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The paper discusses the features of the Biomass Boiler drum water level. Conventional PID Control System can not reach a satisfaction result in nonlinearity and time different from Biomass Boiler Drum Water Control System. In this study, a kind of fuzzy self-adaptive PID controller is described and this controller is used in biomass boiler’s drum wa ter level control system. Using the simulink tool of MATLAB simulation software to simulate the fuzzy adaptive PID and conventional PID control system, the result of the comparison shows that the fuzzy self-adaptive PID has the strong anti-jamming, flexibility and adaptability as well as the higher control precision in Biomass Boiler Drum Water.

The biomass energy is the energy that can be transformed solar energy into chemical energy and stored in the organisms internal with the help of the green plant photosynthesis directly or indirectly. Modern biomass is seen as one of the most promising renewable energy sources in the near future. Using biomass to generate energy carries like heat, electricity and gaseous and liquid fuels can contribute significantly to the reduction of greenhouse gas emissions, as shown in many studies and project in most scenarios about potential development of energy and economic systems [

In the biomass boiler combustion system, the boiler drum water level is an important parameter and it is a sign to measure whether boiler steaming-water system is balance. Maintaining the boiler drum water level in the specified range is a necessary condition and one of the important indexes for the boiler safe operation. If the water level is too low, the water of bubbles lessens at the same time the load largeness. The results are the vaporization speed is very fast and the change rate of water volume is also high. The boiler will explode if it isn’t controlled in time [

Biomass boiler drum water level is important monitoring parameters in the boiler operation. It indirectly reflects the equilibrium relation between boiler steam load and water inflow. Keeping the right level of the steam pocket is the essential condition to ensure the safety of boiler and steam turbine. Because of the change of the steam load and the feeding water pressure, there is “false water level” phenomenon appearing in the boiler. In the operation, when steam load increases, evaporation is greater than the feed water. Water level rises rapidly. It caused by steam volume in the steam water mixture increasing rapidly. This phenomenon called “false liquid level”. When the steam load increases sharply, the water level drops after rising for a while. When the steam load decreases sharply, the water level rises after dropping for a while. “False liquid level” phenomenon appears when this controlled variable is disturbance. If the feed water using single loop system which viewed water level as controlled variable, the adjuster will opposite the direction of the water flow and the direction of the change in load [

Conventional PID control can’t solve the “false liquid level” phenomenon well. However, if the fuzzy control arithmetic is put into the “false liquid level”, the water level of steam boiler can be controlled well.

Fuzzy self-adjusting PID controller is composed of adjustable PID controller and fuzzy controller. The core is fuzzy controller. It contains fuzzification, repository, fuzzy inference, defuzzification, and input/output quantification and so on. Fuzzy logic is a rule which can map a space-input to another space-output. In engineering application, fuzzy logic has the following characters: 1) Fuzzy logic is flexible; 2) Fuzzy logic is based on natural language, and the requirement for intensive reading of data is not very high; 3) Fuzzy logic can take full advantage of expert information; 4) Fuzzy logic is easy to combine with traditional control technique [

Fuzzy self-adjusting PID controller takes E (the error between feedback value and desired value of controlled station) and E_{c} (error rate) as input. Using fuzzy reasoning method it adjusts the PID parameters (K_{p}, K_{i}, K_{d}) which can meet the requirements of E and E_{c} for PID parameter self-setting in different time. Change the PID parameters on line by using the fuzzy rules; these functions form the self-setting fuzzy PID controller. Its control system architecture is shown in

According to the precision and control requirements, it is appropriate that 7 levels are usually selected. The range of fuzzy analects for input variable E, E_{c} is usually [−3, 3], which which is shown in

analects for the output of K_{p} is [−0.3, 0.3] and the range of fuzzy analects for the output of K_{d} is [−0.3, 0.3], which are shown in _{i} is [−0.06, 0.06], which is shown in

The three parameters of PID arithmetic will influence stability, response speed, overshoot and steady precision of the system [_{c} (error rate), K_{p}, K_{i} and K_{d} are following items.

1) When E is relatively big, we should take the bigger K_{p} and the smaller K_{d} to make sure that the system has a good tracking performance. We should also strictly take an integral action to avoid the system has a larger overshoot. Usually take K_{i} = 0;

2) When E and E_{c} is quite suitable, we should make the K_{p} smaller to make sure the system has a smaller overshoot. On this occasion, we should take the suitable K_{i} while K_{d} has important influence on system;

3) When E is very small, we should take the bigger K_{p} and the bigger K_{i} to make sure that the system has a stable performance. We should also take a suitable K_{d} which is based on E_{c} to avoid the system oscillation appeared in set value. When E_{c} is small, take a bigger K_{d}; When E_{c} is big, take a smaller K_{d}. We make some control-rule figures in the following tables according to the setting principle and the simulation test.

It needs you to transform the output energy of fuzzy controller into accurate quantity after the design of control rules. Then the data can be sent to control actuator. The design realized the organization of fuzzy quantity by using of method of weighted mean [_{p} .In the online operation, it works out the deviation and the deviation rate in current time by constantly testing the output data of the system. Then fuzzifier them and get E, E_{c}, you can get

_{p} fuzzy control rules.

_{i} fuzzy control rules.

_{i} fuzzy control rules.

the adjusted quantity of the three parameters through control rules. All these steps are the adjustments to the controller parameters.

The model is constructed in the environment of Matlab/ Simulink, which is composed of fuzzy controller, PID control module, controlled member and input/output, using Simulink, Simulink extras, fuzzy logic and toolbox.

In MATLAB, a new FIS file is made by the FIS editor to confirm the structure and membership of fuzzy controller. As shown in

Input the membership functions and fuzzy rule of E,

E_{c}, K_{p}, K_{i}, K_{d} in turns. And “and method” is regarded as min, “or method” as max, “implication” as min, “aggregation” as max and “defuzzification” as centroid. The file is saved and loaded into workspace when you want to simulate the module.

Select a boiler drum as controlled object. And the transfer function of feeding water flow and water level is

, justification factor K_{e} = 0.9, K_{ec} =

0.1. Defuzzification factor K_{1} = 3, K_{2} = 1.2, K_{3} = 0.01, PID initial value K_{p} = 4, K_{i} = 3, K_{d} = 1.5.

In this section, the step response simulations by using fuzzy PID control and PID control respectively. Furthermore, authors make some comparisons between them. The simulation results are shown by Figures 6-9.

It can be seen that the fuzzy adaptive PID controller makes the speed of response much quicker, the overshoot much smaller, and the oscillation time much shorter such that the states soon reach the level of stability than that of

the PID controller. In a word, the fuzzy adaptive PID controller shows good dynamic and static performance indicators. The fuzzy adaptive PID control algorithm merges the advantages of the PID control and the fuzzy control, and it provides a good control method for the complex systems and the systems with higher demand dynamic process.