采用蜜獾算法优化比例积分导数控制器参数,实现熟料冷却器篦下压力的优化控制

Z. Brijet, T. Suresh
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引用次数: 1

摘要

炉排上穿透具有一定厚度和渗透性的加热熟料床所需的最小空气压力称为炉排下压力。保持炉排压力的一致性是至关重要的,因为过大的压力会导致熟料形成不稳定的悬浮,而最小的压力会使熟料的热量损坏冷却器炉排。因此,有必要通过合理的控制方法来获得优异的性能。为此,本文提出了一种比例积分导数(PID)控制器的优化方法来控制熟料冷却器篦下压力。提出的优化方法是蜜獾算法(HBA),该算法模拟了蜜獾的摄食习性。提出的方法的主要目的是维持和调节熟料冷却器的炉膛压力,并为熟料提供最佳冷却速率。基于该方法的篦冷器具有功耗小、温度稳定等优点。在MATLAB中实现了该技术,并将其效率与各种现有技术进行了比较。仿真结果表明,该方法的功耗RMSE为158,平均精度为98.65。基于栅格压力的RMSE为5.01,平均精度为99.55。该方法基于出口温度的RMSE为4.32,平均精度为81.21。这些值均低于现有方法,说明本文方法优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique
The minimum air pressure necessary at the grate to penetrate a bed of heated clinker of a certain thickness and permeability is known as under‐grate pressure. It is vital to keep the under‐grate pressure consistent because excessive pressure causes the clinker to form an unstable suspension and minimum pressure allows heat from the clinker to harm the cooler grates. So it is necessary to obtain excellent performance through reasonable control methods. For this purpose, this article proposes an optimization approach for a proportional integral derivative (PID) controller to control the pressure below the grate of the clinker cooler. The proposed optimization approach is the honey badger algorithm (HBA), which mimics the feeding habits of honey badgers. The main aim of proposed approach is to maintain and regulate under‐grate pressure of clinker cooler using HBA and provide optimum cooling rate to clinker. The proposed approach based grate cooler also provide the less electricity consumption and stable temperature. The proposed technique is implemented in MATLAB and its efficiency is compared to various existing techniques. From the simulation, the electricity consumption of proposed approach RMSE is 158 and mean accuracy is 98.65. Under‐grate pressure based RMSE is 5.01 and the mean accuracy become 99.55. The outlet temperature based RMSE of proposed approach is 4.32 and mean accuracy is 81.21. These values are lower than existing approaches, it reveals the proposed approach is better than the existing one.
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