CN112526889A - Optimization algorithm of PID-P temperature controller of sulfur-containing flue gas heat exchange system - Google Patents
Optimization algorithm of PID-P temperature controller of sulfur-containing flue gas heat exchange system Download PDFInfo
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Abstract
The invention discloses a PID-P temperature controller optimization algorithm for a sulfur-containing flue gas heat exchange system, and belongs to the field of controller parameter setting strategies and algorithms. The method is characterized in that an inverse S-shaped inertial weight decreasing strategy is introduced on the basis of a basic particle swarm optimization algorithm to balance the global search capability and the local search capability of the algorithm; and the PIF-IITAE fitness function is provided on the basis of the PIF-ITAE fitness function to improve the excellence of the optimization algorithm. The PID-P temperature controller parameter of the phase change flue gas heat exchange system is set by using the improved particle swarm optimization algorithm, so that a large amount of manpower and time resources can be saved, and the precision of the PID-P temperature controller is improved.
Description
Technical Field
The invention belongs to the field of controller parameter setting strategies and algorithms, and particularly relates to a PID-P temperature controller optimization algorithm for a sulfur-containing flue gas heat exchange system.
Background
The controller parameter tuning is the core content of the control system design. The traditional parameter setting method of the cascade controller comprises a successive approximation method, a two-step setting method, a one-step setting method and the most common empirical trial and error method, wherein the methods are all based on manpower and have the defects of complex process and easiness in oscillation and overshoot, and particularly when the target is high-order and nonlinear. Meanwhile, the parameters of the manual setting controller can only be respectively set by different parameters, but the stability margin of each parameter also depends on other values, so that the optimal parameters are often difficult to set by a manual method.
In the 80 s of the 20 th century, a relay feedback technology is applied to the PID controller self-tuning of an industrial process, but the information of a critical point estimated by a standard relay feedback method is not accurate enough, so that a large error is generated when a high-order or large-time-lag object is met, and the response of a system is deteriorated; meanwhile, the relay feedback method also depends on a parameter setting formula.
At the end of the 20 th century, some intelligent PID controllers are newly developed, and the intelligent control technology is applied to the parameter setting process of the controllers, so that adverse factors such as large time lag, strong coupling and large disturbance in engineering are overcome.
The current more common controller parameter setting intelligent algorithms include a fuzzy algorithm, a neural network algorithm, a genetic algorithm, a particle swarm optimization algorithm and the like. The fuzzy algorithm completely depends on the establishment of an expert rule base; the particle swarm optimization algorithm and the genetic algorithm have many common points, both of which use fitness values to evaluate the individual goodness and the certain random search, but the particle swarm optimization algorithm determines the search according to the speed of the particle swarm optimization algorithm, has no obvious intersection and variation of the genetic algorithm, has a good mechanism to effectively balance the diversity and the directionality of the search process, and has simple principle, easy realization and less parameters needing to be adjusted, so the particle swarm optimization algorithm is widely applied to parameter optimization of a controller.
Disclosure of Invention
The invention aims to provide an optimization algorithm of a PID-P temperature controller of a sulfur-containing flue gas heat exchange system aiming at the problems in the prior art.
The technical scheme of the invention is as follows:
a PID-P temperature controller optimization algorithm of a sulfur-containing flue gas heat exchange system specifically comprises the following steps:
an inverse S-shaped inertial weight decreasing strategy and a PIF-IITAE fitness function are introduced on the basis of a basic particle swarm optimization algorithm to improve the advantages of the optimization algorithm.
The optimization algorithm process is as follows:
firstly, initializing the position and speed of each particle in a population, and respectively mapping the particle dimension to each parameter of a PID-P temperature controller in a phase change flue gas heat exchange system; secondly, updating the current optimal position of the ith particle in the jth dimension according to a fitness function, wherein i is a positive integer less than or equal to 50, and j is a positive integer less than or equal to 4; finally, when the fitness value accords with the convergence criterion, the PID-P temperature controller obtains the optimal parameter; otherwise, updating the position and the speed of the population according to the following formula, and simultaneously updating the current optimal position of each particle and the current optimal position of the population according to a fitness function respectively:
wherein v isijIs the velocity, x, of the ith particle in the jth dimensionijIs the position of the ith particle in the jth dimension, r1jAnd r2jIs distributed in each dimension in [0,1 ]]Two independent random constants in between, c1And c2Is a learning factor, c1Referred to as cognitive parameters, c2Called the social parameter, k the current iteration number, w the inertial weight, pijIs the current optimal position of the ith particle in the jth dimension; p is a radical ofgjIs the current optimal position of all particles in the j-th dimension.
Further, an inverse S-type inertial weight decreasing strategy, namely an IWS-ISF-type inertial weight decreasing strategy, is adopted, and is mainly used for balancing the global search capability and the local search capability of the algorithm. The IWS-ISF type inertia weight decreasing strategy adopts the following formula:
wherein, wstartRepresenting the upper value of the inertial weight, wendRepresents the lower limit of the inertial weight, Iter is the number of iterations, ItermaxK is a proportionality coefficient for the maximum number of iterations.
The unit step response of the PID-P temperature controller parameters is set based on different inertia weight decreasing strategies and particle swarm optimization algorithms, specific performance index parameters are shown in the table 1, performance index values of the three inertia weight decreasing strategies are close to each other, and when the inertia weights are in an inverted S-shaped decreasing strategy, the fitness value can approach to 1096.67 only by iterating for 10 times. The inertial weight is in an inverted S-shaped descending strategy, so that the algorithm can keep a longer-time maximum value at the initial stage, the global search capability of the algorithm is enhanced, a longer-time minimum value is kept at the later stage of iteration, the local search capability of the algorithm is enhanced, the global optimal position in a population is reserved, and the convergence speed of the algorithm is accelerated.
Further, a PIF-IITAE fitness function is adopted, the PIF-IITAE fitness function is based on the PIF-ITAE fitness function, an improved IITAE performance index is provided according to an ITAE performance index, and an overshoot sigma item is introduced into the ITAE performance index; meanwhile, in order to keep the original good process quality of ITAE, a weight coefficient f is introduced1And f2To control the relative importance of the original ITAE term and the overshoot term in the ITAE performance index. Wherein f is1Is 1;
，
is expressed by taking fITAEThe order of magnitude of the value; f. ofcIs constant, take 1.5; PIF is Performance index functionThe abbreviation of n; I. t, A and E represent integration, time, absolute value, and error, respectively. The PIF-IITAE fitness function is shown as follows:
wherein T represents the integration duration of the fitness function, T1setIs a set value T of the temperature of the flue gas outlet of the flue gas heat exchanger1And (t) is the outlet temperature value of the flue gas heat exchanger at t time, and t represents the running time of the controller.
Based on the unit step response of the system under different fitness function conditions, the specific performance index parameters are shown in table 2, and it can be seen that the IITAE index has a rapid and stable transition process and a small overshoot, and the IITAE index is a control system performance evaluation index with good engineering practicability and selectivity.
The invention has the following advantages:
the particle swarm algorithm determines searching according to own speed, obvious intersection and variation do not exist, the particle swarm algorithm has a good mechanism to effectively balance diversity and directionality of a searching process, and meanwhile, the particle swarm algorithm is simple in principle, easy to implement and less in parameters needing to be adjusted.
The PID-P temperature controller parameters of the phase change flue gas heat exchange system are optimized by using the particle swarm algorithm, so that a large amount of manpower and time resources can be saved, and the precision of the PID-P temperature controller is improved.
The PID-P temperature controller of the phase change flue gas heat exchange system is optimized by adopting a particle swarm algorithm based on an IWS-ISF type inertia weight decreasing strategy, the strategy strengthens the global search capability at the initial stage of the algorithm and the local search capability at the later stage of the algorithm, and the convergence speed is higher.
The particle swarm optimization algorithm based on the PIF-IITAE fitness function is adopted to set the PID-P temperature controller parameters of the phase change flue gas heat exchange system, so that the system has better dynamic and steady-state performance, the method is a control parameter optimization method with good engineering practicability and robustness, and design basis is provided for controlling the temperature of the flue gas at the outlet of the heat exchanger of the phase change flue gas heat exchange system.
Drawings
FIG. 1 is a system diagram of a PID-P temperature controller of a phase change flue gas heat exchange system of a specific embodiment;
FIG. 2 is a flow chart of a PID-P temperature controller particle swarm optimization algorithm of a specific embodiment;
FIG. 3 is a graph of unit step response of a PID-P temperature controller according to an embodiment.
Detailed Description
In order to more fully express the technical scheme provided by the invention, the following further description is carried out through specific examples:
a phase change recovery system for flue gas waste heat in front of a desulfurizing tower comprises two subsystems which are respectively defined as a phase change air heat exchange system and a phase change flue gas heat exchange system.
In the flue gas waste heat phase change recovery system, the lithium bromide dilute solution vaporizes liquid water in the phase change flue gas heat exchanger, and two-phase media containing steam and a lithium bromide concentrated solution enter a wall temperature regulator for gas-liquid separation; the steam directly enters the phase change air heat exchanger, is cooled by air into condensed water and is stored in a condensed water tank; the condensed water enters the wall temperature regulator through the electric regulating valve, is mixed with the lithium bromide concentrated solution to become dilute solution, and flows into the phase-change flue gas heat exchanger again.
The cascade PID-P temperature controller system diagram of the phase change flue gas heat exchange system is shown in figure 1, under the condition of constant pressure, the concentration of the lithium bromide aqueous solution directly influences the phase change temperature of the lithium bromide aqueous solution in the heat transfer process, the flow of condensed water from a condensed water tank is controlled by adjusting the opening of an actuator, namely an electric adjusting valve, and the concentration of the lithium bromide aqueous solution in a wall temperature regulator is indirectly controlled by a liquid level controller. When the acid dew point temperature of the sulfur-containing flue gas fluctuates greatly, the liquid level controller assists the temperature controller to adjust the wall temperature of the flue gas heat exchanger. In the figure, T10Indicating the flue gas inlet temperature, T, of the flue gas heat exchanger20Indicating wall temperature regulator or flue gas heat exchanger inletThe temperature of lithium bromide solution at the opening, h represents the liquid level of the wall temperature regulator, l represents the opening degree of the electric regulating valve, and q1Expressing the volume flow of the solution at the outlet of the electric regulating valve or the volume flow of the solution at the inlet of the wall temperature regulator, KDRepresenting the differential coefficient, K, of a PID controllerIIndicating the integral coefficient, K, of the PID controllerPIndicating the proportionality coefficient, K, of the PID controllerP0Representing the scaling factor of the cascaded controller.
And (3) setting parameters of a PID-P temperature controller of the sulfur-containing flue gas heat exchange system by adopting an inverse S-shaped inertia weight decreasing strategy and a particle swarm optimization algorithm improved by a PIF-IITAE fitness function.
The flow chart of the particle swarm optimization algorithm of the PID-P temperature controller of the sulfur-containing flue gas heat exchange system is shown in FIG. 2, wherein P isijIs the current optimal position of the ith particle in the jth dimension; p is a radical ofgjIs the current optimal position of all particles in the j-th dimension. Firstly, initializing the position and speed of each particle in the population, and respectively mapping the particle dimension to each parameter of a PID-P temperature controller in the phase change flue gas heat exchange system. Secondly, comparing the fitness value of the ith particle at the jth dimension position with the current optimal position of the particle, and updating according to a fitness function; then, the fitness values of the ith particle at the current optimal position of the jth dimension and all the particles at the current optimal position of the jth dimension are compared, and updating is carried out according to a fitness function; finally, when the iteration times are larger than the set maximum iteration times or the adaptability value is smaller than the set minimum adaptability value, the PID-P temperature controller obtains the optimal parameter KD，KI，KP，KP0(ii) a Otherwise, the positions and the speeds of the particles are updated according to a formula, and simultaneously the current optimal position of each particle and the current optimal position of the population are respectively updated according to a fitness function.
In order to verify the reliability of the new and improved particle swarm optimization algorithm, a unit step response graph of the PID-P temperature controller based on the particle swarm optimization algorithm is shown in FIG. 3, and it can be seen from the graph that the rise time difference between the original optimization algorithm and the improved optimization algorithm is not large, the adjustment time difference between the original optimization algorithm and the improved optimization algorithm is not large, but the overshoot of the improved optimization algorithm is obviously reduced compared with that of the original optimization algorithm. Obviously, the improved particle swarm optimization is effective for optimizing the parameters of the PID-P temperature controller of the sulfur-containing flue gas heat exchange system.
Claims (3)
1. The optimization algorithm of the PID-P temperature controller of the sulfur-containing flue gas heat exchange system is characterized in that:
an inverse S-shaped inertial weight decreasing strategy and a PIF-IITAE fitness function are introduced on the basis of a basic particle swarm optimization algorithm to improve the advantages of the optimization algorithm;
the optimization algorithm process is as follows:
firstly, initializing the position and speed of each particle in a population, and respectively mapping the dimension of each particle to each parameter of a PID-P temperature controller in a phase change flue gas heat exchange system; secondly, updating the current optimal position of the ith particle in the jth dimension according to a fitness function, wherein i is a positive integer less than or equal to 50, and j is a positive integer less than or equal to 4; finally, when the fitness value accords with the convergence criterion, the PID-P temperature controller obtains the optimal parameter; otherwise, updating the position and the speed of the population according to the following formula, and simultaneously updating the current optimal position of each particle and the current optimal position of the population according to a fitness function respectively:
wherein v isijIs the velocity, x, of the ith particle in the jth dimensionijIs the position of the ith particle in the jth dimension, r1jAnd r2jIs distributed in each dimension in [0,1 ]]Two independent random constants in between, c1And c2Is a learning factor, c1Referred to as cognitive parameters, c2Called the social parameter, k the current iteration number, w the inertial weight, pijIs the current optimal position of the ith particle in the jth dimension; p is a radical ofgjIs the current optimal position of all particles in the j-th dimension.
2. The PID-P temperature controller optimization algorithm of the sulfur-containing flue gas heat exchange system according to claim 1, wherein an inverse "S" type inertial weight decrement strategy, namely an IWS-ISF type inertial weight decrement strategy, is adopted to balance global search and local search capabilities of the algorithm, and the IWS-ISF type inertial weight decrement strategy adopts the following formula:
wherein, wstartRepresenting the upper value of the inertial weight, wendRepresents the lower limit of the inertial weight, Iter is the number of iterations, ItermaxK is a proportionality coefficient for the maximum number of iterations.
3. The PID-P temperature controller optimization algorithm of the sulfur-containing flue gas heat exchange system according to claim 1, wherein the adopted PIF-IITAE fitness function is improved on the basis of the PIF-ITAE fitness function, and an overshoot sigma term is introduced into the ITAE performance index according to the improved IITAE performance index provided by the ITAE performance index; meanwhile, in order to keep the original good process quality of ITAE, a weight coefficient f is introduced1And f2To control the relative importance of the original ITAE term and the overshoot term in the ITAE performance index, the PIF-ITAE fitness function is shown as follows:
wherein T represents the integration duration of the fitness function, T1setIs a set value T of the temperature of the flue gas outlet of the flue gas heat exchanger1And (t) is the outlet temperature value of the flue gas heat exchanger at t time, and t represents the running time of the controller.
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