Heat exchanger's shell and tube modeling for intelligent control design (original) (raw)
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Fuzzy Based Learning Control for Shell and Tube Heat Exchanger Process
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Fuzzy Model Reference Learning Control (FMRLC) is an capable technique for the control of nonlinear process. In this paper, a FMRLC is applied in to a non linear shell and tube Heat Exchanger process (STHE). First, the mathematical model of the STHE process is derived and simulation runs are carried out by considering the FMRLC in a closed loop. A similar test runs are also carried out with hybrid fuzzy P+ID Controller and conventional fuzzy for comparison analysis. The results clearly indicate that the incorporation of FMRLC in the control loop in spherical tank system provides a good tracking performance than the hybrid fuzzy P+ID and conventional fuzzy controller.
A fuzzy-PID controller in shell and tube heat exchanger simulation modeled for temperature control
Indonesian Journal of Electrical Engineering and Computer Science, 2021
Shell and tube heat exchanger are the most generally utilized types of heat exchanger for heat transfer in many industrial purposes. Shell and tube heat exchanger comprise a set of units. One unit includes mechanical parts and another unit consists of controlling part. Both the unit has to be modelled to ensure the efficient operation of shell and tube heat exchanger. The mechanical modelling is completely established on the type of applications. The controller modelling is independent of the kind of applications. The controller only needs the input fluid and output fluid properties such as temperature and flow rate. Hence the primary objective of the paper is to focus on the controller part for enhancing the heat exchanger performance. This paper proposes the novel fuzzy-PID controlling technique based on the multiplication operation to make the settling time and overshoot of setpoint temperature to be less to a greater extent and the results are compared with the conventional PI method with various tuning algorithms.
Modeling and Temperature Control of Heat Exchanger Process
2017
The main purpose of a heat exchanger system is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is of prime importance. In this paper, firstly simplified mathematical model for heat exchanger process has been developed and used for the dynamic analysis and control design. Artificial neural networks (ANN) are effective in modeling of non linear multi variables so modeling of heat exchanger process is accomplished using optimized architecture of artificial neural network after that different controllers such as PID controller, feedback plus feed-forward controller and a ratio controller are developed to control the outlet temperature of a shell and tube heat exchanger. The main aim of the proposed controllers is to regulate the temperature of the outgoing fluid to a desired level in the minimum possible time irrespective of load and process disturbances and nonlinearity. The developed ratio controller has improve the overshoot from 1.34 to 0 ...
This paper presents new approach of designing and running hybrid intelligent Controller for controlling temperature of cold fluid outflow in shell and tube heat exchangers. The proposed approach employs PID based fuzzy controller for determination of the optimal results. Results show that the proposed scheme significantly improves the performance of the shell and tube heat exchangers. It is anticipated that designing of PID based fuzzy controller using intelligent techniques would remarkably improves the rate of response of the system, maximum overshoot and settling time would be decreased in designed intelligent controller. The model is simulatedand implemented using Simulink/MATLAB.
Controller Design for Temperature Control of Heat Exchanger System: Simulation Studies
This paper analyzes the performance of different controllers such as feedback, feedback plus feed-forward and internal model controller to regulate the temperature of outlet fluid of a shell and tube heat exchanger to a certain reference value. The transient performance and the error criteria of the controllers are analyzed and the best controller is found out. From the simulation results, it is found out that the internal model control outperforms feedback PID and feedback plus feed-forward controller.
Intelligent Control of Heat Exchangers
Chemical engineering transactions, 2011
This work deals with the design and application of a neuro-fuzzy controller for a heat exchanger. To deal with the problem of parameter adjustment, efficient neuro-fuzzy scheme known as the ANFIS (Adaptive Network-based Fuzzy Inference System) can be used. The ANFIS is a cross between an artificial neural network and a fuzzy inference system (FIS) and represents Takagi-Sugeno fuzzy model as generalized feedforward neural network, and trains it with plant I/O data, thereby adjusting the parameters of the antecedent membership functions as well as those of the functional consequents. The neuro-fuzzy control of the heat exchanger is compared with classical PID control. The simulation results confirm that fuzzy is one of the possibilities for successful control of heat exchangers. The advantage of this approach is that it is not a linear-model-based strategy. Comparison of the simulation results obtained using fuzzy and those obtained using classical PID control demonstrates the effecti...
PERFORMANCE ANALYSIS OF VARIOUS CONTROLLER FOR A HEAT EXCHANGER SYSTEM
This paper analyzes the performance of different controllers such as Proportional controller, Proportional plus derivative controller and Proportional plus derivative plus integral controller(PID) to regulate the temperature of outlet fluid of a shell and tube heat exchanger to a certain reference value. The transient performance and the error criteria of the controllers are analyzed and the best controller is found out. From the simulation results, it is found out that the PID controller outperforms Proportional and proportional plus derivative controller.