CN109839967B - Self-tuning PID energy-saving temperature control method and module - Google Patents
Self-tuning PID energy-saving temperature control method and module Download PDFInfo
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Abstract
The invention relates to a self-tuning PID energy-saving temperature control method, which comprises the steps of firstly setting a target temperature value, a control period, a sampling period and a threshold temperature difference, improving load heating power, obtaining a current region temperature sampling value of a controlled object, calculating a temperature rise rate by combining the sampling period, adjusting and calculating PID control parameters by a Particle Swarm Optimization (PSO) and Differential Evolution (DE) combined PSO-DE algorithm, then adjusting the duty ratio of a control period output pulse according to an operation result, further adjusting the heating power output, enabling the sampled temperature sampling value to be approximately equal to the actual temperature value of the controlled object, reducing the temperature error caused by heating inertia, simultaneously accurately controlling the heating power and having an obvious energy-saving effect; a self-tuning PID energy-saving temperature control module comprises the self-tuning PID energy-saving temperature control method, can accurately control heating power, and has an obvious energy-saving effect.
Description
Technical Field
The invention relates to the field of energy-saving temperature control, in particular to a self-tuning PID energy-saving temperature control method and a module.
Background
With the rapid development of social economy, the modern industrial energy consumption is continuously increased along with the expansion of production scale, wherein the heating type energy consumption accounts for a great proportion in the industrial load energy consumption, and in order to realize the development target of energy conservation and consumption reduction and simultaneously improve the heating process level, the requirements on the temperature control precision and the energy conservation of the heating type load are higher and higher.
In modern industrial production, a PID algorithm with the advantages of simple structure, easy realization, quick response and the like is generally adopted for temperature control. The traditional PID control is combined with the dynamic characteristic of a controlled object, manual debugging is carried out by the experience of experts, parameters are not changed after adjustment, and the limitation is high. With diversification of temperature control environment and requirements and increasingly complex control systems, improved PID control algorithms such as a Z-N self-setting algorithm appear, parameter setting calculation is directly completed according to the control effect of a control object, although the temperature control precision is improved, the set parameters need to be solidified and stored in an external storage in advance.
Aiming at the existing literature retrieval discovery, the literature self-tuning PID temperature control research based on PSO (Seaman soldiers; chemical automation and instruments) provides a self-tuning PID temperature control method based on PSO, compared with the traditional PID algorithm, the method is relatively simple, the temperature control precision is high, complex programming is not needed, and the defect of local optimal solution of the PSO algorithm cannot be overcome; the document intelligent temperature fuzzy control PID system design proposes a fuzzy control system design scheme, the dynamic performance is good, but the fuzzy rule corresponding to high-precision control is complicated. Meanwhile, most of the existing documents research methods for improving temperature control precision, and the attention on the methods for organically combining the improvement of the temperature control precision and the reduction of heating energy consumption is less.
Disclosure of Invention
Aiming at the defects, the invention provides a self-tuning PID energy-saving temperature control method, which is based on the self-tuning of a PSO-DE algorithm and combines the advantage of strong local search capability of a differential evolution algorithm, solves the problem that the PSO algorithm is easy to fall into local optimization, then utilizes an adjusted PID parameter to carry out operation, converts an operation result into a duty ratio of a control period output pulse, and further adjusts the output of heating power, so that the sampled temperature sampling value is approximately equal to the actual temperature value of a controlled object, the temperature error caused by heating inertia is reduced, in addition, because the heating power is improved, the duty ratio of the output pulse is reduced, and the temperature control precision is improved, and meanwhile, the energy-saving effect is remarkable.
The invention also provides a self-tuning PID energy-saving temperature control module which can simultaneously improve the temperature control precision and reduce the heating energy consumption.
A self-tuning PID energy-saving temperature control method comprises the following steps:
the method comprises the following steps: initializing data, and setting a target temperature value, a control period, a sampling period and a threshold temperature difference;
step two: the heating power of the load is increased so as to increase the temperature of the target area of the controlled object at the current moment;
step three: acquiring a target area temperature sampling value of a controlled object at the current moment;
step four: calculating the temperature rise rate according to the temperature sampling value obtained in the third step and the sampling period;
step five: carrying out PSO-DE-based PID parameter self-tuning according to the temperature sampling value and the temperature rise rate at the current moment;
step six: calculating according to the adjusted PID parameters, and adjusting the load heating power output to complete primary power adjustment;
step seven: and repeating the third step to the sixth step until the target temperature value is reached.
In the self-tuning PID energy-saving temperature control method, in the first step, in order to ensure the temperature control precision, the control period and the sampling period are mainly set according to the volume of the controlled object, the initial temperature and the environmental heat dissipation rate.
According to the self-tuning PID energy-saving temperature control method, when the difference between the set target temperature value and the current temperature sampling value is the threshold temperature difference, the self-tuning PID energy-saving temperature control method starts to be executed.
According to the self-tuning PID energy-saving temperature control method, the load heating power is increased by increasing the voltage of the heating power supply.
In the fourth step, the temperature rise rate is H, and the formula for calculating the temperature rise rate H is as follows:
wherein: tcon is the temperature sampling period, Tc is the current temperature sampling value, and Tb is the temperature sampling value before one sampling period.
In the fifth step of the self-tuning PID energy-saving temperature control method, the PID parameter self-tuning step based on PSO-DE comprises the following steps:
a1, setting initial values and ranges of PID parameters Kp, Ki and Kd;
a2, setting the particle number N, the dimension Nl, the iteration times Tpso, the change coefficient alpha and the acceleration constant beta of the improved rapid particle swarm algorithm;
a3, initializing a particle group, and randomly generating an initial position of each particle between 0 and 1;
a4, judging the stability of the closed-loop system of each particle Kp, Ki and Kd at the current temperature and temperature rise rate, if so, obtaining the steady-state error ess, the adjusting time ts, the rising time tr and the overshoot sigma% of the particle to the step response according to a set value, calculating the fitness of each particle, and searching the global optimal fitness particle, wherein the position of the particle is marked as the gbest, and the optimal fitness is marked as the fbest;
a5, updating the positions of the particles, carrying out differential evolution optimization, recalculating the fitness function of the particles, searching the global optimal fitness particles and updating the gbest and fbest; the expression for the location update is as follows:
Xi(t)＝Xi(t-1)+β·[gbest-Xi(t-1)]
+α·rand(Nl).*scale
wherein: xi represents the position vector of the ith particle, Xi (t) represents the position vector of the ith particle in the t iteration, Xi (t-1) represents the position vector of the ith particle in the t-1 iteration, rand (Nl) represents a random number vector of 0 to 1 for generating Nl dimensions, and scale represents a variable change scale vector of Nl dimensions; alpha is a variation coefficient used for adjusting the variation amplitude of the random variation term; beta is an acceleration constant, and the range of the flying distance of the particles to the global optimal position is adjusted;
a6, determining whether the termination condition is satisfied:
t＝Tpso
if yes, the flow ends, otherwise, the flow returns to the step A4.
In the self-tuning PID energy-saving temperature control method, the smaller the fitness function is, the better the particle fitness is, and the fitness function is specifically calculated as follows:
wherein: k1, k2, k3 and k4 are weights of the performance indexes respectively and are 30, 10, 20 and 40 according to the actual value of the system.
In the self-tuning PID energy-saving temperature control method, in step a5, the differential evolution optimization specifically comprises the following steps:
b1, obtaining an intermediate individual vi (t) through mutation operation, wherein the formula is as follows:
vi(t)＝gbest(t)+m·(Xr1(t)-Xr2(t))
wherein vi (t) is the flight speed of the ith particle, and m ∈ [0,2] is a weighting factor;
b2, obtaining a new population through hybridization operation, and increasing the diversity of the particle population, wherein the formula is as follows:
wherein ui (t) is the new subject obtained, CR∈[0,1]Is the variation probability;
b3, calculating fitness function value of new individual after hybridization operation, thereby determining whether to select variant individual, the formula is as follows:
wherein the content of the first and second substances,
as a fitness function.
In the sixth step of the self-tuning PID energy-saving temperature control method, the heating power output is adjusted to convert the PID operation result into an output pulse duty ratio in a control period, so as to control the output of the heating power.
A self-tuning PID energy-saving temperature control module adopting the self-tuning PID energy-saving temperature control method comprises the following steps:
the temperature sampling unit is used for acquiring a temperature sampling value of the current area of the controlled object;
the load heating unit is used for heating the current area of the controlled object;
the power lifting unit is respectively electrically connected with the heating power source and the load heating unit and is used for lifting the load heating power;
and the PID operation unit is electrically connected with the power boosting unit and is used for converting the PID operation result into an output pulse duty ratio in a control period so as to control the output of the heating power.
The invention has the following beneficial effects:
a self-tuning PID energy-saving temperature control method comprises the steps of firstly setting a target temperature value, a control period, a sampling period and a threshold temperature difference, improving load heating power, obtaining a temperature sampling value of a current area of a controlled object, calculating a temperature rise rate by combining the sampling period, adjusting and operating PID control parameters by a particle swarm algorithm (PSO) and a differential evolution algorithm (DE) combined PSO-DE algorithm, adjusting the duty ratio of output pulses of one control period according to an operation result, further adjusting the output of the heating power, enabling the sampled temperature sampling value to be approximately equal to an actual temperature value of the controlled object, and reducing temperature errors caused by heating inertia; the self-tuning PID energy-saving temperature control module comprises the self-tuning PID energy-saving temperature control method, and because the heating power is improved, the duty ratio of output pulses of the energy-saving temperature control module is reduced, the heating power is accurately controlled, and the energy-saving effect is obvious.
Drawings
FIG. 1 is a flow chart of a self-tuning PID energy-saving temperature control method of the invention;
FIG. 2 is a block diagram of the self-tuning PID energy-saving temperature control module of the present invention;
FIG. 3 is a diagram of the output pulses of the modules heated by the commercial power using the conventional PID algorithm and by the self-tuning PID energy-saving temperature control method of the present invention;
fig. 4 is a temperature variation diagram of a controlled object heated by a conventional PID algorithm and by the self-tuning PID energy-saving temperature control method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples.
Referring to fig. 1, a self-tuning PID energy-saving temperature control method includes the following steps:
the method comprises the following steps: initializing data, and setting a target temperature value, a control period, a sampling period and a threshold temperature difference;
step two: the heating power of the load is increased so as to increase the temperature of the target area of the controlled object at the current moment;
step three: acquiring a target area temperature sampling value of a controlled object at the current moment;
step four: calculating the temperature rise rate according to the temperature sampling value obtained in the third step and the sampling period;
step five: carrying out PSO-DE-based PID parameter self-tuning according to the temperature sampling value and the temperature rise rate at the current moment;
step six: calculating according to the adjusted PID parameters, and adjusting the load heating power output to complete primary power adjustment;
step seven: and repeating the third step to the sixth step until the target temperature value is reached.
In order to ensure the temperature control precision, the control period and the sampling period are mainly set according to the volume of the controlled object, the initial temperature and the environmental heat dissipation rate. In the third step, when the difference between the set target temperature value and the current time temperature sampling value is the threshold temperature difference, the self-tuning PID energy-saving temperature control method starts to execute. In step two, the load heating power is increased by increasing the heating power voltage.
In the fourth step, the temperature rise rate is H, and the formula for calculating the temperature rise rate H is:
wherein: tcon is the temperature sampling period, Tc is the current temperature sampling value, and Tb is the temperature sampling value before one sampling period.
In the fifth step, the PID parameter self-tuning step based on the PSO-DE comprises the following steps:
a1, setting initial values and ranges of PID parameters Kp, Ki and Kd;
a2, setting the particle number N, the dimension Nl, the iteration times Tpso, the change coefficient alpha and the acceleration constant beta of the improved rapid particle swarm algorithm;
a3, initializing a particle group, and randomly generating an initial position of each particle between 0 and 1;
a4, judging the stability of the closed-loop system of each particle Kp, Ki and Kd at the current temperature and temperature rise rate, if so, obtaining the steady-state error ess, the adjusting time ts, the rising time tr and the overshoot sigma% of the particle to the step response according to a set value, calculating the fitness of each particle, and searching the global optimal fitness particle, wherein the position of the particle is marked as the gbest, and the optimal fitness is marked as the fbest;
a5, updating the positions of the particles, carrying out differential evolution optimization, recalculating the fitness function of the particles, searching the global optimal fitness particles and updating the gbest and fbest; the expression for the location update is as follows:
Xi(t)＝Xi(t-1)+β·[gbest-Xi(t-1)]
+α·rand(Nl).*scale
wherein: xi represents the position vector of the ith particle, Xi (t) represents the position vector of the ith particle in the t iteration, Xi (t-1) represents the position vector of the ith particle in the t-1 iteration, rand (Nl) represents a random number vector of 0 to 1 for generating Nl dimensions, and scale represents a variable change scale vector of Nl dimensions; alpha is a variation coefficient used for adjusting the variation amplitude of the random variation term; beta is an acceleration constant, and the range of the flying distance of the particles to the global optimal position is adjusted;
a6, determining whether the termination condition is satisfied:
t＝Tpso
if yes, the flow ends, otherwise, the flow returns to the step A4.
Further, the smaller the fitness function is, the better the particle fitness is, and the fitness function is specifically calculated as follows:
wherein: k1, k2, k3 and k4 are weights of the performance indexes respectively and are 30, 10, 20 and 40 according to the actual value of the system.
In step a5, the differential evolution optimization method comprises the following steps:
b1, obtaining an intermediate individual vi (t) through mutation operation, wherein the formula is as follows:
vi(t)＝gbest(t)+m·(Xr1(t)-Xr2(t))
wherein vi (t) is the flight speed of the ith particle, and m ∈ [0,2] is a weighting factor;
b2, obtaining a new population through hybridization operation, and increasing the diversity of the particle population, wherein the formula is as follows:
wherein ui (t) is the new subject obtained, CR∈[0,1]Is the variation probability;
b3, calculating fitness function value of new individual after hybridization operation, thereby determining whether to select variant individual, the formula is as follows:
wherein the content of the first and second substances,
as a fitness function.
In the sixth step, the heating power output is adjusted to convert the PID operation result into an output pulse duty ratio in a control period, so as to control the output of the heating power.
The self-tuning PID energy-saving temperature control method solves the problem that the PSO algorithm is easy to fall into local optimization by self-tuning based on the PSO-DE algorithm and combining the advantage of strong local search capability of the differential evolution algorithm, then utilizes the adjusted PID parameter to carry out operation, converts the operation result into the duty ratio of a control period output pulse, further adjusts the output of heating power, enables the sampled temperature sampling value to be approximately equal to the actual temperature value of a controlled object, reduces the temperature error caused by heating inertia, and has obvious energy-saving effect while improving the temperature control precision because the heating power is improved and the duty ratio of the output pulse is reduced
Referring to fig. 2, the energy-saving temperature control module of the present invention, using the above-mentioned self-tuning PID energy-saving temperature control method, can simultaneously improve the temperature control precision and reduce the heating energy consumption, as shown in fig. 2, the energy-saving temperature control module of the present invention includes:
the temperature sampling unit is used for acquiring a temperature sampling value of the current area of the controlled object;
the load heating unit is used for heating the current area of the controlled object;
the power lifting unit is respectively electrically connected with the heating power source and the load heating unit and is used for controlling the heating power of the load;
and the PID operation unit is electrically connected with the power boosting unit and is used for converting the PID operation result into an output pulse duty ratio in a control period so as to control the output of the heating power.
In practical application, for example, taking heating water in industrial production as an example, the volume of an industrial water tank is 1m3, the initial temperature is 40 ℃, the target temperature is 60 ℃, the control period and the sampling period are both 2s, the threshold temperature difference is 20 ℃, heating is started until the temperature reaches the target value, two heating modes, namely conventional PID algorithm commercial power heating and the self-tuning PID energy-saving temperature control method are adopted, and experimental results are shown in the following table.
TABLE 1 results of two heating regimes
Referring to fig. 3, 4 and table 1, it can be seen that, compared with the conventional PID algorithm commercial power heating, the heating by the self-tuning PID energy-saving temperature control method of the present invention has the advantages that the heating power is increased, the duty ratio of the output pulse of the energy-saving temperature control module is reduced, the heating power is accurately controlled, the temperature control precision is effectively improved, and through calculation, the energy consumption of the heating by the self-tuning PID energy-saving temperature control method of the present invention is reduced by 17%, the heating time is shortened by about 68.3%, and the energy-saving effect is significant.
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all equivalent structural changes made by using the contents of the present specification and the drawings can be directly or indirectly applied to other related technical fields, and the same shall be included in the scope of the present invention.
Claims (9)
1. A self-tuning PID energy-saving temperature control method is characterized by comprising the following steps:
the method comprises the following steps: initializing data, and setting a target temperature value, a control period, a sampling period and a threshold temperature difference;
step two: the heating power of the load is increased so as to increase the temperature of the target area of the controlled object at the current moment;
step three: acquiring a target area temperature sampling value of a controlled object at the current moment;
step four: calculating the temperature rise rate according to the temperature sampling value obtained in the third step and the sampling period;
step five: carrying out PSO-DE-based PID parameter self-tuning according to the temperature sampling value and the temperature rise rate at the current moment;
step six: calculating according to the adjusted PID parameters, and adjusting the load heating power output to complete primary power adjustment;
step seven: repeating the third step to the sixth step until the target temperature value is reached;
in the fifth step, the PID parameter self-tuning based on PSO-DE comprises the following steps:
a1, setting PID parameter Kp、Ki、KdAn initial value and a range;
a2, setting the particle number N and the dimension N of the improved fast particle swarm algorithmlNumber of iterations TpsoThe coefficient of variation α, the acceleration constant β;
a3, initializing a particle group, and randomly generating an initial position of each particle between 0 and 1;
a4, for each particle Kp、Ki、KdJudging that the temperature is at the current temperature and the temperature rise rateThe stability of the closed loop system, if stable, the steady state error (ess) and the adjusting time (t) of the closed loop system to the step response are obtained according to the set valuesRising time trCalculating the fitness of each particle, and searching for a global optimal fitness particle, wherein the position of the global optimal fitness particle is marked as gbest, and the optimal fitness is marked as fbest;
a5, updating the positions of the particles, carrying out differential evolution optimization, recalculating the fitness function of the particles, searching the global optimal fitness particles and updating the gbest and fbest; the expression for the location update is as follows:
Xi(t)＝Xi(t-1)+β·[gbest-Xi(t-1)]+α·rand(Nl).*scale
wherein: xiDenotes the position vector, X, of the ith particlei(t) denotes the position vector of the ith particle at the tth iteration, Xi(t-1) denotes the position vector of the ith particle at the t-1 iteration, rand (N)l) Represents the generation of NlRandom number vector of 0 to 1 dimension, scale representing NlChanging a scale vector of a variable to be solved of the dimension; alpha is a variation coefficient used for adjusting the variation amplitude of the random variation term; beta is an acceleration constant, and the range of the flying distance of the particles to the global optimal position is adjusted;
a6, determining whether the termination condition is satisfied:
t＝Tpso
if yes, the flow ends, otherwise, the flow returns to the step A4.
2. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in the first step, in order to ensure the temperature control accuracy, the control period and the sampling period are mainly set according to the volume of the controlled object, the initial temperature and the environmental heat dissipation rate.
3. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in step three, when the difference between the set target temperature value and the current time temperature sampling value is the threshold temperature difference, the self-tuning PID energy-saving temperature control method starts to execute.
4. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in the second step, the load heating power is increased by increasing a heating power supply voltage.
5. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in the fourth step, the temperature rise rate is H, and the formula for calculating the temperature rise rate H is as follows:
wherein: t is tconFor a temperature sampling period, TcFor the current temperature sample value, TbIs the temperature sample value one sample period before.
6. The self-tuning PID energy-saving temperature control method according to claim 1, wherein the smaller the fitness function is, the better the particle fitness is, and the fitness function is specifically calculated as follows:
wherein: k is a radical of1、k2、k3And k4The weights of the performance indexes are respectively 30, 10, 20 and 40 according to the actual value of the system.
7. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in the step A5, the specific steps of differential evolution optimization are as follows:
b1, obtaining the intermediate individual v by mutation operationi(t), the formula is as follows:
vi(t)＝gbest(t)+m·(Xr1(t)-Xr2(t))
wherein v isi(t) is the flight velocity of the ith particle, and m ∈ [0,2]]Is a weighting factor; gbest (t) is the local optimum, Xr1(t) is the position value at time r1, Xr2(t) is the position value at time r 2.
B2, obtaining a new population through hybridization operation, and increasing the diversity of the particle population, wherein the formula is as follows:
wherein u isi(t) is the new individual obtained, CR∈[0,1]Is the variation probability; xi min(t) is the minimum value at time i, Xi max(t) is the maximum value of the position at time i.
B3, calculating fitness function value of new individual after hybridization operation, thereby determining whether to select variant individual, the formula is as follows:
wherein the content of the first and second substances,
as a fitness function.
8. The self-tuning PID energy-saving temperature control method according to claim 1, wherein in the sixth step, the heating power output is adjusted to convert the PID operation result into an output pulse duty ratio in a control period, thereby controlling the output of the heating power.
9. A self-tuning PID energy-saving temperature control module comprising the self-tuning PID energy-saving temperature control method according to any of claims 1-8, characterized by comprising:
the temperature sampling unit is used for acquiring a temperature sampling value of the current area of the controlled object;
the load heating unit is used for heating the current area of the controlled object;
the power lifting unit is respectively electrically connected with the heating power source and the load heating unit and is used for controlling the heating power of the load;
and the PID operation unit is electrically connected with the power boosting unit and is used for converting the PID operation result into an output pulse duty ratio in a control period so as to control the output of the heating power.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202835760U (en) * | 2012-09-12 | 2013-03-27 | 苏州赛华仪控股份有限公司 | Thermostatic water tank |
| CN104019526A (en) * | 2014-06-24 | 2014-09-03 | 河海大学常州校区 | Fussily self-adaptive PID temperature and humidity control system and method based on improved PSO (Particle Swarm Optimization) algorithm |
| CN105114242A (en) * | 2015-07-22 | 2015-12-02 | 重庆邮电大学 | Hydro governor parameter optimization method based on fuzzy self-adaptive DFPSO algorithm |
| CN107272403A (en) * | 2017-06-14 | 2017-10-20 | 浙江师范大学 | A kind of PID controller parameter setting algorithm based on improvement particle cluster algorithm |
| CN108121208A (en) * | 2017-12-25 | 2018-06-05 | 国网江西省电力有限公司电力科学研究 | Based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods |
| CN109212965A (en) * | 2018-08-06 | 2019-01-15 | 广州云雷智能科技有限公司 | Floor heating temperature control system and method based on particle swarm optimization algorithm |
-
2019
- 2019-01-24 CN CN201910069498.XA patent/CN109839967B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202835760U (en) * | 2012-09-12 | 2013-03-27 | 苏州赛华仪控股份有限公司 | Thermostatic water tank |
| CN104019526A (en) * | 2014-06-24 | 2014-09-03 | 河海大学常州校区 | Fussily self-adaptive PID temperature and humidity control system and method based on improved PSO (Particle Swarm Optimization) algorithm |
| CN105114242A (en) * | 2015-07-22 | 2015-12-02 | 重庆邮电大学 | Hydro governor parameter optimization method based on fuzzy self-adaptive DFPSO algorithm |
| CN107272403A (en) * | 2017-06-14 | 2017-10-20 | 浙江师范大学 | A kind of PID controller parameter setting algorithm based on improvement particle cluster algorithm |
| CN108121208A (en) * | 2017-12-25 | 2018-06-05 | 国网江西省电力有限公司电力科学研究 | Based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods |
| CN109212965A (en) * | 2018-08-06 | 2019-01-15 | 广州云雷智能科技有限公司 | Floor heating temperature control system and method based on particle swarm optimization algorithm |
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