Flow control is essential in many industrial applications such as chemical reactors, heat exchangers and distillation columns. Most industrial processes exhibit nonlinearities and inherit dead time, which limit the performance of conventional PID controllers. This Project is about the design and implementation of Fuzzy Logic Controller (FLC) for flow control applications. The objective is to overcome problems inherited with conventional PID control scheme such as handling unpredictable disturbance, non-measurable noise as well as further improve the transient state and steady state response performance. The proposed control scheme is implemented in Flow Control and Calibration Pilot Plant. The design is done using Matlab/Simulink software package and is connected to the Pilot Plant through USB-type DAQ cards. Simulation and implementation results showed that the developed controller has less overshoot, good control performance, better disturbance handling ability, great robustness and is more flexible and intuitive to tune. It is expected that this advanced controller improves efficiency and productivity of industrial processes through proper handling of any disturbance or noise and increase the robustness of controller actions.
Abstract-This paper proposes a soft sensor to estimate phase flow rates utilizing common measurements in oil and gas production wells. The developed system addresses the limited production monitoring due to using common metering facilities. It offers a cost-effective solution to meet real-time monitoring demands, reduces operational and maintenance costs, and acts as a back-up to multiphase flow meters.The soft sensor is developed using feed-forward neural network, and generalization and network complexity are regulated using K-fold cross-validation and early stopping technique. The soft sensor is validated using actual well test data from producing wells, and model performance is analyzed using cumulative deviation and cumulative flow plots. The developed soft sensor shows promising performance with a mean absolute percent error of around 4% and less than 10% deviation for 90% of the samples.
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