Different control strategies for flow loop control in boiler


Boiler systems have numerous parameters that vary with respect to time and this may act as one of the main causes of reduction in boiler efficiency. The dynamic behavior of industrial plants heavily depends on disturbances and in particular on changes in operating point. The main input variables of a chemical plant are fuel, feed water and air. The outputs of the system are electrical power, steam pressure, steam temperature, flue gas.

In many industrial processes, control of liquid flow or temperature control is required. Boiler flow control system is a very complex system, because of nonlinearities and uncertainties in the system. There are various approaches to the design of the level controllers.

The SCADA is used to monitor the system, PLC (Programmable Logic Controller) is also used for the internal storage of instruction for the implementing function such as arithmetic, counting, timing, sequencing and logic to control through digital or analogue input/output modules various types of machines processes.

control of boiler flow via three methods PID, Fuzzy Logic Controller and PSO-PID. PID control

is one of the earlier control strategies. Also we use fuzzy logic and PSO-PID Controller. Performance analysis of PID, Fuzzy Logic Controller and PSO-PID has been done by the use of MATLAB and simulink.


A proportional–integral–derivative controller (PID controller) is a generic control loop feedback mechanism (controller) widely used in industrial control systems – a PID

is the most commonly used feedback controller. A PID controller calculates an "error" value as the difference between a measured process variable and a desired set point.

The controller attempts to minimize the error by adjusting the process control inputs. In the absence of knowledge of the underlying process, PID controllers are the best controllers. However, for best performance, the PID parameters used in the calculation must be tuned according to the nature of the system – while the design is generic, the parameters depend on the specific system. The PID controller calculation

(algorithm) involves three separate parameters, and is accordingly sometimes called three-term control: the proportional, the integral and derivative values, denoted P, I,

and D. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element. Heuristically, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future

errors, based on current rate of change. By tuning the three constants in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the set point and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability. Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are fairly common, since derivative action is sensitive to measurement noise, whereas the absence of an integral value may prevent the system from reaching its target value due to the control action.


Tuning Methods (Closed-Loop Methods):

Ziegler’s-Nicholas method:

Step 1: Reduce the integrator and derivative gains to 0.

Step 2: Increase Kp from 0 to some critical value Kp=Kc at which

sustained oscillations occur

Step 3: Note the value Kc and the corresponding period of

sustained oscillation, Tc

Step 4: Evaluate control parameters as prescribed by Ziegler and


Modified Ziegler’s-Nicholas method:

For some control loops the measure of oscillation, provide by ¼

decay ratio and the corresponding large overshoots for set-point

changes are undesirable therefore more conservative methods are

often preferable such as modified Z-N settings

Tyreus-luyben method:

Step 1-3: Same as steps 1 to 3 of Ziegler-Nichols method above

Step 4: Evaluate control parameters as prescribed by Tyreus and



PID controller is a standard control structure for classical control theory. But the performance is greatly distorted and the efficiency is reduced due to nonlinearity in the process plant. The fuzzy PID controllers are the natural extension of their conventional version, which preserve their linear structure of PID controller. The fuzzy PID controllers are designed using fuzzy logic control principle in order to obtain a new controller that possesses analytical formulas very similar to digital PID controllers. Fuzzy PID controllers have variable control gains in their linear structure. These variable gains are nonlinear function of the errors and changing rates of error signals. The main contribution of these variable gains in improving the control performance is that they are self- tuned gains and can adapt to rapid changes of the errors and rate of change of error caused by time delay effects, nonlinearities and uncertainties of the underlying process.


In this paper we done the case study for different control strategies for flow control in boiler. There are different control strategies to control flow. PID is one the method to control the parameters in boiler and fuzzy logic controller is one of the method. The basic methodologies we learned, mathematical modeling, PID tunning, implementation of PSO algorithm, and basic of fuzzy logic controller.