Internal circulation purification control system and method for volatile smell generated by industrial equipment

Abstract

The invention discloses an internal circulation purification control method for volatile smell generated by industrial equipment, and relates to the technical field of industrial waste gas treatment.The internal circulation purification control method comprises the steps that volatile gas is collected, gas control indexes are extracted, and the starting state of purification equipment is determined; analyzing the purification benefit ratio and judging the stability; judging whether the multi-equipment working condition analysis intervals are consistent or not, and if yes, constructing a response curve to recognize a preferred intervention interval; and constructing a time prediction model and a multi-device collaborative algorithm from historical data, predicting a time period of occurrence of the preferable intervention interval, and formulating a collaborative control strategy. The internal circulation purification control system for the volatile odor generated by the industrial equipment comprises a state determination module, a stability analysis module, an interval recognition module and a prediction control module. According to the invention, efficient purification of industrial volatile odor is realized, and the system stability and the energy-saving effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial waste gas treatment, and particularly to an internal circulation purification control system and method for volatile odors generated by industrial equipment. Background Art

[0002] During industrial production, volatile organic compounds (VOCs), as the main waste gas and odor pollutants, are characterized by easy volatilization and strong chemical activity, seriously affecting environmental quality and human health. The indoor TVOC concentration needs to be strictly controlled at ≤0.6 mg / m³, but the traditional purification methods have the following technical bottlenecks.

[0003] In industrial scenarios, multiple devices often experience synchronous fluctuations in operating conditions due to systematic factors such as centralized emissions and abnormal ventilation (e.g., the sudden increase in TVOC concentration caused by multiple printing presses starting up simultaneously). However, the existing control systems only support the independent operation of single devices, and do not establish an overlap calculation and coordination mechanism for the operating condition analysis intervals between devices. When pollution breaks out intensively, the full-power operation of a single device is likely to cause overload, while remote devices may have local concentration exceedance due to lagging response, resulting in an imbalance in the overall treatment capacity, and high cluster peak power consumption, posing a risk of power grid impact.

[0004] Therefore, the present invention provides an internal circulation purification control system and method for volatile odors generated by industrial equipment. Summary of the Invention

[0005] The purpose of the present invention is to provide an internal circulation purification control system and method for volatile odors generated by industrial equipment to solve at least one of the above-mentioned existing technical problems.

[0006] The internal circulation purification control method for volatile odors generated by industrial equipment includes the following steps: Collect the volatile gases generated by industrial equipment and extract gas control indicators, perform determination processing on the gas control indicators to determine the startup state of the purification equipment; Collect the average determination indicators of the purification equipment and perform curve analysis, construct a purification benefit group, perform stability analysis on the purification benefit group to obtain a benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; If they are inconsistent, extract the operating condition analysis interval, judge whether the operating condition analysis intervals of multiple devices are consistent. If they are consistent, construct a response curve of the average determination indicator and the purification benefit, and identify the preferred intervention interval; Obtain the time period corresponding to the preferred intervention interval from historical data, construct a time prediction model and a multi-device coordination algorithm, predict the time period when the preferred intervention interval appears, and formulate a coordinated control strategy.

[0007] As a further technical solution of the present invention: The determination method of the startup state of the purification equipment is: Taking the total volatile organic compound concentration as a control index, collect the total volatile organic compound concentrations at the inlet of the plasma module at multiple monitoring times within the monitoring period, and construct a control index group; Extract the characteristic indexes of the control index group and set the device start-stop determination conditions, and determine the start-stop state of the device based on the start-stop determination conditions.

[0008] As a further technical solution of the present invention: the method for obtaining the benefit stability value is: Collect the average determination indexes of the purification equipment, and establish a change curve of time and the average determination indexes in a two-dimensional coordinate system; Obtain the purification time interval of the purification equipment, intercept and analyze the change curve of time and the average determination indexes according to the purification time interval to obtain the purification benefit ratio; Perform fitting analysis on the purification benefit ratio to obtain the benefit stability value of the purification benefit group.

[0009] As a further technical solution of the present invention: the method for obtaining the purification benefit ratio is: Calculate the intercepted area of the change curve of time and the average determination indexes as the purification index quantity; Obtain the total energy consumption value of the purification equipment within the purification time interval, and perform a ratio process on the purification index quantity and the total energy consumption value to obtain the purification benefit ratio.

[0010] As a further technical solution of the present invention: the method for performing fitting analysis is: Combine the purification benefit ratios of multiple purification time intervals to construct a purification benefit group; divide the purification benefit group into multiple sub-intervals of different lengths, and calculate the rescaled range of the sub-intervals; Based on the rescaled range, use linear regression to fit the points to obtain the fitting line and the slope of the fitting line.

[0011] As a further technical solution of the present invention: the method for determining whether the working condition analysis intervals of multiple devices are consistent is: Obtain the working condition analysis intervals of multiple devices. If there is an overlapping part in the working condition analysis intervals of multiple devices, calculate the overlap degree of the working condition analysis intervals of any two devices; Based on the overlap degree, perform comparison and analysis to determine whether the working condition analysis intervals of multiple devices are consistent.

[0012] As a further technical solution of the present invention: the method for identifying the preferred intervention interval is: Calculate the ion benefit ratio of the ion energy consumption index curve, and perform segmentation processing on the curve based on the ion benefit ratio; Obtain the interval of device cooperation and perform boundary correction to identify the dynamic effective interval; Perform multi-dimensional constraint calibration on the dynamic effective interval to identify the preferred intervention interval.

[0013] As a further technical solution of the present invention: The method for performing multi-dimensional constraint calibration is as follows: In multi-dimensional constraint calibration, calculate the efficient segment boundary and the minimum width limit, and determine whether the dynamic effective interval meets the efficient segment boundary and the minimum width limit. If it meets, correct the overlapping scenario factors to determine the collaborative correction interval.

[0014] As a further technical solution of the present invention: The method for formulating a collaborative control strategy is as follows: Obtain the historical monitoring data of industrial gases and perform preprocessing to extract time features and gas features; Input the time features and gas features into the time prediction model to output the periodic time window and real-time probability warning. When the prediction results overlap, trigger the collaborative control strategy to realize the prediction of the occurrence time period of the priority intervention interval; Based on the predicted time period of the occurrence of the preferred intervention interval, formulate a multi-device collaborative control strategy.

[0015] The volatile odor internal circulation purification control system generated by industrial equipment includes the following modules: Status determination module: used to collect the volatile gases generated by industrial equipment and extract gas control indicators, perform determination processing on the gas control indicators, and determine the startup status of the purification equipment; Stability analysis module: based on the determined startup conditions, used to collect the average determination indicators of the purification equipment and perform curve analysis, construct a purification benefit group, perform stability analysis on the purification benefit group to obtain a benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; Interval identification module: if inconsistent, extract the working condition analysis interval, used to judge whether the working condition analysis intervals of multiple devices are consistent. If they are consistent, construct a response curve of the average determination indicator and the purification benefit, and identify the preferred intervention interval; Prediction control module: obtain the time period corresponding to the priority intervention interval from historical data, construct a time prediction model and a multi-device collaborative algorithm, predict the time period of the occurrence of the preferred intervention interval, and formulate a collaborative control strategy.

[0016] The beneficial effects of the present invention: 1. By setting a TVOC detection module in the plasma gas circulation purification equipment, the VOC molecular concentration is collected in real time and a control index group is constructed, and the start-stop determination conditions including the average determination index and the rate index are established, so as to realize the dynamic control of the start-stop state of the purification equipment, reduce the ineffective operation in the low-concentration stable scenario, reduce the loss caused by frequent start-stop of the equipment, and ensure timely intervention at the initial stage of the increase in pollution concentration.

[0017] 2. Based on the area of the concentration change curve within the purification time interval, the purification efficiency ratio is calculated based on the total energy consumption within the interval. The Hurst exponent is used to evaluate efficiency stability. The ion energy consumption index curve is divided into different energy efficiency segments, prioritizing operation within the high-efficiency segment. Through efficiency ratio constraints and forward rate filtering, inefficient edge data and concentration drop intervals are eliminated, which helps improve the energy efficiency of the purification equipment.

[0018] 3. By calculating the mean overlap of multiple device operating condition analysis intervals, we screen out groups of devices with consistent operating conditions. We then construct a response curve for average judgment indicators and purification benefits based on systemic factors such as concentrated emissions from industrial equipment and ventilation anomalies. We construct an initial interval based on the mean and standard deviation of the average judgment indicators for each device. After applying benefit ratio constraints, forward rate filtering, and multi-dimensional constraint calibration, we determine the optimal intervention interval. In multi-device collaborative scenarios, staggered startup and dynamic power allocation reduce cluster power consumption while minimizing overload on individual devices, improving the balance and reliability of the system's overall processing capabilities.

[0019] 4. A dual-model fusion strategy is used to construct a time prediction model, outputting periodic time windows and real-time probabilistic warnings to predict the occurrence of priority intervention intervals. Combined with a collaborative objective function, the voltage and air volume parameters of each device are dynamically adjusted to form control strategies such as step-by-step startup and load balancing. This provides a predictable, adjustable, and intelligent solution for industrial waste gas treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] FIG1 is a structural diagram of the purification equipment provided by the present invention; FIG2 is a flow chart of the internal circulation purification control method for volatile odors generated by industrial equipment provided by the present invention; FIG3 is a flow chart of a method for obtaining a preferred intervention interval provided by the present invention; FIG4 is a block diagram of the internal circulation purification control system for volatile odors generated by industrial equipment provided by the present invention. DETAILED DESCRIPTION

[0022] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention. Embodiment

[0023] As shown in Figure 1, the method for controlling the internal circulation purification of volatile odors generated by industrial equipment provided by the embodiment of the present invention specifically includes the following steps: Step 1: Collect the volatile gases generated by industrial equipment and extract gas control indicators, perform judgment processing on the gas control indicators, and determine the start-up state of the purification equipment; Among them, the method for collecting the volatile gases generated by industrial equipment and extracting the gas light control indicators is as follows: Install a gas detection probe in the plasma gas circulation purification equipment shown in Figure 1. A standard TVOC detection module is installed in the gas monitoring probe to collect the VOC molecular concentration in real time; It should be explained that VOC (Volatile Organic Compounds) is a type of organic compound that is easily volatilized from a solid or liquid state to a gaseous state under normal temperature and pressure. It has strong volatility and chemical activity and is one of the main components of industrial waste gas and odor pollution; TVOC (Total Volatile Organic Compounds) is the total concentration of all volatile organic compounds in the air and is used for environmental quality assessment; Among them, the indoor TVOC concentration limit ≤ 0.6mg / m³; Take the concentration of total volatile organic compounds (TVOC) as the control indicator, collect the concentration of total volatile organic compounds (TVOC) at the inlet of the plasma module at multiple monitoring times during the monitoring period, and construct a control indicator group; Extract the characteristic indicators of the control indicator group and set the equipment start-stop judgment conditions, and determine the start-stop state of the equipment based on the start-stop judgment conditions; Among them, the method for extracting the gas control indicators of the control indicator group is as follows: Perform a moving average process on the concentration of total volatile organic compounds (TVOC) in the control indicator group to obtain an average judgment indicator; Calculate the concentration change rate of the concentration of total volatile organic compounds at two adjacent monitoring times in the control indicator group to obtain the concentration change rate; Obtain the concentration volatilization rate at multiple monitoring moments within the detection period, and perform regression fitting analysis to judge the change trend of the concentration volatilization rate; If the concentration change rate is positive, it is used as a positive rate index, otherwise it is used as a negative rate index; it should be noted that two adjacent monitoring moments refer to the monitoring moments that are closely connected in the time dimension; Use the average judgment index, positive rate index and negative rate index as the gas control indexes of the control index group; Establish the start-stop judgment conditions of the equipment. If the equipment meets any one of the start-stop judgment conditions, change the start-stop state of the purification equipment; Preferably, Judgment Condition 1: If the average judgment index of the equipment is higher than the upper control value, start the purification equipment; Judgment Condition 2: If it meets the positive rate index and the positive rate is higher than the preset rate control value, start the purification equipment; Judgment Condition 3: If the average judgment index is lower than the lower control value and is in the negative rate index, temporarily turn off the purification equipment; Exemplarily, the upper control value is 0.6mg / m³, and the lower control value is 0.3mg / m³; It should be noted that the gas passes through the low-temperature plasma module shown in Figure 1. Under the action of an externally applied electric field, a large number of energy-carrying electrons generated by dielectric discharge in the low-temperature plasma module bombard VOC molecules, causing them to ionize, dissociate and excite, triggering a series of physical and chemical reactions, so that organic waste gases with large molecular masses are degraded into simple substances with small relative molecular masses, or toxic and harmful substances are converted into non-toxic, harmless or less harmful substances, so as to degrade and remove VOCs. Then, the treated air is evenly discharged from the top to the inside of the equipment through the high-efficiency filter layer and continuously circulated; the gas internal circulation method can reuse the air in the workshop and avoid the air-conditioning energy consumption caused by supplementing fresh air due to the temperature difference between inside and outside the workshop; the gas internal circulation can repeatedly treat the VOCs in the air, gradually reduce the concentration, achieve decomposition while volatilizing, and reduce the problem of excessive pressure caused by instantaneous decomposition due to accumulation. Embodiment

[0024] As shown in Figure 2, the method for controlling the internal circulation purification of volatile odors generated by industrial equipment further includes the following steps: Step 2: Based on the determined start-up conditions, collect the average judgment index of the purification equipment and perform curve analysis, construct a purification benefit group, perform stability analysis on the purification benefit group to obtain a benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; Collect the average judgment index of the purification equipment in real time, and establish a change curve of time and the average judgment index in a two-dimensional coordinate system; Obtain the time interval of the start and stop states of the purification equipment as the purification time interval, and intercept the change curve of time and the average judgment index according to the purification time interval; Calculate the intercepted area of the change curve of time and the average judgment index as the purification index quantity; obtain the total energy consumption value of the purification equipment within the purification time interval, and perform a ratio process on the purification index quantity and the total energy consumption value to obtain the purification benefit ratio; Obtain the purification benefit ratios of multiple purification time intervals of the purification equipment and conduct a stability analysis to determine whether the purification benefit ratios within multiple purification time intervals are stable; Among them, the method for determining whether the purification benefit ratios within multiple purification time intervals are stable is as follows: Combine the purification benefit ratios of multiple purification time intervals to construct a purification benefit group Xt; Among them , x t represents the purification benefit ratio of each of the multiple purification time intervals in the purification benefit group, and n is the total number of purification benefit ratios; Through the formula: Calculate the cumulative deviation X(t) from the starting purification time interval i to the current purification time interval t of each in the purification benefit group; Among them is the mean value of the purification benefit ratio; Divide the purification benefit group into multiple sub-intervals of different lengths m, and calculate the range R(m) and standard deviation S(m) of the purification benefit ratio within the sub-intervals of length m; Perform a ratio process on the range and standard deviation of each sub-interval to obtain the rescaled range. Calculate the corresponding rescaled ranges for sub-intervals of different lengths m respectively; Use log(m) as the abscissa and log(R(m) / S(m)) as the ordinate, plot the points of multiple different lengths m in the coordinate system, use the linear regression method to fit the points to obtain the fitting line and the slope of the fitting line, and use the slope of the fitting line as the Hurst index; Use the Hurst index as the benefit stability value of the purification benefit group, and compare the benefit stability value with the preset benefit stability threshold; If the benefit stability value is close to the benefit stability threshold, it is considered that the purification benefit ratios within multiple purification time intervals are stable, otherwise they are unstable; Those skilled in the art can understand that the benefit stability value (Hurst index) is an important index for evaluating the stability of the purification system; When 0.45 ≤ H ≤ 0.55 without significant deviation, the benefit ratio fluctuation is close to a random walk, the equipment performance is stable and there is no obvious trend, which belongs to normal random fluctuation and no special intervention is required; 0.55 < H < 0.65 or 0.35 < H < 0.45 represents a mild deviation. H > 0.5 indicates that the benefit ratio has weak persistence (such as a slight upward trend brought about by equipment optimization), and H < 0.5 has weak anti-persistence (such as a gradual decrease in efficiency caused by slight wear of the filter element), and the maintenance cycle or algorithm optimization needs to be concerned; 0.65 ≤ H ≤ 0.85 or 0.15 ≤ H ≤ 0.35 represents a moderate deviation. H > 0.5 shows significant and continuous high efficiency (the authenticity of the data needs to be checked), and H < 0.5 shows significant anti-continuous low efficiency (indicating the precursor of filter element aging or failure), and a maintenance warning needs to be triggered and targeted maintenance needs to be carried out; H > 0.85 or H < 0.15 represents a high deviation. H > 0.5 may be due to data anomalies (such as misjudgment by sensors), and H < 0.5 is mostly due to sudden equipment failures or extreme pollution impacts, and immediate shutdown for inspection is required to avoid accident risks; If the purification benefit ratios in multiple purification time intervals are unstable, extract the sub-interval with the largest range of purification benefit ratios in the purification benefit group, and obtain the purification time with the lowest purification benefit ratio in the sub-interval with the largest range as the working condition analysis interval.

[0025] It should be explained that the role of identifying the working condition analysis interval is as follows: Role 1: Locate system-type pollution factors and trigger the multi-device collaboration mechanism. The working condition analysis interval is the key period extracted when the purification benefit ratio is unstable, and is used to identify whether multiple devices have synchronous fluctuations in working conditions due to common reasons. By calculating the overlap degree of the working condition analysis intervals of multiple devices, if the overlap mean is higher than the threshold, it is determined that there are system-type factors, and the multi-device collaborative control process is triggered to reduce the overload or response lag caused by the independent processing of single devices, and improve the overall response ability of the system to concentrated pollution; Role 2: Build the data basis for collaborative control and optimize the intervention strategy. The data of multiple devices within the working condition analysis interval are the core input for identifying the optimal intervention interval. By screening groups of devices with consistent working conditions, based on their concentration distribution characteristics (constructing the initial interval, and then through benefit ratio constraint, forward rate filtering and multi-dimensional calibration, the high-efficiency operation interval of the plasma module can be located. The control strategy changes from the empirical threshold of single devices to data-driven multi-devices, improving the reliability and energy-saving of the intervention interval.

[0026] The technical solution of this embodiment is: based on the determined start-up conditions, collect the average judgment indexes of the purification equipment and conduct curve analysis, construct the purification benefit group, conduct stability analysis on the purification benefit group to obtain the benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; by benefit ratio constraint and forward rate filtering, eliminate low-efficiency marginal data and concentration decline intervals, which is beneficial to improving the energy efficiency of the purification equipment. Embodiment

[0027] Such as Figure 2As shown, the method for controlling the internal circulation purification of volatile odors generated by industrial equipment further includes the following steps: Step 3: If they are inconsistent, extract the operating condition analysis interval. Determine whether the operating condition analysis intervals of multiple devices are consistent. If they are consistent, construct the response curve of the average determination index and the purification efficiency, and identify the preferred intervention interval. Preferably, multiple devices upload data to the server through a local wireless network. The server automatically records the start and stop time points, the purification time interval, and the change curve of the average determination index, and calculates the operating condition analysis interval. Among them, the method for determining whether the operating condition analysis intervals of multiple devices are consistent is as follows: Obtain the operating condition analysis intervals of multiple devices from the server. If there is an overlapping part in the operating condition analysis intervals of multiple devices, calculate the overlap degree of the operating condition analysis intervals of any two devices. Exemplarily, if the operating condition analysis intervals of two devices are A = [a1, a2] and B = [b1, b2] respectively, where a1 and a2 are the start times of the operating condition analysis interval, and b1 and b2 are the end times of the operating condition analysis interval. If A and B have an overlapping part, that is, a1 > b2 or b1 > a2, the length of the overlapping interval is 0, indicating that the operating condition analysis intervals of the two devices do not overlap. If there is an overlap, then the overlap length ; Obtain the overlap degree through the formula: ; Among them, min and max are the minimum and maximum value functions. min(a2, b2) represents selecting the smaller one from the two numbers a2 and b2, and max(a1, b1) represents selecting the larger one from the two numbers a1 and b1. Calculate the average overlap degree of all devices and compare it with the preset overlap degree threshold. If the average overlap degree is greater than or equal to the preset overlap degree threshold, the operating condition analysis intervals of the devices are consistent; otherwise, they are inconsistent. It should be explained that if the operating condition analysis intervals of the devices are consistent, there are systematic factors in the operating condition analysis interval, such as concentrated emissions of industrial equipment, abnormal workshop ventilation, or common equipment problems. If they are consistent, obtain the average energy consumption and the average determination index of the low-temperature plasma module within multiple monitoring cycles in the operating condition analysis interval, and construct the ion energy consumption index curve. It should be explained that the role of calculating the overlap degree is: ​Function 1: Identify the consistency of multi-device operating conditions and locate systematic pollution factors. The overlap degree is used to quantify the time coincidence degree of the multi-device operating condition analysis interval and is the core index for judging whether the equipment is driven by common factors. When the average overlap degree of all devices is higher than the preset threshold, it indicates that the start-stop times and concentration fluctuation trends of the multi-devices are highly synchronized, and there are systematic pollution incentives.

[0028] Function 2: Trigger collaborative control strategies and optimize the operating efficiency of multi-devices. Achieve load balance by dynamically adjusting voltage and air volume, and reduce the efficiency decline or life loss caused by single-device overload.

[0029] As Figure 3 shown, the method for identifying the preferred intervention interval is as follows: S1. Calculate the ion benefit ratio of the ion energy consumption index curve and perform segmented processing on the curve based on the ion benefit ratio; Through the formula: Obtain the ion benefit ratio of each time point t of the ion energy consumption index curve ; where C in (j) is the average judgment index of each point j of the ion energy consumption index curve, E mod (j) is the average energy consumption of each point j, and Q(j) is the air volume of each point j; is the environmental correction function, where T represents temperature and H represents humidity, which are determined by professional technicians in the field. E noi is the noise correction amount, and E mod (j); It should be explained that C in (j), C in (j), and Q(j) are all dimensionless before being substituted into the formula to calculate the ion benefit ratio of the ion energy consumption index curve; The environmental correction function is fitted based on experimental data. Under different temperature T and humidity H conditions, the purification equipment is tested, and the actual purification effect when the same pollutant concentration is input is recorded. By analyzing the mapping relationship between temperature and humidity changes and purification efficiency, mathematical models are constructed using methods such as regression analysis and neural networks to determine the specific expression of to correct the influence of temperature and humidity on the purification process and make the calculation of the ion benefit ratio more in line with the actual working conditions; The determination of the noise correction amount requires experimental statistics and interference analysis. During the operation of the equipment, the interference degree of equipment mechanical vibration, circuit background noise, and sensor inherent error on the calculation of the ion benefit ratio is statistically analyzed. Through multiple groups of control experiments, the calculation deviation between normal operation and the presence of interference is compared, and the average deviation is calculated to determine the noise correction amount to eliminate the influence of irrelevant factors on the evaluation of purification benefits and ensure the accuracy and reliability of the calculation results; Based on the ion energy consumption index curve, the ion benefit ratio divides the ion energy consumption index curve into a low-efficiency section, a medium-efficiency section, and a high-efficiency section; S2. Obtain the interval of equipment collaboration and perform boundary correction to identify the dynamic effective interval; Obtain the overlap degree of all equipment and calculate the mean value to obtain the global overlap mean value. Screen the equipment based on the global overlap mean value, and screen the equipment with an overlap degree higher than the global overlap mean value; construct an initial interval based on the mean value and standard deviation of the average determination index of the equipment with the global overlap mean value. Eliminate the low-efficiency marginal data through the benefit ratio constraint, and then filter out the decreasing invalid intervals through the forward rate index to form a dynamic effective interval; Exemplarily, calculate the global overlap mean value and screen the equipment: The overlap degrees of the operating condition intervals of equipment A - H are: 75%, 60%, 85%, 50%, 70%, 65%, 55%, 80% respectively; The global overlap mean value is 67.5%, and the equipment with an overlap degree ≥ 67.5% is screened out: A (75%), C (85%), G (80%), a total of 3 units; Construct an initial interval based on the average determination index: The average determination indexes of the 3 units of equipment are: 0.8 mg / m³, 1.0 mg / m³, 0.9 mg / m³ respectively, the mean value μ is 0.9 mg / m³, and the standard deviation σ is 0.1 mg / m³; Initial interval: [μ - 1.5, μ + 1.5] = [0.75, 1.05] (limited within 0.6 - 1.2 mg / m³ in the high-efficiency section); Eliminate low-efficiency data through the benefit ratio constraint: The peak value of the ion benefit ratio in the high-efficiency section is 3.0, and the set threshold is 2.7. Eliminate the marginal data with an ion benefit ratio < 2.7 in the high-efficiency section within the initial interval: For example, when the ion benefit ratio of equipment A is 2.6 at 0.75 - 0.8 mg / m³, the finally retained interval shrinks to [0.8, 1.05]; Filter out invalid intervals through the forward rate: Screen the initial interval with a forward rate higher than the preset rate control value; For example, when the average determination index of equipment C is at 1.0 - 1.05 mg / m³, the forward rate is 0.015, which is lower than the preset rate control value of 0.02 mg / m 3 *min, and the final dynamic effective interval is [0.8, 1.0]; S3. Perform multi-dimensional constraint calibration on the dynamic effective interval to identify the preferred intervention interval; In the multi-dimensional constraint calibration, calculate the high-efficiency section boundary and the minimum width limit, and judge whether the dynamic effective interval meets the high-efficiency section boundary and the minimum width limit. If it meets, perform the overlap scenario factor correction to determine the collaborative correction interval; Finally, through the intersection of the dynamic effective interval and the collaborative correction interval, the optimal intervention interval that takes into account the benefit ratio compliance rate, equipment characteristics and system collaboration is identified; For example, a multidimensional constraint calibration is performed on the dynamic effective interval [0.8, 1.0 mg / m³] obtained in S2; High-efficiency segment boundary and minimum width limit: High-efficiency segment boundary: 0.6-1.2mg / m³, then the dynamic effective range meets the requirements; Minimum width limit: The interval width is required to be ≥ 0.2mg / m³. The current dynamic effective interval width is 0.2mg / m³, which meets the conditions; Correction of strong overlapping scene factors: The overlap of the three devices is >80%, indicating strong systemic pollution. The pre-operation mode is activated, and the interval is expanded to the left by 0.05 mg / m³. The collaborative correction interval is [0.75, 0.95]. Calculate the intersection of the dynamic effective interval and the collaborative correction interval, and the optimal intervention interval is [0.75, 0.95]; Step 4: Obtain the time period corresponding to the priority intervention interval from historical data, build a time prediction model and multi-device collaborative algorithm, predict the time period when the preferred intervention interval will appear, and formulate a collaborative control strategy; Among them, the method of building a time prediction model is: Obtain historical industrial gas monitoring data and preprocess it to extract time characteristics and gas characteristics, and eliminate abnormal data; Among them, time characteristics include: start and stop time of purification equipment, production shifts; Gas characteristics include: average judgment index of priority intervention interval, ion benefit ratio; Then, a dual-model fusion strategy is adopted, using the Prophet model algorithm to capture the periodicity of time series and combining it with the LSTM model algorithm to handle irregular fluctuations and long-term dependencies. Build a time prediction model using the rophet model algorithm and the LSTM model algorithm; Input time characteristics and gas characteristics into the time prediction model, and output the periodic time window and real-time probability warning through the time prediction model; Those skilled in the art will understand that when constructing a Prophet model, time series data is decomposed into trend, seasonal, and holiday terms. The trend term uses a linear or logistic growth model to describe the long-term trend of the data; the seasonal term uses Fourier series to fit cyclical changes (such as daily, weekly, and annual seasonality); and the holiday term is modeled using a custom holiday list and impact magnitude. The Prophet model uses historical data to fit and optimize various parameters (such as trend slope, seasonal cycle amplitude, holiday influence coefficient). When making predictions, future time points are substituted into the model, and the superposition values of each component are calculated to obtain the final prediction result, which can effectively capture the periodic and trend characteristics of the time series; When constructing the LSTM model, the input layer receives the historical data of the time series (such as feature vectors organized by time steps), and then processes the data through one or more LSTM layers. Each LSTM layer contains memory units, forget gates, input gates, and output gates, which are used to capture long-term dependencies and solve the gradient vanishing problem of traditional recurrent neural networks (RNNs). After being processed by the LSTM layer, the data enters the fully connected layer, and the predicted values are output through activation functions. During model training, the backpropagation algorithm (such as BPTT) is used to adjust the network weights to minimize the error between the predicted values and the actual values (such as mean squared error). When making predictions, the sequence of historical time steps is input into the model, and the LSTM layer passes information through the memory units to gradually calculate and output the predicted values of future time points, which is good at dealing with non-linear and complex time series patterns; When the prediction results overlap, the cooperative control strategy is triggered to achieve the prediction of the time period when the priority intervention interval appears; Based on the time period when the preferred intervention interval appears, a multi-device cooperative control strategy is formulated; Among them, the method of the multi-device cooperative control strategy is as follows: Preferably, a cooperative objective function is established: , where is the real-time energy consumption of the purification equipment, load(i) is the square of the deviation of the equipment load from the mean value, and is the preset weight is the preset weight coefficient; It should be noted that is set by professionals in this field. m represents the total number of purification equipment, which limits the range of the summation operation. i represents the index of the purification equipment, which is used to distinguish different individual purification equipment; Based on the cooperative objective function, the decision variables are determined as the voltage and air volume of each device, and the equipment processing capacity constraints (such as removal rate ≥ concentration increase rate) are incorporated; The model predictive control is used to solve the objective function to find the optimal parameter combination that balances energy consumption and load within the future time window; The real-time energy consumption of each device is collected and the load status, the load deviation degree load(i) is calculated and substituted into the objective function, and the edge controller is used to optimize and output the device operation instructions in real time to adjust the voltage and air volume of each device.

[0030] The technical solution of this embodiment is as follows: If they are inconsistent, extract the working condition analysis interval to determine whether the working condition analysis intervals of multiple devices are consistent. If they are consistent, construct the response curve of the average judgment index and the purification efficiency, and identify the preferred intervention interval; Obtain the time period corresponding to the preferred intervention interval from the historical data, construct the time prediction model and the multi-device cooperation algorithm, predict the time period when the preferred intervention interval appears, and formulate the cooperative control strategy; Provide a predictable and adjustable intelligent solution for industrial waste gas treatment.

[0031] Embodiment 4 As shown in Figure 4, the volatile odor internal circulation purification control system generated by industrial equipment includes the following modules: Status determination module: used to collect the volatile gas generated by industrial equipment and extract the gas control index, perform judgment processing on the gas control index, and determine the startup status of the purification equipment; Stability analysis module: Based on the determined startup conditions, used to collect the average judgment index of the purification equipment and perform curve analysis, construct the purification efficiency group, perform stability analysis on the purification efficiency group to obtain the efficiency stability value, and judge whether the purification efficiency of the purification equipment is stable based on the efficiency stability value; Interval identification module: If they are inconsistent, extract the working condition analysis interval to determine whether the working condition analysis intervals of multiple devices are consistent. If they are consistent, construct the response curve of the average judgment index and the purification efficiency, and identify the preferred intervention interval; Prediction control module: Obtain the time period corresponding to the preferred intervention interval from the historical data, construct the time prediction model and the multi-device cooperation algorithm, predict the time period when the preferred intervention interval appears, and formulate the cooperative control strategy; The above has described an embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment, characterized in that, It includes the following steps: Extract gas control indicators and perform judgment processing to determine the startup status of the purification equipment; Collect the average judgment indicators of the purification equipment and conduct curve analysis, construct a purification benefit group, perform stability analysis on the purification benefit group to obtain a benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; If inconsistent, extract the working condition analysis interval, judge whether the working condition analysis intervals of multiple devices are consistent. If consistent, construct a response curve of the average judgment indicator and the purification benefit, and identify the preferred intervention interval; Obtain the time period corresponding to the preferred intervention interval from historical data, construct a time prediction model and a multi-device collaboration algorithm, predict the time period when the preferred intervention interval appears, and formulate a collaborative control strategy.

2. The volatile odor internal circulation purification control method for the industrial equipment according to claim 1, characterized in that, The method for determining the startup status of the purification equipment is as follows: Take the total volatile organic compound concentration as the control indicator, collect the total volatile organic compound concentrations at the inlet of the plasma module at multiple monitoring moments within the monitoring period, and construct a control indicator group; Extract the characteristic indicators of the control indicator group and set the equipment start-stop judgment conditions, and determine the start-stop status of the equipment based on the start-stop judgment conditions.

3. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 1, characterized in that, The method for obtaining the benefit stability value is as follows: Collect the average judgment indicators of the purification equipment, and establish a change curve of time and the average judgment indicator in a two-dimensional coordinate system; Obtain the purification time interval of the purification equipment, intercept and analyze the change curve of time and the average judgment indicator according to the purification time interval to obtain the purification benefit ratio; Conduct fitting analysis on the purification benefit ratio to obtain the benefit stability value.

4. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 3, characterized in that, The method for obtaining the purification benefit ratio is as follows: Calculate the intercepted area of the change curve of time and the average judgment indicator as the purification index quantity; Obtain the total energy consumption value of the purification equipment within the purification time interval, and perform a ratio process on the purification index quantity and the total energy consumption value to obtain the purification benefit ratio.

5. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 4, characterized in that, The method for conducting fitting analysis is as follows: Combine the purification benefit ratios of multiple purification time intervals to construct a purification benefit group; Divide the purification benefit group into multiple sub-intervals of different lengths, and calculate the rescaled range of the sub-intervals; Based on the rescaled range, use linear regression method for fitting to obtain the fitting line and the slope of the fitting line.

6. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 1, characterized in that, The method for judging whether the working condition analysis intervals of multiple devices are consistent is as follows: Obtain the working condition analysis intervals of multiple devices. If there is an overlapping part in the working condition analysis intervals of multiple devices, calculate the overlap degree of the working condition analysis intervals of any two devices; Based on the overlap degree, conduct a comparison analysis to judge whether the working condition analysis intervals of multiple devices are consistent.

7. The volatile odor internal circulation purification control method for industrial equipment according to claim 1, characterized in that, The method for identifying the preferred intervention interval is as follows: Calculate the ion benefit ratio of the ion energy consumption index curve, and perform segmented processing on the curve based on the ion benefit ratio; Obtain the interval of equipment collaboration and perform boundary correction to identify the dynamic effective interval; Perform multi-dimensional constraint calibration on the dynamic effective interval to identify the preferred intervention interval.

8. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 7, characterized in that, The method for performing multi-dimensional constraint calibration is as follows: In multi-dimensional constraint calibration, calculate the boundary of the high-efficiency section and the minimum width limit, judge whether the dynamic effective interval meets the boundary of the high-efficiency section and the minimum width limit. If it meets, perform correction of the overlapping scenario factors to determine the collaborative correction interval.

9. The method for purifying and controlling the internal circulation of volatile odors generated by industrial equipment according to claim 1, characterized in that The method for formulating the collaborative control strategy is as follows: Obtain the historical monitoring data of industrial gases and perform preprocessing to extract time features and gas features; Input the time features and gas features into the time prediction model to output the periodic time window and real-time probability warning. When the prediction results coincide, trigger the collaborative control strategy to realize the prediction of the time period when the priority intervention interval appears; Based on the predicted time period when the priority intervention interval appears, formulate a multi-device collaborative control strategy.

10. An internal circulation purification control system for volatile odors generated by industrial equipment, which is used to implement the internal circulation purification control method for volatile odors generated by industrial equipment described in any one of claims 1-9, characterized in that, It includes the following modules: Status determination module: used to extract gas control indicators and perform judgment processing to determine the startup status of the purification equipment; Stability analysis module: based on the determined startup conditions, used to collect the average judgment indicators of the purification equipment and perform curve analysis, construct a purification benefit group, perform stability analysis on the purification benefit group to obtain the benefit stability value, and judge whether the purification benefit of the purification equipment is stable based on the benefit stability value; Interval identification module: if inconsistent, extract the working condition analysis interval, used to judge whether the working condition analysis intervals of multiple devices are consistent. If consistent, construct the response curve of the average judgment indicator and the purification benefit, and identify the preferred intervention interval; Prediction control module: obtain the time period corresponding to the priority intervention interval from the historical data, construct a time prediction model and a multi-device collaborative algorithm, predict the time period when the preferred intervention interval appears, and formulate a collaborative control strategy.

Images