25 research outputs found

    3D computational fluid dynamics study of a drying process in a can making industry

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    YesIn the drying process of a can making industry, the drying efficiency of a thermal drying oven can be improved by adjusting the volumetric air flow rate of the blower. To maximize drying efficiency, an optimal flow rate is needed. Consequently, a three-dimensional computational fluid dynamics (CFD) is used to provide simulation according to the response of air velocity, air temperature and evaporated solvent concentration with respect to changes in volumetric air flow rate in the drying oven. An experimental study has been carried out to determine the evaporation rate of the solvent. To validate the models, the process data obtained from the CFD is compared with that obtained from actual data. In the accurate models, the simulation results demonstrate that the decrease in volumetric air flow rate provides no major discrepancy of the air velocity patterns in all dimensions and decreases the maximum temperature in the oven. Consequently, this decrease in volumetric air flow rate rapidly increases the evaporated solvent concentration in the beginning and then gradually decreases over the length of the oven. In addition, further reduction of the flow rate gives lower heat loss of the oven up to 83.67%.The authors would like to thank The Thailand Research Fund (TRF) under The Royal Golden Jubilee Ph.D. Program (PHD/0158/2550), The Institutional Research Grant (The Thailand Research Fund) (IRG 5780014) and Chulalongkorn University (Contract No. RES_57_411_21_076) for financial support to this work

    Nonparametric nonlinear model predictive control

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    Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

    Model predictive control for the reactant concentration control of a reactor

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    The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors

    Comparison sf optimisation based control techniques for the control of a CSTR

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    This paper presents the implementation of optimisation based control techniques: an open loop optimal control (OLOC) and a recording horizon control (RHC), for the control of a continuous stirred tank reactor (CSTR) system with exothermic reactions, which exhibit multiple steady state conditions. This type of control techniques requires the solution of an open loop optimal control problem which consists of an objective function, system constraint and state or manipulated variable constraint. Simulation results have demonstrated that the OLOC controller can only perform well if all model parameters are known exactly and not when model mismatches exist. The RHC controller, on the other hand, gives better results and is much more robust for systems with plant/model mismatches within a certain range

    The use of a partially simulated exothermic reactor to test nonlinear algorithms

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    Two nonlinear control algorithms for controlling nonlinear systems include the receding horizon method and the nonlinear neural network inverse model methods. These methods have been found to be useful in dealing with difficult-to-control nonlinear systems, especially in simulated systems. However although much simulation work has been performed with these methods, simulation only is inadequate to guarantee that these algorithms could be successfully implemented in real plants. For this reason, a relatively low cost and simple online experimental configuration of a partially simulated continuous reactor has been devised which allows for the realistic testing of a wide range of nonlinear estimation and control techniques i.e. receding horizon control and neural network inverse model control methods. The results show that these methods are viable and attractive nonlinear methods for real-time application in chemical reactor systems