Dust generation source estimation method and analysis device

Abstract

To provide a dust generation source estimation method and an analysis device capable of easily estimating a dust generation source in an air-clean space in a short time.SOLUTION: The analysis device includes: an estimated particle concentration calculation unit 15 for using a weight matrix W in which a weight coefficient is increased as a distance is closer based on the distance between a position of each work area and a position of each particle sensor, and performing processing for calculating a value obtained by dividing an addition value of the weight coefficient of each particle sensor for one work area multiplied by a particle concentration detected by each particle sensor by the addition value of the weight coefficient of each particle sensor for the one work area as the estimated particle concentration of the one work area for all work areas; and a dust generation source estimation unit 16 for calculating a value obtained by dividing the estimated particle concentration of the work area by the addition value of the estimated particle concentration of all work areas for each work area as a dust generation contribution amount of each work area and estimating the work area of the dust generation source.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a dust source estimating method and an analyzing device that can easily estimate a dust source in an air clean space in a short time. [Background technology]

[0002] In industrial clean rooms, cleanliness control is important to improve product quality and yield, but cleanliness is affected by dust generated by workers and product manufacturing processes. In conventional industrial clean room operations, particle concentration is generally measured periodically using handheld measuring equipment to control cleanliness. If the cleanliness level falls below the control level, i.e., if the particle concentration becomes high, the cause must be analyzed and identified by repeating measurements after the fact, and it takes time to implement subsequent cleanliness measures.

[0003] As a method for identifying the cause of the deterioration of cleanliness, that is, the dust source, there is a method disclosed in Patent Document 1, in which the particle size distribution is grasped by a particle concentration measuring device, the part of dust generation that is not stationary in time is selected, and the particle size distribution and the time change characteristics of the concentration based on the particle size distribution are compared with information previously collected about the dust source to identify the dust source. There are other methods like this that have been seen for a long time, such as Non-Patent Document 1. This is an application of a long-known method called the source receptor method in the field of air environment, and requires a profile of the pollutant at the source (the particle size distribution of the generated particles in the case of these documents). Therefore, it is a method for finding out the contribution of known pollution sources to pollution at a certain point, and cannot be used to identify the location of a pollution source when the pollution source is unknown.

[0004] Furthermore, in Patent Document 2, unlike Patent Document 1, the values ​​obtained by the measuring device are compared with the results of a simulation (numerical fluid analysis) in which two parameters, the location of pollutant generation and the amount of generation, are appropriately assumed, and the parameters are corrected based on the deviation and the simulation is run again, and this process is repeated to estimate the likely location of pollutant generation and amount of generation.

[0005] The method of identifying the dust source disclosed in Patent Document 2 is also a general method, but it requires calculations to be performed in real time, which generates a huge amount of computational processing and takes a long time to identify the contamination source. The measuring device described in Patent Document 2 periodically obtains the concentration of contaminants, but this calculation is performed each time for each location in the vast clean room, which is not realistic.

[0006] In addition, Patent Document 2 proposes, as a solution for shortening the time required to identify a pollution source, that when it is determined that there is no change in the airflow distribution, no airflow calculation is performed, and only the transport equation related to the pollutant concentration or the equation of motion of the pollutant particles is calculated using the calculation result of the previous airflow distribution. As another solution, a simulator is used to calculate the airflow distribution and the pollutant concentration distribution under many conceivable parameters and calculation conditions in advance, and the results are recorded in a storage device (not shown). It also proposes that the calculation result that best reproduces the measurement result is obtained from the storage device in the pollution source search unit. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 6-66711 [Patent Document 2] JP 2006-177685 A [Non-patent literature]

[0008] [Non-Patent Document 1] Yi Tian, ​​Pratim Biswas, Sotiris E. Pratsinis & Jin Jwang Wu: Receptor Modeling for Contaminant Particle Source Apportionment in Clean Rooms, Aerosol Science and Technology, Vol.12, No.4, pp.805-812, 1990 Summary of the Invention [Problem to be solved by the invention]

[0009] Incidentally, Patent Document 2 is practical when there are several contamination sources (dust sources) and the airflow patterns are limited, but when there are 10 or more dust sources and the amount of generated contaminants is unknown, an astronomical number of patterns arise, and it is not practical for estimating the contribution of multiple dust sources in the complex airflow fields of turbulent clean rooms that have become widespread in recent years, or in clean rooms equipped with a system that controls the change and adjustment of the airflow volume of the cleanroom air conditioning.

[0010] The present invention has been made in view of the above, and has an object to provide a dust source estimation method and an analysis device that can easily estimate the dust source in an air clean space in a short time. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems and achieve the object, the dust source estimating method according to the present invention is a dust source estimating method for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas where dust may be generated are arranged, and includes a weight matrix generating step of generating a weight matrix indicating a weight coefficient between each work area and each particle sensor, the weight coefficient being larger as the distance between the position of each work area and the position of each particle sensor is shorter, a particle concentration detecting step of detecting a particle concentration by each particle sensor, and a particle concentration detecting step of detecting a particle concentration by using the weight matrix. The method includes an estimated particle concentration calculation step of performing a process for all work areas in which an estimated particle concentration for one work area is calculated by multiplying a weighting coefficient of each particle sensor for the work area by the particle concentration detected by each particle sensor, and dividing the sum of the weighting coefficients of each particle sensor for the one work area by the sum of the weighting coefficients of the particle sensors for the one work area, and a dust source estimation step of calculating, for each work area, a value obtained by dividing the estimated particle concentration of the work area by the sum of the estimated particle concentrations of all work areas as the dust contribution amount of each work area, and estimating which work area is a dust source based on the dust contribution amount.

[0012] The dust source estimating method according to the present invention is a dust source estimating method for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas possibly generating dust are arranged, and includes a weight matrix generating step of performing a process for each work area to obtain a weight coefficient between the work area and each particle sensor, the weight coefficient being larger as the particle concentration is higher, based on the particle concentration detected by each particle sensor by making a unit amount of dust be generated from the work area, and acquiring a weight matrix indicating the weight coefficient between each work area and each particle sensor; and a particle concentration detecting step of detecting the particle concentration by each particle sensor. an estimated particle concentration calculation step of performing a process for all work areas using the weight matrix to calculate an estimated particle concentration for one work area by dividing the sum of values ​​obtained by multiplying the weight coefficients of each particle sensor for one work area by the particle concentrations detected by each particle sensor by the sum of the weight coefficients of each particle sensor for the one work area, and a dust source estimation step of calculating, for each work area, a value obtained by dividing the estimated particle concentration of the work area by the sum of the estimated particle concentrations of all work areas as the dust contribution amount of each work area, and estimating which work area is a dust source based on the dust contribution amount.

[0013] In addition, the dust source estimation method according to the present invention is characterized in that, in the above-mentioned invention, the weighting coefficient is the inverse of a value obtained by raising the distance between the work area and the particle sensor to a power, and the value of the power is a value between 1 and 5.

[0014] In the dust source estimating method according to the present invention, in the above invention, the dust source identifying step estimates the source as a dust source when the dust contribution amount is equal to or greater than a predetermined value.

[0015] The dust source estimating method according to the present invention is characterized in that, in the above-mentioned invention, the relationship between each work type and the dust contribution amount is displayed based on a plurality of work process schedules performed in each work area and time-series data of particle concentrations detected by each particle sensor during the work period of the work process schedules.

[0016] Moreover, an analysis device according to the present invention is an analysis device that estimates a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas where dust may be generated are arranged, and is characterized by comprising: a weight matrix indicating a weight coefficient for each work area and each particle sensor, the weight coefficient being larger as the distance between the position of each work area and the position of each particle sensor is closer, and an estimated particle concentration calculation section that uses the weight matrix to perform a process for all work areas of calculating an estimated particle concentration for the one work area by dividing the sum of values ​​obtained by multiplying the weight coefficients of each particle sensor for one work area by the particle concentrations detected by each particle sensor, by the sum of the weight coefficients of each particle sensor for the one work area; and a dust source estimation section that calculates a value obtained by dividing the estimated particle concentration of the work area by the sum of the estimated particle concentrations of all work areas for each work area as the dust contribution amount of each work area, and estimates the work area as a dust source based on the dust contribution amount.

[0017] The analysis device according to the present invention is an analysis device for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas that may generate dust are arranged, and the analysis device estimates the work area as a dust source in the clean room in which a unit amount of dust is generated from one work area, and the analysis device performs a process for each work area to obtain a weighting coefficient between the work area and each particle sensor, the weighting coefficient being larger as the particle concentration is higher, based on the particle concentration detected by each particle sensor. The weighting coefficient is a weight matrix indicating the weighting coefficient between each work area and each particle sensor. an estimated particle concentration calculation unit that performs a process for all work areas to calculate an estimated particle concentration for one work area by dividing the sum of values ​​obtained by multiplying the weighting coefficients of each particle sensor for the rear by the particle concentrations detected by each particle sensor by the sum of the weighting coefficients of each particle sensor for the one work area; and a dust source estimation unit that calculates, for each work area, a value obtained by dividing the estimated particle concentration of the work area by the sum of the estimated particle concentrations of all work areas as the dust contribution amount of each work area, and estimates which work areas are dust sources based on the dust contribution amount. Effect of the Invention

[0018] According to the present invention, the dust source in an air cleaned space can be easily estimated in a short time. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram for explaining the concept of the dust source identification method according to the present embodiment. [Diagram 2] FIG. 2 is a diagram showing an overall schematic configuration of a clean room that forms an air-cleaned space. [Diagram 3] FIG. 3 is a diagram showing the configuration of one section E of the clean room. [Figure 4] FIG. 4 is a block diagram showing a detailed configuration of the analysis device. [Diagram 5] FIG. 5 is a flowchart showing a procedure of a dust source estimation process performed by the control unit. [Figure 6] FIG. 6 is a diagram showing an example of a work process schedule. [Figure 7] FIG. 7 is a diagram showing the results of the dust generation contribution amount measured using the weighting matrix according to the embodiment. [Figure 8] FIG. 8 is a diagram showing the results of the dust generation contribution amount measured using the sensitivity matrix according to the modified example. [Figure 9] FIG. 9 shows the weekly average of the dust generation contribution amount for each work type. [Figure 10] FIG. 10 is a diagram showing the results of the dust generation contribution amount when the exponent value of the power is changed in the estimation of the dust generation contribution amount using a weight matrix. [Figure 11] FIG. 11 is a diagram visualizing the weighting coefficients when the exponent value of the power of the weighting matrix is ​​changed. [Figure 12] FIG. 12 is a visualization of the weighting coefficients of the sensitivity matrix. [Figure 13] FIG. 13 is a side view showing the configuration of the compartments of the air purification system. [Figure 14] FIG. 14 is a block diagram showing a control system of the air purification system. [Figure 15] FIG. 15 is a block diagram showing four control modes executed in the air purification system and their transition relationships. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

[0021] <Concept of dust source estimation method> FIG. 1 is a diagram for explaining the concept of the dust source identification method according to the present embodiment. FIG. 1(a) is a diagram for explaining the inverse distance weighting method, which is a conventional method for estimating the value of an unmeasured point P in spatial statistics. As shown in FIG. 1(a), in the inverse distance weighting method, for example, when estimating the particle concentration of an unmeasured point P, the detected particle concentration of a particle sensor that is close to the unmeasured point P and each of the particle sensors AA, BB, CC, and DD arranged around the unmeasured point P is considered to have a strong influence on the particle concentration of the unmeasured point P. In other words, the higher the detected particle concentration of the particle sensor, the stronger the influence on the particle concentration of the unmeasured point P, and the closer the distance between the particle sensor and the unmeasured point P, the stronger the influence on the particle concentration of the unmeasured point P.

[0022] In this embodiment, as shown in Fig. 1(b), this inverse distance weighting method is used, and it is assumed that the higher the detected particle concentration of the particle sensor, the higher the dust generation contribution rate of the working area EP, and that the closer the distance to the particle sensor, the higher the dust generation contribution rate of the working area EP, and the dust generation contribution rate of the working area EP is estimated based on the detected particle concentration and distance of each particle sensor AA, BB, CC, DD. In other words, the closer to the working area EP and the higher the detected particle concentration of the particle sensor, the greater the amount of dust generated in the working area EP, and the dust generation amount of the working area EP is estimated inversely. Then, the working area EP with the greatest amount of dust generation is estimated as the dust source.

[0023] <Calculation of dust generation contribution amount> When multiple work areas j (j = 1 to M) and multiple particle sensors i (i = 1 to N) are arranged in a clean room, a weight matrix W is calculated based on the distance d(i, j) between the work area j and the particle sensor i. i j and the particle concentration C of each particle sensor i i Based on this, the estimated particle concentration C of particle sensor j is calculated. j That is, the estimated particle concentration C of particle sensor j is calculated using equation (1). j is calculated. Note that the weight matrix W ij is calculated using equation (2).

[0024]

number

[0025]

number

[0026] Equation (1) expresses the weighting coefficient of each particle sensor i for one working area j (weight matrix W i j ) the particle concentration C detected by each particle sensor i i The sum of the multiplied values ​​is the weighting coefficient (weight matrix W i j ) is the estimated particle concentration C j The weight matrix W shown in equation (2) is calculated as follows: i j is the reciprocal of the value of the distance d(i,j) between the work area j and the particle sensor i raised to a power, and the exponent value k of the power is, for example, a value between 1 and 5, as described later.

[0027] Then, the estimated particle concentration C j are added together as shown in equation (3) to give the total estimated particle concentration C total is required.

[0028]

number

[0029] And the dust contribution amount PS of work area j j is the contribution rate of dust generation, which can be calculated using equation (4).

[0030]

number

[0031] Equation (4) expresses for each work area j the estimated particle concentration C j The total estimated particle concentration C total This value is the sum of the estimated particle concentration C total When converted to a percentage, it becomes the dust generation contribution rate.

[0032] <Cleanroom configuration> Fig. 2 is a diagram showing an overall schematic configuration of a clean room CR that forms an air-cleaned space. Fig. 3 is a diagram showing the configuration of one section E of the clean room CR. As shown in Fig. 2, the clean room CR is a collection of 15 sections E, and each section E forms a local airflow field. In the figure, the six sections E on the left side are parts yards EB, which are areas where parts and the like are stored, and the nine sections E on the right side are areas where various work processes are carried out, and there are 18 work areas A01 to A18.

[0033] As shown in Fig. 3, two heat exchange units FCUs are arranged in the center on the top surface Ea side of each compartment E, and six air purifiers FFUs are arranged around the heat exchange units FCUs, allowing air to circulate within the compartment E to form a local airflow field. The heat exchange units FCUs are arranged above the air purifiers FFUs and in the vicinity of the top surface Ea, and perform heat exchange (heat removal) on the air drawn in from below and blow it out to the side. The air purifiers FFUs are arranged below the heat exchange units FCUs, and purify the air drawn in from above and blow it out downward.

[0034] Moreover, particle sensors 1 (S01, 02) are disposed near the two heat exchange devices FCU, respectively. Eighteen particle sensors S01 to S18 are provided in the cleanliness control area (the area on the right side of the figure) of the clean room CR, and the particle sensors 1 are sensors that detect the cleanliness of the corresponding area, that is, the particle concentration. Furthermore, work areas A01 to A18 are provided on the floor side of each section E.

[0035] Therefore, in order to manage the cleanliness of the clean room CR, 18 particle sensors S01 to S18 and 18 work areas A01 to A18 are provided, and the above weight matrix W i j is a matrix with 18 rows and 18 columns. Note that the weight matrix W i j is sometimes referred to as the weighting matrix W for short.

[0036] <Analysis equipment> 2(b), an analysis device 10 is connected to the clean room CR. The analysis device 10 estimates the work areas A01-A18 as dust sources in the clean room CR, in which a plurality of particle sensors S01-S18 and a plurality of work areas A01-A18 possibly generating dust are arranged, using the above-mentioned dust source estimation method.

[0037] Fig. 4 is a block diagram showing a detailed configuration of the analysis device 10. As shown in Fig. 4, the analysis device 10 is connected to particle sensors S01 to S18, and acquires particle concentrations detected by each of the particle sensors S01 to S18. The analysis device 10 has an input unit 11, a display unit 12, a storage unit 13, and a control unit 14.

[0038] The input unit 11 is an input interface for inputting various operations. The display unit 12 is an output interface for displaying and outputting various information. The storage unit 13 is a storage device consisting of a hard disk drive, non-volatile memory, etc., and stores a weighting matrix W, a work process schedule SC, and time series data DA. The work process schedule SC is a schedule of multiple work types performed in each work area A01-18. The time series data DA is time series data of particle concentrations detected by each particle sensor S01-S18 during the work period of the work process schedule SC.

[0039] The control unit 14 is a control unit that controls the entire analysis device 10, and has an estimated particle concentration calculation unit 15 and a dust source estimation unit 16. The control unit 14 stores programs corresponding to these functional units in a storage device such as a non-volatile memory or a magnetic disk device, and loads these programs into the memory and executes them on the CPU, thereby executing the corresponding processes.

[0040] The estimated particle concentration calculation unit 15 uses a weighting matrix W to perform a process for all work areas A01 to A18 in which the sum of values ​​obtained by multiplying the weighting coefficients of each particle sensor S01 to S18 for one work area A01 to A18 by the particle concentrations detected by each particle sensor S01 to S18 is divided by the sum of the weighting coefficients of each particle sensor S01 to S18 for one work area A01 to A18 to calculate an estimated particle concentration for one work area A01 to A18.

[0041] The dust source estimation unit 16 obtains a dust contribution amount of each of the work areas A01-A18 by dividing the estimated particle concentration of the work areas A01-A18 by the sum of the estimated particle concentrations of all the work areas A01-A18, and estimates the work areas A01-A18 as dust sources based on the dust contribution amounts. For example, the dust source estimation is performed when the dust contribution amount is equal to or greater than a predetermined value.

[0042] <Dust source estimation processing> Fig. 5 is a flowchart showing a procedure of a dust source estimation process performed by the control unit 14. As shown in Fig. 5, first, the control unit 14 generates a weighting matrix W based on the positional relationship between the work areas A01 to A18 and the particle sensors S01 to S18 (step S101).

[0043] Thereafter, the control unit 14 acquires the particle concentrations detected by the particle sensors S01 to S18 (step S102). Then, the estimated particle concentration calculation unit 15 calculates the estimated particle concentrations for all the work areas A01 to A18 (step S103). Thereafter, the dust source estimation unit 16 calculates the dust contribution amount (step S104), estimates the work areas A01 to A18 whose dust contribution amount is equal to or greater than a predetermined value as dust sources (step S105), and ends this process.

[0044] <Modification> In the above embodiment, for a clean room in which a local airflow field is formed by the configuration of section E, dust source estimation processing is performed using a weighting matrix W in which the weight is the inverse of the distance between the work area and the particle sensor. However, in a clean room in which a local airflow field cannot be formed, particles from the dust source are carried far away by the airflow in high concentrations, so the use of a weighting matrix based on distance is not suitable.

[0045] For this reason, in this modified example, a sensitivity matrix D is used, which is a weight matrix in which the sensitivity of the particle sensor i to the working area j is used as a weight, instead of the distance d(i,j) between the working area j and the particle sensor i. The sensitivity matrix D may be obtained by using a CFD (Computational Fluid Dynamics) method, for example, by providing some unit amount of dust from one of the working areas A01 to A18, obtaining information on the particle concentration detected by each particle sensor S01 to S18, and performing this process for each of the working areas A01 to A18. Specifically, a steady analysis is performed by providing a unit amount of dust (for example, 1000 particles / min per working area) only from a certain working area j among the working areas A01 to A18, and the particle concentration of each particle sensor S01 to S18 at that time is extracted, and this particle concentration is set as the sensitivity of each particle sensor S01 to S18 to the working area j. This sensitivity matrix D is also a matrix with 18 rows and 18 columns.

[0046] In addition, the calculation of the dust generation contribution amount according to the modified example is performed by replacing the weight matrix W in equation (4) with the sensitivity matrix D as shown in equation (5), and calculating the dust generation contribution amount PS2 j This can be done as follows.

[0047]

number

[0048] In the dust source estimation process according to this modification, the sensitivity matrix D is generated in step S101 shown in Fig. 5. The other processes are the same as those in the embodiment.

[0049] <Specific examples of dust source estimation> First, in the clean room CR, work of a predetermined work type is not performed in a specific work area, but all work is performed in each work area in order. For example, as shown in Fig. 6, the work process schedules SC1 and SC2 are performed twice (two weeks) for five days on weekdays. In the work process schedules SC1 and SC2 shown in Fig. 6, five work types "A", "B", "C", "D", "E", "F", "G", and "H" are assigned to each work area A01 to 18, including the empty areas.

[0050] During this work period, the analysis device 10 acquires time series data Ci(t) of particle concentration Ci detected by each particle sensor S01-S18, and calculates time series data Cj(t) of estimated particle concentration Cj. Then, as shown in formula (6), the time series data Cj(t) is used to integrate each dust generation contribution amount ΔPSj during the work period (t=0-T) to obtain the dust generation contribution amount PSj for the entire work period. Note that formula (1) shows the case where the weight matrix W shown in the embodiment is used, and the same can be obtained when the sensitivity matrix D shown in the modified example is used.

[0051]

number

[0052] FIG. 7 is a diagram showing the results of the dust generation contribution amount measured using the weight matrix W according to the embodiment, where FIG. 7(a) shows the result for the first week, and FIG. 7(b) shows the result for the second week. FIG. 8 is a diagram showing the results of the dust generation contribution amount measured using the sensitivity matrix D according to the modified example, where FIG. 8(a) shows the result for the first week, and FIG. 8(b) shows the result for the second week. In FIG. 7 and FIG. 8, the dust generation contribution amount of each work type "A" to "H" is shown for each day (each day of the week). In FIG. 7 and FIG. 8, the exponent value k is set to 2. In FIG. 7 and FIG. 8, a "1" is written at the top of the bar graph of the work type with the highest dust generation contribution amount on each day of the week.

[0053] In the first week in Figure 7, the dust contribution of work type "A" was high from Monday to Wednesday, and the dust contribution of work type "D" was high on Friday. In addition, the second week was a week with many work types "A" and "H", but the dust contribution of work type "H" was high on Monday, and the dust contribution of work type "A" was high from Tuesday to Friday.

[0054] On the other hand, in the first week in Figure 8, the results are somewhat more dispersed between each work type compared to the dust contribution amounts shown in Figure 7. Looking at the results by day of the week, the dust contribution amounts of work type "A" were high on Monday, Wednesday, Thursday, and Friday, while the dust contribution amount of work type "B" was high on Tuesday. Also, in the second week, as in Figure 7, the dust contribution amount of work type "A" was high on all days.

[0055] FIG. 9 shows the dust contribution amount for each work type averaged over a week. FIG. 9(a) shows the dust contribution amount using the weight matrix W, and FIG. 9(b) shows the dust contribution amount using the sensitivity matrix D. From the results in FIG. 9, the results of the dust contribution amount using the weight matrix W and the dust contribution amount using the sensitivity matrix D are almost similar, and it was found that work type "A" is highly likely to be the dust source. Then, from this result, we narrowed down our focus to work type "A", confirmed the time series data of the dust contribution amount in the work area where work type "A" is performed, and measured the particle concentration in the on-site work area during the time period when the dust contribution amount of work type "A" is particularly high, and obtained results that lead to identifying work type "A" as a dust-generating process.

[0056] FIG. 10 shows the results of the dust contribution amount when the exponent value k of the power is changed in the estimation of the dust contribution amount using the weight matrix W. FIG. 10(a) to FIG. 10(c) show the cases where the exponent value k is 1, 3, and 5, respectively. From the results of FIG. 10, the larger the exponent value k, the greater the weight of the particle sensor close to the work area, and the influence of the particle sensor is rapidly weakened when the particle sensor is only a little away. In addition, when the exponent value k is 5, the dust contribution amount of the work type "A" was estimated to be high in both the first and second weeks. In addition, when the exponent value k is 3 and 5, the results were almost the same. As a result, if the exponent value k is too large, the influence of the distant particle sensor is neglected, so the exponent value k should be 2 to 3.

[0057] The value of the exponent value k will be further considered. Figure 11 is a diagram visualizing the weighting coefficients when the exponent value k of the power of the weighting matrix W is changed. In Figure 11, the weighting coefficients, which are the elements of the weighting matrix W, are normalized by dividing them by the sum of all the weighting coefficients, and are made dimensionless in the range from 0 to 1. When the exponent value k is 1, the weighting coefficient is approximately 0.4 at the maximum, and the weights are dispersed. When the exponent value k is further increased, the difference between the magnitudes of the weighting coefficients becomes more noticeable, and the influence of a specific particle sensor on a specific work area is more emphasized. For example, when the exponent value k is 5, the value of the particle sensor S04 becomes large for the work area A04, and the values ​​of the other particle sensors hardly change.

[0058] Moreover, Fig. 12 is a diagram visualizing the weighting coefficients of the sensitivity matrix D. In the case of the sensitivity matrix D, the exponent value k of the power is not used. The distribution of the weighting coefficients (sensitivity coefficients) shown in Fig. 12 is similar to the distribution shown in Fig. 11, but the highest value is about 0.7, and the weighting coefficients are distributed to a larger number of cells compared to the weighting matrix W. This is a manifestation of the influence of advection due to air currents.

[0059] When calculating the dust contribution amount of each work type in each work process, the average dust contribution amount of each work type may be obtained by continuously calculating the amount of dust for an entire day (or a long period of time) and averaging the amount of dust for the work period. Furthermore, in the event of sudden large dust generation, for example when a particle sensor detects a high particle concentration, the dust contribution amount may be estimated to identify the cause of the dust generation. In other words, the dust contribution amount of each work process may be calculated only when the particle concentration exceeds a set value. In this case, the dust contribution amount may be calculated and accumulated each time sudden dust generation occurs to clarify the dust contribution amount of each work type, and the dust contribution amounts may be compared to identify the cause of the dust generation.

[0060] However, under conditions where the particle concentration is low, the dust generation contribution amount calculated using either the weighting matrix W or the sensitivity matrix D will be low and will not differ significantly, so the dust generation contribution amount may be calculated using the weighting matrix W rather than the sensitivity matrix D.

[0061] In addition, if dust generation is caused solely by workers or their work activities, information from the image sensor can be combined, and processing can be performed such that work areas in which the image sensor does not recognize people are excluded from the evaluation during those times.

[0062] Furthermore, when the work process schedule SC is prepared from the beginning, as in this embodiment and its modified example, the amount of dust generated by each work type can be immediately known from the information on particle concentration obtained in real time by the particle sensor. Conversely, if records of which work types were performed on which days and where, and past schedules remain as operational records, the amount of dust generated by each work type can be calculated later based on those records. Also, if information such as a sensor that can count the number of people, on / off status of equipment, and surface temperature can be obtained, that information can be incorporated into the calculation of the amount of dust generated, or shown as auxiliary information when visualizing the results.

[0063] The graphs of the dust generation contribution amount shown in Figs. 7 to 9 are displayed on the display unit 12 or the like, and are displayed as information useful in identifying the cause of dust generation.

[0064] <Application to air purification systems> Here, the analysis device shown in the embodiment and the modified example can be incorporated into the air cleaning system of the clean room CR shown in FIG. 2 and FIG.

[0065] FIG. 13 is a side view showing the configuration of section E of the air purification system. FIG. 14 is a block diagram showing the control system of the air purification system. The air purification system is a system that purifies the air in section E. Section E is the whole or a part of the clean room CR. Section E may be separated from other sections by partitions or curtains, or may not have any partitions depending on the conditions. Section E is equipped with production equipment for semiconductors and precision instruments, and requires a purified air environment.

[0066] As shown in FIGS. 13 and 14, the air purification system includes an air purification device FFU, a human presence sensor 2, a particle sensor 1, a heat exchange device FCU, and a control unit 5.

[0067] The air purifier FFU is placed in the upper part of the section E, slightly below the top surface Ea, and has a fan 3 installed at the top and a HEPA (High Efficiency Particulate Air) 4 installed at the bottom. The air purifier FFU sucks in air from above with the fan 3, purifies it with the HEPA 4, and blows it out downward. The fan 3 is of a variable speed drive type. Under the action of the control unit 5, the air purifier FFU can control the rotation speed of the fan 3 according to the situation and adjust the output.

[0068] The human presence sensor 2 is a sensor that detects whether or not a human H is present in the corresponding monitoring area, and examples of such sensors include an infrared sensor, an electromagnetic sensor, or a camera. The human presence sensor 2 can also detect the number of humans H present in the area. The human presence sensor 2 can also detect not only the number of humans H but also their movements, and can determine the type of work based on the type and frequency of the movements. Detectable movements include walking. The human presence sensor 2 is fixed integrally with the air purification device FFU, and each of them supplies a detection signal to the control unit 5. The human presence sensor 2 covers the section E.

[0069] The particle sensor 1 is a sensor that detects the cleanliness of the corresponding area, that is, the particle concentration, and is disposed, for example, on the side of the heat exchanger FCU. When the particle concentration is low, the air cleanliness is high. A temperature sensor may be provided in addition to the particle sensor 1. The particle sensors 1 each supply a detection signal to the control unit 5. When a temperature sensor is provided, a temperature signal is supplied to the heat exchanger FCU. The particle concentration detected by the particle sensor 1 is output to an analysis device 10, for example, via the control unit 5 or directly.

[0070] The heat exchange unit FCU is disposed above the air purifier FFU and near the top surface Ea, and exchanges heat (removes heat) with the air drawn in from below and blows it out to the side. The heat exchange unit FCU is disposed in the middle of each air purifier FFU. The heat exchange unit FCU can adjust the set temperature and wind speed according to the temperature sensor signal and the situation. The processed air blown out by the heat exchange unit FCU is set to a wind speed that does not short-circuit to the air intake of the heat exchange unit FCU and is sufficiently sucked into the air intake of the air purifier FFU.

[0071] In an air purification system configured in this manner, air in section E is sucked in from above the air purification device FFU, purified, and blown out downward, where it is heated by a heat source such as production equipment or human H, and then turns around on the floor and rises. The rising air is sucked into the heat exchange device FCU located between the air purification devices FFU, where it is heat exchanged and then blown out to the side, where it is sucked into the air purification device FFU again and convects. Note that the flow of air in section E is indicated by arrows in FIG. 13.

[0072] In the air purification system, the air purification unit FFU and the heat exchange unit FCU are separate, so temperature control and air purification can be performed separately and to the necessary extent, and one will not be operated at excessive capacity due to the influence of the other. In addition, the air flow can be circulated and convected within the section E, eliminating the need for air passages under the floor or above the ceiling.

[0073] In FIG. 14, each control object and each detector is connected to a control unit 5. The control unit 5 is, for example, a PLC (Programmable Logic Controller) and is provided in section E. In the air purification system, there is no need to provide a large-scale central processing unit, for example, an FEMS (Factory Energy Management System), outside the room, and control processing can be performed by a small control unit 5 inside the room, thereby reducing system construction costs. In addition, although the air purification device FFU and the heat exchange device FCU are functionally independent, the heat exchange device FCU may also be controlled by the control unit 5. In this case, it is preferable that the control unit 5 controls the heat exchange device FCU based on the temperature of section E.

[0074] The air purifier FFU is of a variable output type. The control unit 5 controls the output of the air purifier FFU (i.e., controls the rotation speed and air volume of the fan 3) via an inverter (not shown) based on the particle concentration (for example, the average value of the detection values ​​by the particle sensor 1) and temperature. The control that the control unit 5 performs on the air purifier FFU is broadly defined and includes constant speed operation, variable speed operation, and stopping. Stopping includes a case where the air volume of the fan 3 is set to 0 while the power is on, and a case where the power is off.

[0075] Fig. 15 is a block diagram showing four control modes executed in the air purification system and their transition relationships. In the air purification system, the control unit 5 executes a total of four modes, including a basic mode (first mode), a thinning mode (second mode), a human detection mode (third mode), and a maximum mode (fourth mode), as shown in Fig. 15. Mode transitions are performed automatically according to conditions described later.

[0076] In the basic mode, the output is controlled so that the particle concentration is at or below a target concentration (first threshold value). This control is, for example, proportional control. In the basic mode, all air purifiers FFU in section E are operated at the same output. The number of air purifiers FFU operated in the basic mode is basically all, but depending on the conditions, it may be a specified number N (N≧2).

[0077] In the air purification system, the purified airflow from the air purification device FFU needs to reach the work areas A01, A02, A05, and A06 of the production equipment (for example, at a position about 1000 mm from the floor). Therefore, in the basic mode, a minimum rotation speed is specified for the air purification device FFU during operation so that the purified airflow reaches the work area. This minimum rotation speed may be specified individually based on the heat load condition below the air purification device FFU and the installation height of the air purification device FFU, etc.

[0078] The thinning mode is a mode that is executed when it is determined that there is no need to operate all of the air purifiers FFU in section E, and is entered from the basic mode. Specifically, when the particle concentration in section E is equal to or lower than the target concentration and a predetermined stable state standard is satisfied, the air purifiers FFU that are in operation are stopped in sequence until a predetermined number of units (including 0 units) are reached. In this case, for example, first one air purifier FFU is stopped, and if the concentration is still below the target concentration even after a predetermined time has elapsed, another air purifier FFU is stopped. This is then repeated to stop the air purifiers in sequence until the predetermined number of units are reached.

[0079] The human detection mode is a mode that responds to dust generation from humans H, and operates the fan 3 at a predetermined first output or more. This first output is specified as being equal to or greater than the maximum value of the normal output range in the basic mode. When the human detection sensor 2 detects a human H, the mode is switched from the basic mode or thinning mode. The number of air purifiers FFU that operate in the human detection mode shall be equal to or greater than the specified number N (including all) that operate in the basic mode.

[0080] The maximum mode is a mode to which the system is switched when the particle concentration exceeds a predetermined monitoring concentration (second threshold). The monitoring concentration is a value higher than the target concentration described above, and is a value lower than a concentration that would affect production by the production equipment, but is a value for a state in which prompt purification is desired. The system is also switched to the maximum mode when the particle concentration exceeds a predetermined increase rate threshold. The maximum mode is switched to from all other control modes under the same conditions, and all air purification devices FFU are operated at the second output. This second output is basically the maximum rated output, but depending on the conditions, it may be a specified output higher than the first output described above.

[0081] Next, the transition conditions between the four control modes will be described in detail. The transition conditions between the control modes will be described below according to the symbols attached to the transition arrows in Fig. 15. The transition between the control modes is based on the following conditions, but a predetermined transition grace period may be set after the conditions are satisfied.

[0082] 5a. Conditions for switching from basic mode to thinning mode A transition from fundamental mode to thinned mode occurs when the following first steady state criterion or second steady state criterion is satisfied:

[0083] First stable state criterion: When the air purifier FFU continues to operate at the lowest output within its output setting range for a specified period of time, it transitions to thinning mode.

[0084] Second stable state criterion: If the particle concentration continues to be lower than the excess cleaning concentration, which is smaller than the above target concentration, for a specified period of time, the system will switch to the thinning mode. In addition to these two conditions, the stable state criterion may also be a state in which the particle concentration is stable at a sufficiently low value and is not expected to increase.

[0085] 5b. Conditions for switching from basic mode to motion detection mode When the following first human detection mode condition or second human detection mode condition is met, the basic mode is switched to the human detection mode.

[0086] First human detection mode condition: When a human H enters the section E while the basic mode is being executed with no human H present in the section E, if x number of human H enter the section E and a predetermined time has elapsed, the human detection mode will be switched to. x is a constant greater than or equal to 1.

[0087] Second human detection mode condition: When a human H is present in the area E but the impact of dust generation is small (for example, when there are only a few humans H or when there is little movement of humans H. Hereinafter referred to as a low dust generation state), and the basic mode is being executed, if a new human H enters the area, and the number of new human H is equal to or greater than a predetermined percentage (for example, 50%) of the current y human H present and a predetermined time has elapsed, the human detection mode will be switched to.

[0088] 5c. Conditions for transition from basic mode to maximum mode A transition to maximum mode occurs when the particle concentration exceeds a monitored concentration while the basic mode is running, or when the particle concentration exceeds a predetermined rate of increase threshold.

[0089] 5d. Conditions for switching from motion detection mode to basic mode The mode switches from the motion detection mode to the basic mode when the following first basic mode condition or second basic mode condition is met. If the particle concentration is low in the motion detection mode, the mode first switches to the basic mode, which performs proportional control. In the thinning mode, the mode switches only when it is determined that there is no problem in switching to the basic mode (for example, the particle concentration does not fall below the target concentration even when the particle concentration is at the lower limit of the rotation speed in proportional control). Therefore, the mode switches from the motion detection mode to the thinning mode always via the basic mode.

[0090] First basic mode condition: When the motion detection mode is being executed in a low particulate state, if the particle concentration remains below the target concentration even after a predetermined time has elapsed, the mode switches to the basic mode.

[0091] Second basic mode condition: When the human presence mode is being executed in a low dust generation state, if a person H leaves the section E and a predetermined time has passed (the human presence sensor 2 outputs a signal indicating that human H is not present for a predetermined period of time), and the particle concentration is below the target concentration, the mode will transition to the basic mode.

[0092] 5e. Conditions for switching from motion detection mode to maximum mode If the particle concentration exceeds the monitoring concentration while the motion detection mode is running, the mode will switch to maximum mode. This is the same concept as the condition in 5c above (switching from basic mode to maximum mode).

[0093] 5f. Conditions for switching from thinning mode to basic mode If the particle concentration exceeds the target concentration while the thinning mode is being executed, the mode is switched to the basic mode. In this case, the air purifiers FFU that are equal to or greater than the specified number N, including the air purifiers FFU that have been thinned out and stopped, are operated at the minimum rotation speed in the basic mode.

[0094] 5g. Conditions for switching from thinning mode to motion detection mode The transition from the thinning mode to the human detection mode occurs when the first human detection mode condition or the second human detection mode condition is met, similar to the condition in 5b above (transition from the basic mode to the human detection mode).

[0095] 5h. Conditions for Transition from Sparing Mode to Maximum Mode When the particle concentration exceeds the monitoring concentration during the execution of the human presence detection mode, it transitions to the maximum mode of operating with a predetermined output. This is the same concept as the condition in 5c. above (transition from the basic mode to the maximum mode).

[0096] 5i. Conditions for Transition from Maximum Mode to Basic Mode During the execution of the maximum mode, when the particle concentration falls below the target concentration, it transitions to the basic mode. Note that it does not transition from the maximum mode to the sparing mode. This is because the maximum mode is basically activated in case of an emergency, and it is not appropriate to transition to the sparing mode for the purpose of energy saving under such circumstances.

[0097] 5j. Conditions for Transition from Maximum Mode to Human Presence Detection Mode The maximum mode is released when it is detected that the particle concentration has dropped to the target concentration, and usually it transitions to the basic mode. However, depending on environmental conditions, system specifications, etc., when the presence of human H is detected, it may transition from the maximum mode directly to the human presence detection mode without going through the basic mode.

[0098] As described above, in the above air purification system, the control unit 5 operates a basic mode in which N or more air purification devices FFU are operated so that the particle concentration in compartment E is below the target concentration, and a sparing mode in which the operating air purification devices FFU are sequentially stopped to a predetermined number when the particle concentration in compartment E is below the target concentration and a predetermined steady state criterion is satisfied. In this way, by executing the sparing mode separately from the normal basic control mode, the operating cost can be reduced and energy can be saved. Also, in the sparing mode, rather than concentrating on air purification at a specific location or specific time, the operating cost can be further reduced by transitioning from the basic mode during steady state.

[0099] Furthermore, the control unit 5 executes a human detection mode in which a specified number N or more of the air purifying devices FFU are operated at a predetermined first output or higher when the human detection sensor 2 detects a human H. According to the human detection control mode, the particle concentration can be appropriately maintained even when a human H enters the section E.

[0100] Furthermore, the control unit 5 executes a maximum mode in which all of the air purifiers FFU are operated at or above the predetermined second output when the particle concentration in the section E becomes equal to or higher than the monitoring concentration. In this way, when the particle concentration becomes equal to or higher than the monitoring concentration, the particle concentration can be quickly reduced by operating all of the air purifiers FFU at or above the predetermined second output.

[0101] It is not necessary to use all of the above four control modes. For example, if the human sensor 2 is not provided, the human mode is not used. Also, it is not necessary to uniformly apply the above control modes to the entire section E, and a part may always be operated in the maximum mode or thinning mode. Furthermore, in addition to the presence or absence of humans H and particle concentration detected by the human sensor 2, the temperature of section E and the operating status of the production equipment may also be reflected in the transition of the control mode.

[0102] Note that the configurations illustrated in the above embodiments and modifications are merely functional schematics and do not necessarily have to be physically configured as illustrated. In other words, the distribution and integration of each device and component is not limited to that illustrated, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various usage conditions, etc. [Explanation of symbols]

[0103] 1, S01 to S18, AA, BB, CC, DD Particle Sensor 2. Human presence sensor 3 Fan 4. HEPA 5,14 Control section 10 Analysis device 11 Input section 12 Display section 13 Storage section 15 Estimated particle concentration calculation section 16 Dust Source Identification Section A01~A18,EP Work Area CR Clean Room D Sensitivity Matrix DA Time Series Data Section E Ea Top EB Parts Yard FCU heat exchange device FFU air purifier P Unmeasured point SC, SC1, SC2 Work process schedule W weight matrix

Claims

1. A dust source estimation method for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas possibly generating dust are arranged, comprising: a weight matrix generating step of generating a weight matrix indicating weight coefficients between each work area and each particle sensor, the weight coefficient being larger as the distance between the position of each work area and the position of each particle sensor becomes shorter; a particle concentration detection step in which each particle sensor detects a particle concentration; an estimated particle concentration calculation step of calculating, for all work areas, a value obtained by multiplying the weight coefficients of the particle sensors for one work area by the particle concentrations detected by the particle sensors, and dividing the sum of the weight coefficients of the particle sensors for the one work area by the sum of the weight coefficients of the particle sensors for the one work area, as an estimated particle concentration for the one work area, using the weight matrix; a dust source estimation step of calculating, for each work area, a dust contribution amount of each work area by dividing an estimated particle concentration of the work area by a sum of the estimated particle concentrations of all work areas, and estimating a work area that is a dust source based on the dust contribution amount; A dust source estimation method comprising:

2. A dust source estimation method for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas possibly generating dust are arranged, comprising: a weight matrix generating step of generating a weight matrix showing the weight coefficients between each work area and each particle sensor, the weight coefficient being increased as the particle concentration increases, based on the particle concentration detected by each particle sensor when a unit amount of dust is generated from the work area; a particle concentration detection step in which each particle sensor detects a particle concentration; an estimated particle concentration calculation step of calculating, for all work areas, a value obtained by multiplying the weight coefficients of the particle sensors for one work area by the particle concentrations detected by the particle sensors, and dividing the sum of the weight coefficients of the particle sensors for the one work area by the sum of the weight coefficients of the particle sensors for the one work area, as an estimated particle concentration for the one work area, using the weight matrix; a dust source estimation step of calculating, for each work area, a dust contribution amount of each work area by dividing an estimated particle concentration of the work area by a sum of the estimated particle concentrations of all work areas, and estimating a work area that is a dust source based on the dust contribution amount; A dust source estimation method comprising:

3. 2. The method for estimating a particle source according to claim 1, wherein the weighting coefficient is an inverse of a value obtained by raising the distance between the work area and the particle sensor to a power, and the value of the power is a value between 1 and 5.

4. 3. The dust source estimating method according to claim 1, wherein the dust source estimating step estimates a dust source as a dust source when the dust contribution amount is equal to or greater than a predetermined value.

5. 3. The dust source estimating method according to claim 1, further comprising the step of displaying a relationship between each work type and the dust contribution amount based on a plurality of work process schedules performed in each work area and time-series data of particle concentrations detected by each particle sensor during a work period of the work process schedules.

6. An analysis device for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas possibly generating dust are arranged, comprising: a weight matrix indicating a weight coefficient between each work area and each particle sensor, the weight coefficient being larger as the distance between the position of each work area and the position of each particle sensor becomes shorter; an estimated particle concentration calculation unit that uses the weight matrix to perform a process for all work areas of calculating a value obtained by multiplying the weight coefficients of the particle sensors for one work area by the particle concentrations detected by each particle sensor, and dividing the sum of the weight coefficients of the particle sensors for the one work area by the sum of the weight coefficients of the particle sensors for the one work area, as an estimated particle concentration for the one work area; a dust source estimation unit which calculates, for each work area, a dust contribution amount of each work area by dividing an estimated particle concentration of the work area by a sum of the estimated particle concentrations of all work areas, and estimates a work area which is a dust source based on the dust contribution amount; An analysis device comprising:

7. An analysis device for estimating a work area as a dust source in a clean room in which a plurality of particle sensors and a plurality of work areas possibly generating dust are arranged, comprising: a weight matrix indicating a weight coefficient between each work area and each particle sensor, the weight coefficient being larger as the particle concentration increases, the weight coefficient being obtained by performing a process for each work area to determine a weight coefficient between the work area and each particle sensor based on particle concentration detected by each particle sensor when a unit amount of dust is generated from the work area; an estimated particle concentration calculation unit that uses the weight matrix to perform a process for all work areas of calculating a value obtained by multiplying the weight coefficients of the particle sensors for one work area by the particle concentrations detected by each particle sensor, and dividing the sum of the weight coefficients of the particle sensors for the one work area by the sum of the weight coefficients of the particle sensors for the one work area, as an estimated particle concentration for the one work area; a dust source estimation unit which calculates, for each work area, a dust contribution amount of each work area by dividing an estimated particle concentration of the work area by a sum of the estimated particle concentrations of all work areas, and estimates a work area which is a dust source based on the dust contribution amount; An analysis device comprising:

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