capture by the activated carbon, in order to anticipate the end of the life of the activated carbon.

[0043] The detection of deviations from the nominal conditions of the hot drying air entering the activated carbon tank can be achieved through measurements using temperature and humidity sensors, and a comparison of these measurements with predefined threshold values. These threshold values ​​can be defined based on historical data or industry standards.

[0044] The detection of a pollutant capture failure by activated carbon can be achieved by measuring the level of pollutant (e.g., desorbed VOCs) recovered at each treatment stage. This measurement can be obtained using dedicated sensors and compared to a predefined threshold value. Similarly, these threshold values ​​can be defined based on historical data or industry standards. When at least one anomaly is detected during the anomaly detection phase, the adjustment step (c) also takes the detected anomalies into account and may further include adjusting the flow rate of exhaust air injected into the activated carbon tank.

[0045] In other words, if an anomaly is detected, the process implements corrective actions such as:

[0046] - adjusting the flow rate of stale air passing through the activated carbon; and / or

[0047] - adjusting the timing of the wash cycles and the required wash duration; and / or

[0048] - adjusting the drying phase and the required drying time.

[0049] Thus, the adjustment of the flow rate of stale air to be injected into the tank takes into account the remaining capacity of the activated carbon to adsorb pollutants.

[0050] Thus, the adjustment of the washing and drying phases is carried out so that the hot drying air applied to each cleaning phase meets the predefined nominal conditions, and / or is carried out taking into account the quality of the activated carbon contained in the tank.

[0051] Indeed, activated carbon generally exhibits a higher adsorption capacity at the beginning of its lifespan, with this capacity decreasing towards the end of its life. The useful lifespan of activated carbon before thermal reactivation typically depends on the amount of pollutant to be captured. Therefore, the washing and drying phases do not need to be scheduled periodically, and their number, timing, and duration can be adjusted according to the quality of the activated carbon over time.

[0052] Adjusting the usage profile can also be a function of:

[0053] - the flow rate of water vapor and / or the flow rate of hot drying air available over time, and / or

[0054] - the source of the energy used to generate the steam and / or hot air, and the value of the associated key criterion. Advantageously, the optimization phase 3 / also includes a calculation of the energy performance of each treatment cycle.

[0055] Advantageously, the key criterion can be chosen from: the financial cost induced by the production of steam and hot drying air, the consumption in kilowatt-hours for the production of steam and hot drying air, the CO2 emission rate by all the means implemented for the production of the energy used in each cleaning cycle.

[0056] According to one embodiment, the stale air is filtered alternately in a first activated carbon tank, and in a second activated carbon tank when the first tank is in the cleaning phase, the optimization process being applied to both tanks.

[0057] The invention also relates to a management module implementing the optimization process described above.

[0058] Brief description of the figures

[0059] Other features and advantages of the invention will become clear from the following description, which is given by way of example only and is not exhaustive, with reference to the accompanying figures, in which:

[0060] Figure 1 is a schematic representation of the treatment of stale air by successive passage through two activated carbon tanks.

[0061] Figure 2 is a schematic representation of an industrial site integrating a management module implementing the optimization process of the invention according to one embodiment.

[0062] Figure 3 is a simplified flowchart illustrating the main steps of the dynamic optimization process according to one embodiment.

[0063] Figure 4 is a simplified flowchart illustrating the main steps of the dynamic optimization phase according to one embodiment. Detailed description of the embodiments

[0064] The intelligent management module 20, according to an embodiment of the invention and implemented in part of an installation at an industrial site, is illustrated in Figure 2, for the treatment of stale air Av. The industrial site includes, in particular:

[0065] - a management module 20 implementing the dynamic optimization process according to an embodiment of the invention; this management module is notably coupled to:

[0066] . means for generating and recovering thermal and / or electrical energy; . means for generating drying air As for drying activated carbon, for example a hot coil 10;

[0067] . means for generating steam 11, 12, these means may include means for recovering condensate from the cleaning of activated carbon to carry out preheating

[0068] . activated carbon tanks, and in particular a first activated carbon tank 31 and a second activated carbon tank 32;

[0069] . an air handling system such as a ventilation system;

[0070] . a distribution network including pipes and valve systems, to convey stale air, drying air and water vapor to the tanks, and to expel treated air and drying air to the outside;

[0071] . various sensors or analyzers for flow, temperature, humidity etc. which are not shown.

[0072] The stale air (Av) is then conveyed to the activated carbon tanks for filtration, and the treated air (Avr) is expelled outside. For each tank, depending on the volume of stale air treated (Av), the activated carbon must be cleaned regularly to ensure its effectiveness and delay its deactivation. This cleaning process, however, results in significant energy consumption.

[0073] Thus, to optimize this energy consumption, the dynamic optimization implemented by management module 20, according to a specific embodiment, is applied to each of the tanks. The tanks are used alternately: while the first tank is being cleaned, the second tank is used to filter the stale air, and vice versa.

[0074] This dual-tank solution offers operational flexibility as it allows for continuous operation for the treatment of stale air.

[0075] The dynamic optimization process includes, in particular, a modelling phase 100 over a predefined period of time to determine an initial profile of activated carbon usage in the tank.

[0076] In practice, the time period extends over several treatment cycles, each treatment cycle comprising one or more successive phases of filtration of stale air using activated carbon, followed by a phase of cleaning the activated carbon.

[0077] The cleaning phase consists of a steam washing phase followed by a hot air drying phase.

[0078] The initial usage profile includes, in particular, the following information:

[0079] . an estimate of the flow rate or volume of stale air to be treated for each filtration phase over the predefined time period;

[0080] . the number of filtration, washing and drying phases, the duration of each of the filtration, washing and drying phases, and the trigger times of each of the filtration, washing and drying phases, depending on the estimate of the flow rate or volume of stale air to be treated for each filtration phase;

[0081] . an identification of the thermal and / or electrical energy sources used to generate the steam and drying air required for each cleaning phase; . the energy consumption (electrical and thermal) related to each filtration, washing and drying phase during the predefined period;

[0082] The estimation of the quality of the activated carbon or the lifespan of the activated carbon after each filtration phase and / or after each cleaning phase. The determination of the initial usage profile can also be based on historical data resulting from previous processes.

[0083] Once this initial modeling is complete, the system implements a 200 application phase of the usage profile. The system also performs a 300 dynamic optimization phase of energy consumption to adjust the usage profile to be applied, and this continues until the end of the predefined time period.

[0084] The 300 optimization phase includes, in particular, the following steps:

[0085] a / real-time estimation of at least 301 of the quality of the activated carbon after each filtration phase and / or after each cleaning phase;

[0086] b / identification of the origin 302 of an available energy source meeting at least one key criterion (e.g. cost) to generate water vapor and / or drying air;

[0087] c / adjustment 303 at least of the triggering times and / or the number and / or duration of each of the filtration and / or washing and drying phases, depending on the origin identified in step b /

[0088] d / establishment of an adjusted usage profile 304 based on these adjustments; e / application 200 of the adjusted usage profile and implementation of steps a / to e / until the end of the predefined time period.

[0089] Depending on the application, the 300 optimization phase may also include determining additional information, such as:

[0090] . real-time measurement of pollutant concentration in stale air; . real-time acquisition of meteorological data; and . the water vapor flow rate and / or the available hot drying air flow rate over time;

[0091] . the determination of the source of the energy used to generate the water vapor and / or hot air, and the value of the key criterion associated with this source;

[0092] . real-time detection of anomalies that may occur throughout the process, such as a drift in the nominal conditions of the hot drying air entering the activated carbon tank, for example a drift in the temperature of the hot air, humidity levels, etc., or a failure to capture pollutants by the activated carbon, in order to anticipate the end of the life of the activated carbon;

[0093] . the acquisition of meteorological data;

[0094] . the determination of a weighting based on qualitative data entered by the operator, the qualitative data being derived, for example, from human observations concerning olfactory nuisances.

[0095] Depending on the application, the adjustment step c / can be carried out taking into account all or part of this additional information, and may also include: . adjusting the flow rate of stale air to be injected into the tank, depending on the quality of the activated carbon contained in the tank;

[0096] . the adjustment of the triggering times and the duration of the washing and drying phases of the activated carbon, depending on the source of the energy and a predefined key criterion.

[0097] The key criterion can be chosen from: the financial cost induced by the production of steam and hot drying air, the consumption in kilowatt-hours for the production of steam and hot drying air, the CO2 emission rate by all the means implemented for the production of the energy used in each cleaning cycle.

[0098] Examples of implementation are given below for specific applications or situations.

[0099] Context 1: Animal feed production plant

[0100] The factory processes 50,000 m 3 of air per day to reduce VOC, NH3 and H2S emissions, in accordance with strict regulatory limits. Located near a residential area, it must minimize odor nuisances while optimizing energy costs and extending the lifespan of the activated carbon. The implementation of dynamic energy optimization can be carried out as follows:

[0101] 1 / Modeling phase and initial profile:

[0102] The initial usage profile is established over a predefined period of time, for example 6 months, covering several phases of filtration, washing and drying.

[0103] The number, timing, and duration of the filtration, washing, and drying phases are modeled based on historical data and regulatory thresholds. For example, the filtration phases are determined to maintain emissions within the following regulatory thresholds:

[0104] VOCs < 50 mg / m³ 3 ,

[0105] NH3 < 30 ppm,

[0106] H2S < 5 ppm.

[0107] The initial profile may include regulating the flow rate of exhaust air injected into the tank during each filtration phase, using the ventilation system, while meeting the selected key criterion. For example, the regulation of the exhaust air flow rate may also take into account peak and off-peak activity periods. For instance, during periods of low pollution (e.g., at night or off-peak activity), the control module 20 can be coupled with an automatic bypass system 40 to reduce the airflow passing through the activated carbon, extending its lifespan and reducing the fans' power consumption by 15%.

[0108] The energy consumption of the equipment used to carry out the filtration, washing and drying phases, such as fans, steam generator, hot air drying generator, etc., is also modeled.

[0109] The key criterion selected to be taken into account for energy consumption is cost. 2 / Application and dynamic optimization phase:

[0110] The initial profile is applied and real-time measurements or estimation of emissions can be carried out using electrochemical sensors to measure the concentrations of VOCs, NH3, and H2S.

[0111] The system also detects any deviations from the nominal drying air conditions in real time. This detection is achieved using sensors that monitor the temperature and humidity of the hot air used to dry the activated carbon. For example, if the humidity falls below a defined threshold, the system reduces the drying time to prevent excessive energy consumption.

[0112] Detection of pollutant capture failures by activated carbon is also performed. For example, an increase in desorbed VOCs after a washing phase indicates a decrease in the activated carbon's effectiveness. The management module can then adjust the frequency of the washing phases to prevent premature saturation.

[0113] The management module can also take into account current or forecast weather conditions. For example, in the event of strong winds, the airflow can be temporarily increased to disperse pollutants more effectively in the event of an emergency release, limiting the impact on local residents.

[0114] The identification of energy sources for generating steam and drying air is also carried out to meet the key criterion. For example, if free or low-cost energy (e.g., energy consumed during off-peak hours) is available, the management module can adjust the drying phases to prioritize the use of this free or low-cost energy. The water used to generate the steam necessary for washing the activated carbon can be preheated using condensate recovered during the washing phases. The recovery of this condensate can be on the order of 200 liters per washing phase.

[0115] These recovered condensates can also be used to preheat equipment cleaning water, enabling circular economy savings by reducing the plant's hot water consumption by 10%.

[0116] Thus, the optimization process makes it possible to reduce energy costs and in particular a daily saving of 100kWh per day, i.e. an annual saving of 3,650 kWh, via the regulation of the air flow, but also a 10% reduction in hot water consumption thanks to the recovery of condensate.

[0117] Optimizing washing and drying cycles according to the actual state of the activated carbon helps to extend the lifespan of the activated carbon.

[0118] Context 2: Rendering site

[0119] A rendering plant processes 100 tonnes of animal waste per day, generating emissions of NH3, H2S, and other VOCs. The air handling system manages a flow of 70,000 m³ 3 / day. Due to the nature of the operations and their potential for odor nuisance, strict control of emissions is required, as well as efficient energy management to keep operating costs at an acceptable level.

[0120] Due to the large volume of air to be treated, two activated carbon tanks operating alternately are used. One tank is in operation while the other is in a backwash cycle, ensuring continuous treatment. Each tank contains approximately 3 tons of activated carbon and must be backwashed every 30 days, depending on the saturation levels. 1 / Modeling phase and initial profile:

[0121] The initial usage profile is established over a predefined period of time, for example 6 months, covering several phases of filtration, washing and drying.

[0122] The number, timing of activation and duration of filtration, washing and drying phases for each tank are modeled based on historical data and regulatory thresholds.

[0123] The key criterion selected to be taken into account for energy consumption is cost.

[0124] As with the previous scenario, the initial profile can include regulating the flow rate of exhaust air injected into the tank during each filtration phase, using the ventilation system, while still meeting the selected key criterion. For example, the exhaust air flow regulation can also take into account peak and off-peak activity periods. For instance, during periods of low pollution (e.g., at night or off-peak activity), the control module 20 can be coupled with an automatic bypass system 40 to reduce the airflow passing through the activated carbon, extending its lifespan and reducing the fans' power consumption by 15%.

[0125] The energy consumption of the equipment used to carry out the filtration, washing and drying phases, such as fans, steam generator, hot air drying generator, etc., is also modeled.

[0126] 2 / Application and dynamic optimization phase:

[0127] The initial profile is applied and real-time measurements or estimates of emissions can be made using electrochemical sensors to measure the concentrations of VOCs, NH3, and H2S. Just as in the previous context, the management module makes real-time measurements and estimates of the filtration and / or washing and drying process.

[0128] Detection can be achieved using sensors that monitor the temperature and humidity of the hot air used to dry the activated carbon. For example, if the humidity is below a defined threshold, the system reduces the drying time to prevent excessive energy consumption.

[0129] Detection of pollutant capture failure by activated carbon is also possible. For example, an increase in desorbed VOCs after a washing phase indicates a decrease in the activated carbon's effectiveness. The management module can then adjust the frequency of the washing phases to prevent premature saturation.

[0130] The management module can also take into account current or forecast weather conditions. For example, in the event of strong winds, the airflow can be temporarily increased to disperse pollutants more effectively in the event of an emergency release, limiting the impact on local residents.

[0131] The identification of energy sources for generating steam and drying air is also carried out to meet the key criterion. For example, if free or low-cost energy (e.g., consumed during off-peak hours) is available, the management module can adjust the drying phases to prioritize the use of this free or low-cost energy.

[0132] The water used for generating steam needed for washing activated carbon can be preheated using condensate from the two tanks at the end of the washing phases.

[0133] Specifically, in the case of rendering plants, each washing cycle uses approximately 1,000 liters of steam at 140°C and generates 300 liters of condensate laden with pollutants. This condensate can be recovered and used to preheat water to 60°C in the cooking process of animal by-products, resulting in energy savings of approximately 10,000 kWh per year.

[0134] The process reduces the frequency of washing by 10%, saving approximately 100 wash cycles over a 10-year period, representing a saving of 10,000 m³ 3 of steam and a reduction in energy costs of €15,000 over the same period.

[0135] Context 3: Chemical plant producing solvents

[0136] A chemical plant specializing in the production of solvents (acetone, benzene) needs to process 120,000 m³ 3 of air per day to limit VOC emissions in accordance with strict local regulations (< 20 mg / m³)3 ). This regulatory constraint is compounded by the need to control energy costs in a context of high steam demand for activated carbon tank washing cycles.

[0137] 1 / Modeling phase and initial profile:

[0138] The initial usage profile is established over a predefined period of time, for example 12 months, covering several phases of filtration, washing and drying.

[0139] The number, timing of activation and duration of filtration, washing and drying phases for each tank are modeled based on historical data and regulatory thresholds.

[0140] The initial profile may include regulating the flow rate of exhaust air injected into the tank during each filtration phase, using the ventilation system, while meeting the selected key criterion. For example, the regulation of the exhaust air flow rate may also take into account peak and off-peak activity periods. For instance, during periods of low pollution (e.g., at night or off-peak activity), the management module 20 can be coupled with an automatic bypass system 40 to reduce the airflow passing through the activated carbon, extending its lifespan and reducing the fans' power consumption by 15%.

[0141] The energy consumption of the equipment used to carry out the filtration, washing and drying phases, such as fans, steam generator, hot air drying generator, etc., is also modeled.

[0142] The key criterion selected to be taken into account for energy consumption is cost.

[0143] 2 / Application and dynamic optimization phase:

[0144] The initial profile is applied and real-time measurements or estimation of emissions can be carried out using electrochemical sensors to measure VOC concentrations.

[0145] The system also detects any deviations from the nominal drying air conditions in real time. This detection is achieved using sensors that monitor the temperature and humidity of the hot air used to dry the activated carbon. For example, if the humidity falls below a defined threshold, the system reduces the drying time to prevent excessive energy consumption.

[0146] Detection of pollutant capture failures by activated carbon is also performed. For example, an increase in desorbed VOCs after a washing phase indicates a decrease in the activated carbon's effectiveness. The management module can then adjust the frequency of the washing phases to prevent premature saturation.

[0147] The management module can also take into account current or forecast weather conditions. For example, in the event of strong winds, the airflow can be temporarily increased to disperse pollutants more effectively during an emergency release, thus limiting the impact on local residents. Energy sources for generating steam and drying air are also identified to meet the key criterion. For example, if free or low-cost energy (e.g., energy consumed during off-peak hours) is available, the management module can adjust the drying phases to prioritize the use of this free or low-cost energy.

[0148] The water used to generate the steam necessary for washing the activated carbon can be preheated using condensate recovered during the washing phases. The recovery of this condensate can be on the order of 200 liters per washing phase.

[0149] For example, the management module can adjust the washing and drying phases according to production phases and peak VOC emissions. Washing phases can be reduced by 20%, resulting in annual savings of 15,000 m³ 3 of steam.

[0150] The recovered condensate (500 litres of condensate produced in each wash cycle) can be used to heat boiler water, saving approximately 5,000 kWh of energy per year.

[0151] Dynamic energy optimization not only ensures regulatory compliance for VOC emissions, but also transforms a constraint into an opportunity. Adjusting filtration and cleaning phases, recovering energy from condensate, and adapting cycles to actual needs enables significant savings while enhancing operational sustainability.

Claims

DEMANDS 1. Energy optimization process for cleaning activated carbon contained in at least one waste air treatment tank at an industrial site, the process comprising: 1 / a modelling phase (100) over a predefined period of time to determine an initial profile of activated carbon usage in the tank, - the period of time extending over several treatment cycles including at least one phase of filtration of stale air using activated carbon, and at least one phase of cleaning the activated carbon consisting of a steam washing phase followed by a hot air drying phase; - the initial usage profile including at least: . the times of triggering the phases, the number and duration of each of the filtration, washing and drying phases, based on an estimate of the flow rate or the volume of stale air to be treated for each filtration phase; . the energy consumption associated with each washing phase and each drying phase during the usage cycle; . the quality of the activated carbon or the lifespan of the activated carbon after each filtration phase and / or after each cleaning phase; 2 / an application phase (200) of the usage profile; 3 / a dynamic optimization phase (300) of the energy consumption of the processing cycles throughout the said predefined time period, comprising the following steps: a / real-time estimation (301) at least of the quality of the activated carbon after each filtration phase and / or after each cleaning phase; b / identification of the origin (302) of an available energy source meeting at least one key criterion for generating water vapor and / or drying air; c / adjustment (303) at least of the triggering times and / or the number and / or duration of each of the filtration and / or washing and drying phases, according to the provenance identified in step b / d / establishment of an adjusted usage profile (304) on the basis of these adjustments; e / implementation of steps 2 / to 3 / until the end of said predefined time period.

2. An optimization method according to claim 1, wherein the adjustment step c further comprises: - adjusting the flow rate of stale air to be injected into the tank, depending on the quality of the activated carbon contained in the tank; - the triggering times and the duration of the washing and drying phases of the activated carbon, adjusted according to the origin identified in step b / and a predefined key criterion associated with said origin.

3. Optimization method according to claim 1 or 2, wherein the activated carbon washing phases are adjusted according to whether the available usable energy for this washing phase is energy from recovery or which is generated at a cost.

4. Optimization method according to any one of claims 1 to 3, wherein the condensates generated after the steam washing phases are recovered and used to preheat water.

5. Optimization method according to any one of claims 1 to 4, wherein the drying phases of activated carbon are adjusted according to whether the available energy usable for this drying phase is free energy or generates costs.

6. Optimization method according to any one of claims 1 to 5, wherein the quality or lifespan of the activated carbon corresponds to its remaining adsorption capacity, the quality or lifespan of the activated carbon is estimated by measurement: . the flow rate of stale air passing through the activated carbon at each filtration phase; and . the quality of the air exiting the tank after each filtration phase.

7. An optimization method according to any one of claims 1 to 6, wherein the dynamic optimization phase further comprises: - in step a: . real-time measurement of pollutant concentration in polluted air; . real-time acquisition of meteorological data; and - in step c: . adjusting the flow rate of stale air to be injected into the tank according to the data collected in step a / to maximize the efficiency of the activated carbon while reducing the energy consumption of the fans used for injecting stale air.

8. Optimization method according to any one of claims 1 to 7, wherein step a / further includes qualitative data entered by an operator relating to human observations of odor nuisances in the environment outside the industrial site, and step c / further includes taking into account this qualitative data in adjusting the flow rate of stale air to be injected.

9. An optimization method according to any one of claims 1 to 8, wherein: the dynamic optimization phase 3 / which also includes: - real-time anomaly detection, an anomaly corresponding to: . a drift in the nominal conditions of the hot drying air entering the activated carbon tank, for example a drift in the temperature of the hot air, its humidity level, etc.; and / or . a failure of pollutant capture by the activated carbon, in order to anticipate the end of the life of the activated carbon; and in which step c / of adjustment takes into account the anomalies detected and implements corrective actions including: - adjusting the flow rate of stale air passing through the activated carbon; and / or - adjusting the timing of the wash cycles and the required wash duration; and / or - adjusting the drying phase and the required drying time.

10. Optimization method according to any one of claims 1 to 9, wherein the stale air is filtered alternately in a first activated carbon tank, and in a second activated carbon tank when the first tank is in the cleaning phase, the optimization method being applied to both tanks.

11. Management module implementing the optimization process according to one of claims 1 to 10.

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