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Comparison of three control strategies for optimization of spray dryer operation

机译:喷雾干燥器运行优化的三种控制策略比较

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摘要

Spray drying is the preferred process to reduce the water content of many chemicals, pharmaceuticals, and foodstuffs. A significant amount of energy is used in spray drying to remove water and produce a free flowing powder product. In this paper, we present and compare the performance of three controllers for operation of a four-stage spray dryer. The three controllers are a proportional-integral (PI) controller that is used in industrial practice for spray dryer operation, a linear model predictive controller with real-time optimization (MPC with RTO, MPC-RTO), and an economically optimizing nonlinear model predictive controller (E-NMPC). The MPC with RTO is based on the same linear state space model in the MPC and the RTO layer. The E-NMPC consists of a single optimization layer that uses a nonlinear system of ordinary differential equations for its predictions. The PI control strategy has a fixed target that is independent of the disturbances, while the MPC-RTO and the E-NMPC adapt the operating point to the disturbances. The goal of spray dryer operation is to optimize the profit of operation in the presence offeedcomposition and ambient air humidity variations; i.e. to maximize the production rate, while minimizing the energy consumption, keeping the residual moisture content of the powder below a maximum limit, and avoiding that the powder sticks to the chamber walls. We use an industrially recorded disturbance scenario in order to produce realistic simulations and conclusions. The key performance indicators such as the profit of operation, the product flow rate, the specific energy consumption, the energy efficiency, and the residual moisture content of the produced powder are computed and compared for the three controllers. In this simulation study, we find that the economic performance of the MPC with RTO as well as the E-NMPC is considerably improved compared to the PI control strategy used in industrial practice. The MPC with RTO improves the profit of operation by 8.61%, and the E-NMPC improve.
机译:喷雾干燥是减少许多化学药品,药品和食品中水含量的首选方法。大量的能量用于喷雾干燥中以除去水并产生自由流动的粉末产品。在本文中,我们介绍并比较了用于四级喷雾干燥器的三个控制器的性能。这三个控制器是工业上用于喷雾干燥器操作的比例积分(PI)控制器,具有实时优化的线性模型预测控制器(带有RTO的MPC,MPC-RTO)以及经济上优化的非线性模型预测控制器(E-NMPC)。具有RTO的MPC基于MPC和RTO层中相同的线性状态空间模型。 E-NMPC由单个优化层组成,该优化层使用常微分方程的非线性系统进行预测。 PI控制策略具有与干扰无关的固定目标,而MPC-RTO和E-NMPC使工作点适应干扰。喷雾干燥机运行的目标是在存在进料成分和周围空气湿度变化的情况下优化运行收益;即,在使能量消耗最小化的同时,最大化生产率,将粉末的残留水分保持在最大极限以下,并且避免粉末粘附到腔室壁上。为了产生真实的模拟和结论,我们使用了工业记录的干扰情况。计算并比较了三个控制器的关键性能指标,例如运营利润,产品流量,单位能耗,能效和所生产粉末的残留水分含量。在此仿真研究中,我们发现与工业实践中使用的PI控制策略相比,具有RTO的MPC和E-NMPC的经济性能得到了显着改善。具有RTO的MPC将运营利润提高了8.61%,而E-NMPC则得到了改善。

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