Design and implementation of an advanced controller in plant distributed control system for improving control of non-linear belt weigh feeder. (February 2018) - Mahajan, Nayana P.; Deshpande, Sadanand B.; Kadwane, Sumant G.
Highlights: This research paper is related to improvement in performance of a mechanical machine (Belt weigh feeder) in actual running plant. A belt weigh feeder system is a flat belt conveyor designed for feeding bulk material into the process in a controlled fashion to achieve optimum process efficiency and product quality. The problem of belt speed control is challenging as the dynamic response of system is nonlinear and there are frequent changes in belt load due to variation in bulk material characteristics. The control accuracy of belt weigh feeder is fully dependent on the controller performance in providing precise control of speed of motor/belt. In this study, a lot of operating data of an industrial BWF has been analysed to study the nonlinear response of BWF system friction and to establish prominence of Stribeck friction in BWF nonlinearity. Further, detailed modelling of the BWF system has been done to highlight the effects of nonlinearity on system response which has not been done in any previous research. Subsequently, PI Fuzzy Logic with cascaded adaptive controller (Advanced BWF controller) is designed where the gains of PI controller obtained using Fuzzy Logics are further adapted (or compensated) considering the operating speed of the system to achieve loop stability and prevent overshoots at higher speeds. The combined control strategy of PI Fuzzy Logic Control along with adaptive gain compensation has been implemented in plant Distributed (Digital) Control System (DCS) with an innovative use of calculation blocks available in the DCS software. The results obtained after implementing the Advanced BWF controller for an actual plant BWF indicate improved response of machine on all control parameters namely rise time, overshoot, set point tracking, belt load disturbance rejection and its control accuracy improves by around 34 % based on calculation of IAE. To the best of knowledge of the authors, there is no previous reference available where the implementation of PI FLC in plant DCS is presented. Abstract: A Belt Weigh Feeder (BWF) is a flat belt conveyor designed for feeding bulk material into a chemical process in a controlled fashion for better process control. The dynamic weight of bulk material is measured with a belt weigh bridge and the belt speed is controlled to compensate for any variation in the weight so that the mass feed rate is maintained as per the set feed rate. The problem of belt speed control is challenging as the dynamic response of system is non-linear and there are frequent changes in belt load due to variation in bulk material characteristics. The control accuracy of belt weigh feeder is fully dependent on the controller's performance in providing precise control of speed of motor/belt. Any delay in achieving the set feed rate or frequent deviation between set and actual feed rate affects the quality and efficiency of downstream process. Conventional PI controller is unable to provide optimum control due to system non-linearity. To overcome this problem, in this paper first the operating data of the BWF system is analysed and the nature and cause for the nonlinearity is investigated. The system is then modelled using the design parameters of plant belt weigh feeder, which is then simulated to have a better insight into its non-linear response. Subsequently, based on simulation results, a PI Fuzzy Logic (PI-FL) controller is designed to improve the control accuracy of the system. Further, to ensure the stability of the system, an adaptive controller is introduced in cascade to fine tune the gains of PI-FL controller as per the operating speed of the BWF. Finally, an advanced PI-FL with cascade adaptive controller is implemented in the plant DCS (microprocessor based process control system). The actual test results indicate reduction in the Integral of Absolute Error (IAE) of the system by about 34% using this controller.