基于支持向量机的回转干燥窑生产过程建模与能耗优化研究
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摘要
回转干燥窑广泛应用于工业生产中,尤其在有色金属生产中占有重要地位,如何降低其生产过程中的高能耗一直是困扰企业的难题。干燥过程涉及复杂的传热、传质机理,与干燥物质的特性密切相关,回转干燥窑是一个大惯性、大滞后性、存在时变参数的控制对象,加上窑的主要部分处于回转状态,关键工艺参数难以准确及时的测量,因此很难定量的描述封闭窑内的热工状况。这种参数信息的模糊性,使得建模的难度非常大,导致传统的优化控制策略难以实施。
本文以锌精矿回转干燥窑为背景,在深入研究其干燥过程机理的基础上,提出了基于支持向量机的回转干燥窑生产过程建模方法,采用基于支持向量机模糊模型的方法解决模型中干燥速率难以确定的问题。为了提高模型的预测精度,利用干燥过程分多个阶段的特点,提出了基于多阶段多支持向量机的回转干燥窑生产过程级联模型的方法,采用多阶段多支持向量机模糊模型确定干燥速率。针对支持向量机模糊模型中超参数的优化问题,提出了改进的遗传算法及免疫进化遗传算法,避免了人工选取参数的不足。为了提高模型的运算速度,提出了改进的变长度粒子群优化算法精简支持向量。在所获模型基础上提出了基于混沌扰动的粒子群约束优化算法,优化干燥过程热工参数,实验数据、仿真和应用都验证了此方法的有效性。论文的主要研究工作及创新性成果如下：
(1)建立了基于能量、质量守恒定律的回转干燥窑生产过程机理模型,该模型由燃烧室及回转干燥窑窑体机理模型组成,通过燃烧室模型确定产生的烟气量及烟气温度,通过回转干燥窑窑体模型掌握热量利用情况,同时获得物料与烟气在窑内沿轴向连续变化的情况。设计了热平衡测试、参数测试和干燥速率测试实验,并进行了模型仿真,结果显示该模型能反映实际过程趋势,为后续研究奠定了基础。
(2)提出了基于支持向量机的回转干燥窑生产过程建模方法,运用机理模型描述干燥过程,确定模型的结构,运用模糊建模方法确定干燥速率,并采用基于支持向量机回归模型的方法解决干燥速率模糊规则难以获取的问题,既增加了模糊规则的可靠性又保持了模糊模型的解释性。为了补偿干燥速率实验样本与现场生产数据之间的误差,提出了基于支持向量的残差补偿方法,有效地提高了模型的预测精度。
(3)提出了改进的遗传算法及免疫进化遗传算法解决支持向量机模糊模型中超参数的优化问题,改进的遗传算法保留了遗传算法全局搜索能力,同时为了克服遗传算法收敛慢的不足,在后期融入了局部线性搜索；免疫进化遗传算法将遗传算法的搜索空间划分为小生境,根据小生境中个体的浓度利用免疫进化算法对适应值进行了修正,克服了遗传算法易出现“早熟”的问题。运行结果表明,两种方法都能有效地找到最优超参数,而改进的遗传算法具有更快的收敛速度。
(4)提出了基于多干燥阶段多支持向量机的回转干燥窑生产过程级联模型的方法,利用多阶段多支持向量机模糊模型确定干燥速率。针对建模时输入样本空间难以划分的问题,提出了融入分区熵判断准则的模糊C均值算法(FCM),同时为了克服FCM算法对噪声数据敏感问题,引入了可能性FCM聚类算法,讨论了基于核函数的聚类算法,提高了输入样本空间划分的准确率。实验结果表明,该级联模型能提高预测精度,但增加了模型的复杂程度。
(5)针对过多的支持向量降低模型的运算速度问题,提出了一种新的基于粒子群优化算法的支持向量精简方法,运用变长度粒子编码,将预测精度直接作为性能指标,通过粒子群优化算法获得精简支持向量集。结果表明该算法能在基本保持原预测精度的基础上有效地减少了支持向量个数,提高了模型的运行速度。
(6)在回转干燥窑生产过程模型的基础上,针对工程中带约束的优化问题,提出了基于混沌扰动的粒子群约束优化算法,以干燥全过程参数范围为约束,以热效率最高为目标,通过混沌扰动使粒子跳出局部极值及不动点,获得最优的工艺参数,从而达到节约能源、降低消耗的目的,运行结果及实际应用表明了方法的有效性。
Rotary dryers are widely used in industrial processes, particularly in the non-ferrous metal production. But how to reduce the high-energy consumption of rotary dryer during production process is still a business problem. Its drying process not only involves complex heat transfer and mass transfer mechanism, but also closely relates to the properties of dry material. The rotary dryer is a control object with large inertia, large time delay, and time-varying parameters, and what's more, the main parts of the dryers are in a rotating state, the key technological parameter can not be measured accurately and timely. Therefore, it is difficult to describe the thermal state of closed dryer quantitatively. The ambiguity of parameter information makes modeling very difficult, so the traditional optimization control strategy is hard to implement.
In the paper, taking a zinc concentrate ore rotary dryer as the background, a support-vector-regression(SVR)-based model for rotary dryer production process is proposed based on the deep study of the drying process mechanism. A SVR-based fuzzy model is adopted to solve the problem of determing the model parameters drying rate. In order to improve prediction accuracy, using the multi-phased-distribution feature of rotary drying process, the multi-phased and multi-SVR cascade model is proposed and drying rate is determined by utilizing multi-phased and multi-SVR fuzzy model. While for the optimization of hyper parameters of SVR fuzzy model, an improved genetic algorithm and an immune genetic algorithm are proposed respectively to avoid the lack of experience in choosing parameters. In order to improve the computation speed of the model, an improved variable-length particle swarm optimization algorithm based reduced support vector is proposed. Based on the obtained model, the chaos disturbence particle swarm constained optimazation algorithm is proposed to optimize the thermal parameters of drying process. Experimental data, simulations and applications have proved the effectiveness of this method. The main research and innovative achievements of the paper are as follows:
(1) Rotary dryer production process first-principle model based on energy conservation and mass conservation law is established. The model consists of combustion chamber and rotary dryer body first-principle model. The volume and temperature of flue gas are obtained by the combustion chamber model. The heat utilization and the continuously changing situations of material and drying medium in the dryer along the axial direction are gotten by the rotary dryer body model. Thermal balance testing experiments, parameters testing experiments and drying rate testing experiments are designed and simulated, which lays a foundation for follow-up study. The simulation results show that this model can reflect the trend of the actual process.
(2) The SVR-based rotary dryer production process model is proposed. First-principle model is used to describe the drying process and determine the structure of the model. Fuzzy modeling method is employed to determine the drying rate. In regard to the problem of obtaining the drying rate fuzzy rules, a method using SVR model is adopted. In order to compensate errors between experimental samples and actual production samples of drying rate, a support vector residual compensation method is proposed. This method can compensate for the errors effectively.
(3) An improved genetic algorithm and an immune genetic algorithm are used respectively to solve the problem of optimization of hyper parameters of SVR fuzzy model. Features of the former include:retain the global search ability of genetic algorithm, and in order to overcome the slow convergence of genetic algorithm, integrate local linear search in the latter part of the algorithm to speed up the convergence rate. For the latter, in order to overcome the "premature" problem of genetic algorithm during process of searching for the optimal solution, genetic algorithm is improved. Dividing the search space of algorithm into small niches, and according to the concentration of individual niche, immune evolutionary algorithm is used to amend the fitness values. The results show that both methods can find the optimal model hyper parameters effectively, while the improved genetic algorithm has faster convergence speed.
(4) A rotary dryer production process cascade model based on multi-drying-phased and multi-SVR is proposed. For the key problem that input sample space is very difficult to divide during modeling, an improved fuzzy C means algorithm (FCM) based on space entropy rules is proposed. Meanwhile, in order to overcome the problem that FCM algorithm is very sensitive to noise data, the possibility FCM clustering algorithm (PFCM) is introduced, the Kernel-based clustering algorithm (KPFCM) is discussed, to make the accuracy of input sample space better. The experimental results show that the cascade model can improve the prediction accuracy, but increase the complexity of model.
(5) For the problems of increasing computation speed caused by too many support vectors, a new particle swarm optimization algorithm based reduced support vector method is proposed. Using variable length particle coding to predict accuracy, letting it as a performance index directly, and obtaining the reduced support vector set by particle swarm optimization algorithm. The results show that the algorithm can reduce the number of support vectors effectively on the basis of maintaining the original prediction accuracy, and thus improve the model speed.
(6) Based on rotary dryer production process model, for the optimization problems with constraints in engineering, the chaos disturbence particle swarm constained optimization algorithm is proposed. Taking the range of parameters in drying process as constraint, with the highest thermal efficiency as the goal, make particles out of local minima and non-fixed point by chaotic disturbance to obtain the optimal technological parameters, and thus save energy and reduce consumption. The results and practical application have proved the effectiveness of this method.
本文以锌精矿回转干燥窑为背景,在深入研究其干燥过程机理的基础上,提出了基于支持向量机的回转干燥窑生产过程建模方法,采用基于支持向量机模糊模型的方法解决模型中干燥速率难以确定的问题。为了提高模型的预测精度,利用干燥过程分多个阶段的特点,提出了基于多阶段多支持向量机的回转干燥窑生产过程级联模型的方法,采用多阶段多支持向量机模糊模型确定干燥速率。针对支持向量机模糊模型中超参数的优化问题,提出了改进的遗传算法及免疫进化遗传算法,避免了人工选取参数的不足。为了提高模型的运算速度,提出了改进的变长度粒子群优化算法精简支持向量。在所获模型基础上提出了基于混沌扰动的粒子群约束优化算法,优化干燥过程热工参数,实验数据、仿真和应用都验证了此方法的有效性。论文的主要研究工作及创新性成果如下：
(1)建立了基于能量、质量守恒定律的回转干燥窑生产过程机理模型,该模型由燃烧室及回转干燥窑窑体机理模型组成,通过燃烧室模型确定产生的烟气量及烟气温度,通过回转干燥窑窑体模型掌握热量利用情况,同时获得物料与烟气在窑内沿轴向连续变化的情况。设计了热平衡测试、参数测试和干燥速率测试实验,并进行了模型仿真,结果显示该模型能反映实际过程趋势,为后续研究奠定了基础。
(2)提出了基于支持向量机的回转干燥窑生产过程建模方法,运用机理模型描述干燥过程,确定模型的结构,运用模糊建模方法确定干燥速率,并采用基于支持向量机回归模型的方法解决干燥速率模糊规则难以获取的问题,既增加了模糊规则的可靠性又保持了模糊模型的解释性。为了补偿干燥速率实验样本与现场生产数据之间的误差,提出了基于支持向量的残差补偿方法,有效地提高了模型的预测精度。
(3)提出了改进的遗传算法及免疫进化遗传算法解决支持向量机模糊模型中超参数的优化问题,改进的遗传算法保留了遗传算法全局搜索能力,同时为了克服遗传算法收敛慢的不足,在后期融入了局部线性搜索；免疫进化遗传算法将遗传算法的搜索空间划分为小生境,根据小生境中个体的浓度利用免疫进化算法对适应值进行了修正,克服了遗传算法易出现“早熟”的问题。运行结果表明,两种方法都能有效地找到最优超参数,而改进的遗传算法具有更快的收敛速度。
(4)提出了基于多干燥阶段多支持向量机的回转干燥窑生产过程级联模型的方法,利用多阶段多支持向量机模糊模型确定干燥速率。针对建模时输入样本空间难以划分的问题,提出了融入分区熵判断准则的模糊C均值算法(FCM),同时为了克服FCM算法对噪声数据敏感问题,引入了可能性FCM聚类算法,讨论了基于核函数的聚类算法,提高了输入样本空间划分的准确率。实验结果表明,该级联模型能提高预测精度,但增加了模型的复杂程度。
(5)针对过多的支持向量降低模型的运算速度问题,提出了一种新的基于粒子群优化算法的支持向量精简方法,运用变长度粒子编码,将预测精度直接作为性能指标,通过粒子群优化算法获得精简支持向量集。结果表明该算法能在基本保持原预测精度的基础上有效地减少了支持向量个数,提高了模型的运行速度。
(6)在回转干燥窑生产过程模型的基础上,针对工程中带约束的优化问题,提出了基于混沌扰动的粒子群约束优化算法,以干燥全过程参数范围为约束,以热效率最高为目标,通过混沌扰动使粒子跳出局部极值及不动点,获得最优的工艺参数,从而达到节约能源、降低消耗的目的,运行结果及实际应用表明了方法的有效性。
Rotary dryers are widely used in industrial processes, particularly in the non-ferrous metal production. But how to reduce the high-energy consumption of rotary dryer during production process is still a business problem. Its drying process not only involves complex heat transfer and mass transfer mechanism, but also closely relates to the properties of dry material. The rotary dryer is a control object with large inertia, large time delay, and time-varying parameters, and what's more, the main parts of the dryers are in a rotating state, the key technological parameter can not be measured accurately and timely. Therefore, it is difficult to describe the thermal state of closed dryer quantitatively. The ambiguity of parameter information makes modeling very difficult, so the traditional optimization control strategy is hard to implement.
In the paper, taking a zinc concentrate ore rotary dryer as the background, a support-vector-regression(SVR)-based model for rotary dryer production process is proposed based on the deep study of the drying process mechanism. A SVR-based fuzzy model is adopted to solve the problem of determing the model parameters drying rate. In order to improve prediction accuracy, using the multi-phased-distribution feature of rotary drying process, the multi-phased and multi-SVR cascade model is proposed and drying rate is determined by utilizing multi-phased and multi-SVR fuzzy model. While for the optimization of hyper parameters of SVR fuzzy model, an improved genetic algorithm and an immune genetic algorithm are proposed respectively to avoid the lack of experience in choosing parameters. In order to improve the computation speed of the model, an improved variable-length particle swarm optimization algorithm based reduced support vector is proposed. Based on the obtained model, the chaos disturbence particle swarm constained optimazation algorithm is proposed to optimize the thermal parameters of drying process. Experimental data, simulations and applications have proved the effectiveness of this method. The main research and innovative achievements of the paper are as follows:
(1) Rotary dryer production process first-principle model based on energy conservation and mass conservation law is established. The model consists of combustion chamber and rotary dryer body first-principle model. The volume and temperature of flue gas are obtained by the combustion chamber model. The heat utilization and the continuously changing situations of material and drying medium in the dryer along the axial direction are gotten by the rotary dryer body model. Thermal balance testing experiments, parameters testing experiments and drying rate testing experiments are designed and simulated, which lays a foundation for follow-up study. The simulation results show that this model can reflect the trend of the actual process.
(2) The SVR-based rotary dryer production process model is proposed. First-principle model is used to describe the drying process and determine the structure of the model. Fuzzy modeling method is employed to determine the drying rate. In regard to the problem of obtaining the drying rate fuzzy rules, a method using SVR model is adopted. In order to compensate errors between experimental samples and actual production samples of drying rate, a support vector residual compensation method is proposed. This method can compensate for the errors effectively.
(3) An improved genetic algorithm and an immune genetic algorithm are used respectively to solve the problem of optimization of hyper parameters of SVR fuzzy model. Features of the former include:retain the global search ability of genetic algorithm, and in order to overcome the slow convergence of genetic algorithm, integrate local linear search in the latter part of the algorithm to speed up the convergence rate. For the latter, in order to overcome the "premature" problem of genetic algorithm during process of searching for the optimal solution, genetic algorithm is improved. Dividing the search space of algorithm into small niches, and according to the concentration of individual niche, immune evolutionary algorithm is used to amend the fitness values. The results show that both methods can find the optimal model hyper parameters effectively, while the improved genetic algorithm has faster convergence speed.
(4) A rotary dryer production process cascade model based on multi-drying-phased and multi-SVR is proposed. For the key problem that input sample space is very difficult to divide during modeling, an improved fuzzy C means algorithm (FCM) based on space entropy rules is proposed. Meanwhile, in order to overcome the problem that FCM algorithm is very sensitive to noise data, the possibility FCM clustering algorithm (PFCM) is introduced, the Kernel-based clustering algorithm (KPFCM) is discussed, to make the accuracy of input sample space better. The experimental results show that the cascade model can improve the prediction accuracy, but increase the complexity of model.
(5) For the problems of increasing computation speed caused by too many support vectors, a new particle swarm optimization algorithm based reduced support vector method is proposed. Using variable length particle coding to predict accuracy, letting it as a performance index directly, and obtaining the reduced support vector set by particle swarm optimization algorithm. The results show that the algorithm can reduce the number of support vectors effectively on the basis of maintaining the original prediction accuracy, and thus improve the model speed.
(6) Based on rotary dryer production process model, for the optimization problems with constraints in engineering, the chaos disturbence particle swarm constained optimization algorithm is proposed. Taking the range of parameters in drying process as constraint, with the highest thermal efficiency as the goal, make particles out of local minima and non-fixed point by chaotic disturbance to obtain the optimal technological parameters, and thus save energy and reduce consumption. The results and practical application have proved the effectiveness of this method.
引文
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