氧化铝生料浆配料过程不确定优化方法研究及应用
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摘要
生料浆配料是烧结法氧化铝生产过程中的第一道工序,配料的合理性直接关系到后续工序的产品质量和资源能源消耗。然而,生料浆配料过程是一个复杂的工业过程,生料浆指标之间耦合严重,过程反应机理不明,工况变化复杂,特别是参与配料的物料成分含量频繁波动且无法在线实时检测,使过程输入参数表现出很大不确定性,致使人工操作模式和传统优化方法都难以对氧化铝生料浆配料过程进行有效的优化控制。
为了在优化过程中充分考虑物料参数不确定性的影响,提高优化结果的鲁棒性,本文提出了氧化铝生料浆配料不确定优化策略,并将其应用于实际工业过程,取得了良好的效果。论文主要研究工作及创新性成果如下：
(1)在对过程工艺进行深入分析的基础上,围绕不确定参数的数学描述、不确定优化模型结构及求解算法三个方面的问题,提出了氧化铝生料浆配料过程的不确定优化控制方案。该方案根据配料物料表现出的不同特性,将物料分为原料和返料两大类,分别研究其成分含量不确定参数置信区间的获取方法,并定义了区间不确定度和区间覆盖率评价置信区间的合理性；同时,考虑生料浆质量指标的生产要求和检测滞后性,研究了生料浆配料过程不确定优化模型的结构,并提出了与模型结构相匹配的分类专家知识库和专家推理策略,实现不确定优化模型的有效求解。
(2)针对返料成分含量数据波动大和非线性强的问题,提出了一种基于粗糙集(RS)广义重构技术的不确定预估方法。该方法首先通过关联维数和最大李亚普诺夫指数鉴别返料成分含量时间序列的混沌特性；然后利用RS约简理论,提出了一种广义相空间重构技术,提取包含充分预测信息的精简样本集,建立最小二乘支持向量机(LS_SVM)返料成分含量时间序列预测模型；最后,通过对预测结果补偿一定的误差,获取返料成分参数合理的置信区间。对于随时间变化相对平稳的原料成分参数,采用数据统计的方法,确定不确定参数的置信区间,并引入一种滚动更新策略,不断优化置信区间边界。数据验证结果表明了置信区间的合理性。
(3)考虑生料浆配料过程不确定性、滞后性、指标耦合性和区间要求,建立了基于质量预测模型的字典序区间目标不确定优化模型。生料浆质量预测模型以基于物料平衡的机理模型为主规律模型,并将基于神经网络和经验知识的智能补偿模型相集成,以提高模型预测精度。仿真结果表明,与机理模型和单一神经网络模型相比较,集成模型具有更高的预测精度,能够很好地解决生料浆质量指标检测滞后的问题。基于质量预测模型,建立了一种以质量指标区间违背最小为目标函数的字典序区间目标不确定优化模型,该模型综合考虑了生料浆指标的强耦合性和区间要求,能够准确地描述氧化铝配料过程的优化问题。
(4)针对不确定优化模型目标函数的字典序结构,提出了一种基于Hammersley序列抽样(HSS)技术和分类专家知识库的字典序优化推理策略。在知识库的设计中,根据优化模型一级优化目标,将知识规则进行分类,并在每类规则集内对知识规则进行优先级排序,这种具有优先级别的分类知识库结构与优化模型的字典序和推理策略相匹配,能够实现不确定问题的快速求解。
(5)开发了氧化铝生料浆配料不确定优化系统。该系统利用OPC技术实现了优化机与底层集散控制系统(DCS)的实时数据通信,并通过企业内部网与分析检测数据管理系统的服务器相连,实现了数据与操作信息的网络共享和传输,主要功能包括过程监控、配比优化、数据导入、数据管理、报表打印等。系统的工业应用减轻了工人的劳动轻度,提高了系统的鲁棒性和指标的合格率,稳定了生产,为其它行业配料过程的控制提供了一种优化模式。
The raw material blending(RMB) is the first process of alumina production by sintering method. The quality of the slurry produced by the RMB process makes direct influence on the product quality and energy consumption of the downstream processes. However, the RMB is a complex process with strong coupling, unknown chemical reactions and complex condition changes. Especially, the composition fluctuation of raw materials and the difficulty of on-line measurement result in the uncertainty in process parameters, which makes it very difficult for human operation and traditional optimization technologies to effectively realize the optimal control of the blending process.
Considering the uncertainty in raw material composition parameters, the optimization strategy under uncertainty is proposed to enhance the robustness of the optimization system for the blending process of alumina production. And the industrial application results prove the effectiveness of the proposed scheme. The major innovation research achievements include:
(1)After investigating the RMB process, the optimization strategy under uncertainty, including three modules of the mathematical description of uncertain parameters, the structure of optimization model with uncertain parameters and the solution algorithm, is proposed for the blending process. The optimization strategy firstly obtains the confidence interval of uncertain parameters on the basis that the blended materials are divided into two kinds of raw materials and returned materials. And the interval uncertainty degree and the interval cover rate are defined to evaluate the confidence intervals. Then, according to the production requirements of slurry quality and the time-delay of off-line measurement, the structure of the optimization model with uncertain parameters is researched for the RMB process. And, the classificatory knowledge base and the expert reasoning strategy, which match with the structure of the optimization model, are designed to effectively solve the optimization problem of the RMB process.
(2)For the returned material with the large composition fluctuation and the strong nonlinearity, the confidence intervals of the compositions are predicted based on the generalized phase space reconstruction technology. Firstly, the correlation dimension and the largest Lyapunov index are adopted to analyze chaotic characteristics of time series of compositions of returned material. Then, by using rough set(RS) theory, the generalized phase space of multi-component time series of returned material is reconstructed to extract training samples and LS_SVM was used to describe the relationship between input and output variables. Finally, the confidence intervals of composition parameters of returned material are obtained by compensating certain deviations for the prediction results of LS_SVM. For raw materials with small fluctuation of compositions, the statistical method is used to determine the confidence intervals of uncertainty parameters, and the rolling update strategy is introduced to on-line optimize the interval. Data verification results show the effectiveness of parameter intervals.
(3)Based on the uncertainty, time-delay, coupling and interval requirements, the lexicographic interval goal optimization model with uncertain parameters, which includes the prediction model for slurry quality, is built for the RMB process of alumina production. In the prediction model, a mechanistic model based on material balance principle is established as the master-rule model, and an intelligent compensation model combining neural networks(NNs) with empirical knowledge is integrated with the mechanistic model to enhance the prediction precision. Simulation experiments show that the prediction results of integrated model are better than that of single mechanism model as well as that of NN model. So, the integrated model can deal with the large time-delay of off-line measurement for slurry quality. Based on the quality prediction model, the lexicographic interval goal optimization model with uncertain parameters is built, the objective of which is to minimize the violation to the intervals of quality indexes. This model can accurately describe the optimization problem of alumina blending process due to taking into account uncertain parameters, strong coupling and interval requirements of quality indexes.
(4)Considering the structure of the optimization model, the lexicographic reasoning strategy based on the hammersley sequence sampling(HSS) technology and classificatory knowledge base is designed to solve the optimization problem with multiple objectives, interval requirements and uncertain parameters. In the design of knowledge rules, the knowledge rules of blending process are classified into different groups according to the first optimization objective, and sorted by precedence in every group. This kind of classificatory knowledge base with precedence matches the optimization model and the reasoning strategy, and the effective solution is realized.
(5) On the basis of the researches amentioned above, the optimization system under uncertainty is developed for the RMB process of alumina production. In the sytem, the OPC is used to realize the real-time communication between the optimization computer and the DCS, and the analyzed data management system is connected to the optimization system by the intranet to acquire automatically the analyzed data from the offline laboratory. And the optimization system realizes the functions including process monitoring, ratio optimization, data leading-in, data management and so on. The results of industrial application show that the proposed strategy can reduce the burden of operators, enhance the robustness of the system, improve the quality of slurry and stablize the whole alumina production. It provides a good optimization mode for other blending processes with uncertainty.
为了在优化过程中充分考虑物料参数不确定性的影响,提高优化结果的鲁棒性,本文提出了氧化铝生料浆配料不确定优化策略,并将其应用于实际工业过程,取得了良好的效果。论文主要研究工作及创新性成果如下：
(1)在对过程工艺进行深入分析的基础上,围绕不确定参数的数学描述、不确定优化模型结构及求解算法三个方面的问题,提出了氧化铝生料浆配料过程的不确定优化控制方案。该方案根据配料物料表现出的不同特性,将物料分为原料和返料两大类,分别研究其成分含量不确定参数置信区间的获取方法,并定义了区间不确定度和区间覆盖率评价置信区间的合理性；同时,考虑生料浆质量指标的生产要求和检测滞后性,研究了生料浆配料过程不确定优化模型的结构,并提出了与模型结构相匹配的分类专家知识库和专家推理策略,实现不确定优化模型的有效求解。
(2)针对返料成分含量数据波动大和非线性强的问题,提出了一种基于粗糙集(RS)广义重构技术的不确定预估方法。该方法首先通过关联维数和最大李亚普诺夫指数鉴别返料成分含量时间序列的混沌特性；然后利用RS约简理论,提出了一种广义相空间重构技术,提取包含充分预测信息的精简样本集,建立最小二乘支持向量机(LS_SVM)返料成分含量时间序列预测模型；最后,通过对预测结果补偿一定的误差,获取返料成分参数合理的置信区间。对于随时间变化相对平稳的原料成分参数,采用数据统计的方法,确定不确定参数的置信区间,并引入一种滚动更新策略,不断优化置信区间边界。数据验证结果表明了置信区间的合理性。
(3)考虑生料浆配料过程不确定性、滞后性、指标耦合性和区间要求,建立了基于质量预测模型的字典序区间目标不确定优化模型。生料浆质量预测模型以基于物料平衡的机理模型为主规律模型,并将基于神经网络和经验知识的智能补偿模型相集成,以提高模型预测精度。仿真结果表明,与机理模型和单一神经网络模型相比较,集成模型具有更高的预测精度,能够很好地解决生料浆质量指标检测滞后的问题。基于质量预测模型,建立了一种以质量指标区间违背最小为目标函数的字典序区间目标不确定优化模型,该模型综合考虑了生料浆指标的强耦合性和区间要求,能够准确地描述氧化铝配料过程的优化问题。
(4)针对不确定优化模型目标函数的字典序结构,提出了一种基于Hammersley序列抽样(HSS)技术和分类专家知识库的字典序优化推理策略。在知识库的设计中,根据优化模型一级优化目标,将知识规则进行分类,并在每类规则集内对知识规则进行优先级排序,这种具有优先级别的分类知识库结构与优化模型的字典序和推理策略相匹配,能够实现不确定问题的快速求解。
(5)开发了氧化铝生料浆配料不确定优化系统。该系统利用OPC技术实现了优化机与底层集散控制系统(DCS)的实时数据通信,并通过企业内部网与分析检测数据管理系统的服务器相连,实现了数据与操作信息的网络共享和传输,主要功能包括过程监控、配比优化、数据导入、数据管理、报表打印等。系统的工业应用减轻了工人的劳动轻度,提高了系统的鲁棒性和指标的合格率,稳定了生产,为其它行业配料过程的控制提供了一种优化模式。
The raw material blending(RMB) is the first process of alumina production by sintering method. The quality of the slurry produced by the RMB process makes direct influence on the product quality and energy consumption of the downstream processes. However, the RMB is a complex process with strong coupling, unknown chemical reactions and complex condition changes. Especially, the composition fluctuation of raw materials and the difficulty of on-line measurement result in the uncertainty in process parameters, which makes it very difficult for human operation and traditional optimization technologies to effectively realize the optimal control of the blending process.
Considering the uncertainty in raw material composition parameters, the optimization strategy under uncertainty is proposed to enhance the robustness of the optimization system for the blending process of alumina production. And the industrial application results prove the effectiveness of the proposed scheme. The major innovation research achievements include:
(1)After investigating the RMB process, the optimization strategy under uncertainty, including three modules of the mathematical description of uncertain parameters, the structure of optimization model with uncertain parameters and the solution algorithm, is proposed for the blending process. The optimization strategy firstly obtains the confidence interval of uncertain parameters on the basis that the blended materials are divided into two kinds of raw materials and returned materials. And the interval uncertainty degree and the interval cover rate are defined to evaluate the confidence intervals. Then, according to the production requirements of slurry quality and the time-delay of off-line measurement, the structure of the optimization model with uncertain parameters is researched for the RMB process. And, the classificatory knowledge base and the expert reasoning strategy, which match with the structure of the optimization model, are designed to effectively solve the optimization problem of the RMB process.
(2)For the returned material with the large composition fluctuation and the strong nonlinearity, the confidence intervals of the compositions are predicted based on the generalized phase space reconstruction technology. Firstly, the correlation dimension and the largest Lyapunov index are adopted to analyze chaotic characteristics of time series of compositions of returned material. Then, by using rough set(RS) theory, the generalized phase space of multi-component time series of returned material is reconstructed to extract training samples and LS_SVM was used to describe the relationship between input and output variables. Finally, the confidence intervals of composition parameters of returned material are obtained by compensating certain deviations for the prediction results of LS_SVM. For raw materials with small fluctuation of compositions, the statistical method is used to determine the confidence intervals of uncertainty parameters, and the rolling update strategy is introduced to on-line optimize the interval. Data verification results show the effectiveness of parameter intervals.
(3)Based on the uncertainty, time-delay, coupling and interval requirements, the lexicographic interval goal optimization model with uncertain parameters, which includes the prediction model for slurry quality, is built for the RMB process of alumina production. In the prediction model, a mechanistic model based on material balance principle is established as the master-rule model, and an intelligent compensation model combining neural networks(NNs) with empirical knowledge is integrated with the mechanistic model to enhance the prediction precision. Simulation experiments show that the prediction results of integrated model are better than that of single mechanism model as well as that of NN model. So, the integrated model can deal with the large time-delay of off-line measurement for slurry quality. Based on the quality prediction model, the lexicographic interval goal optimization model with uncertain parameters is built, the objective of which is to minimize the violation to the intervals of quality indexes. This model can accurately describe the optimization problem of alumina blending process due to taking into account uncertain parameters, strong coupling and interval requirements of quality indexes.
(4)Considering the structure of the optimization model, the lexicographic reasoning strategy based on the hammersley sequence sampling(HSS) technology and classificatory knowledge base is designed to solve the optimization problem with multiple objectives, interval requirements and uncertain parameters. In the design of knowledge rules, the knowledge rules of blending process are classified into different groups according to the first optimization objective, and sorted by precedence in every group. This kind of classificatory knowledge base with precedence matches the optimization model and the reasoning strategy, and the effective solution is realized.
(5) On the basis of the researches amentioned above, the optimization system under uncertainty is developed for the RMB process of alumina production. In the sytem, the OPC is used to realize the real-time communication between the optimization computer and the DCS, and the analyzed data management system is connected to the optimization system by the intranet to acquire automatically the analyzed data from the offline laboratory. And the optimization system realizes the functions including process monitoring, ratio optimization, data leading-in, data management and so on. The results of industrial application show that the proposed strategy can reduce the burden of operators, enhance the robustness of the system, improve the quality of slurry and stablize the whole alumina production. It provides a good optimization mode for other blending processes with uncertainty.
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