Plant-wide Process Operating Performance Assessment Based on Two-level Multi-block GMM-PRS
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摘要: 过程运行状态评价旨在实时判断运行性能优劣程度，并追溯导致非优运行状态的原因，指导操作人员进行生产调整，保证企业经济效益.因此，对过程运行性能优劣评价的研究具有重要的理论和应用价值.本文针对定量、定性变量共存的流程工业过程运行状态评价问题，提出基于两层分块混合模型的评价方法.将流程工业过程根据其物理特性和管理方向划分子块，产生子块层和全流程层.在定量信息占主导地位的子块内，建立定量的高斯混合模型（Gaussian mixture model，GMM）.在定性信息占主导地位的子块内，建立定性概率粗糙集（Probabilistic rough set，PRS）模型.综合各子块运行状态信息，进一步判定全流程运行状态等级.针对非优运行状态等级，本文提出基于贡献率的非优原因追溯方法，在非优子块内进行原因追溯.最后，将所提方法应用于某黄金湿法冶炼生产过程，说明所提方法的可行性和有效性.Abstract: Process operating performance assessment judges operating performance optimal degree online, and identifies the causes for non-optimal performance to guide the production adjustment for operators. Therefore, the research on process operating performance assessment is of great significance in both theory and practical applications. To solve the plant-wide process operating performance assessment problem with coexistence of quantitative and qualitative variables, a two-level multi-block hybrid model based assessment approach is proposed in this article. According to the physical property and management direction, a plant-wide process is classified into multiple sub-blocks. Hence, there are sub-block level and global level. In a sub-block that is dominated by quantitative information, a Gaussian mixture model (GMM) is established. Accordingly, in a qualitative information dominated sub-block, a probabilistic rough set (PRS) is built. Based on the sub-block-level performance grade information, the global-level performance grade can be further judged. For the non-optimal performance grade, cause identification is implemented in the non-optimal sub-block. A contribution rate based non-optimal cause identification method is developed in this research. In the end, to illustrate the feasibility and validity, the proposed technique is applied to a gold hydrometallurgy process.
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Key words:
- Process operating performance assessment /
- plant-wide process /
- Gaussian mixture model (GMM) /
- probabilistic rough set (PRS) /
- gold hydrometallurgy process
1) 本文责任编委 邓方 -
图 1 两层分块混合模型结构示意图
Fig. 1 The illustration of the two-level multi-block hybrid model structure
图 4 实验1子块3中非优原因追溯结果
Fig. 4 The cause identification result within Sub-block 3 in Case 1
图 6 实验2子块5中非优原因追溯结果
Fig. 6 The cause identification result within Sub-block 5 in Case 2
表 1 过程变量列表
Table 1 The process variable list
序号 指标名称 数据类型 位置 1 第一次浸出前矿石固金品位 定性变量 一次浸出 2 第一次浸出前矿浆浓度 定性变量 一次浸出 3 第一次浸出前调浆水量 定量变量 一次浸出 4 第一次浸出调浆后矿浆流量 定性变量 一次浸出 5 第一次浸出氰化钠添加量 定量变量 一次浸出 6 第一次浸出后氰根离子浓度 定量变量 一次浸出 7 第一次浸出充气量 定量变量 一次浸出 8 第一次浸出溶氧浓度 定量变量 一次浸出 9 第一次浸出后金氰络合物离子浓度 定量变量 一次浸出 10 第一次洗涤前矿浆浓度 定性变量 一次洗涤 11 第一次洗涤前矿浆流量 定性变量 一次洗涤 12 第一次洗涤后贵液流量 定性变量 一次洗涤 13 第一次洗涤后滤饼流量 定性变量 一次洗涤 14 第一次洗涤后金氰络合物离子浓度 定性变量 一次洗涤 15 第二次浸出前矿石固金品位 定性变量 二次浸出 16 第二次浸出前矿浆浓度 定性变量 二次浸出 17 第二次浸出前调浆水量 定性变量 二次浸出 18 第二次浸出调浆后矿浆流量 定量变量 二次浸出 19 第二次浸出氰化钠添加量 定性变量 二次浸出 20 第二次浸出后氰根离子浓度 定量变量 二次浸出 21 第二次浸出充气量 定量变量 二次浸出 22 第二次浸出溶氧浓度 定量变量 二次浸出 23 第二次浸出后金氰络合物离子浓度 定量变量 二次浸出 24 第二次洗涤前矿浆浓度 定性变量 二次洗涤 25 第二次洗涤前矿浆流量 定性变量 二次洗涤 26 第二次洗涤后贵液流量 定性变量 二次洗涤 27 第二次洗涤后滤饼流量 定性变量 二次洗涤 28 第二次洗涤后金氰络合物离子浓度 定量变量 二次洗涤 29 置换前贵液金氰络合物离子浓度 定量变量 置换 30 脱氧塔压力1 定量变量 置换 31 脱氧塔压力2 定量变量 置换 32 脱氧塔压力3 定量变量 置换 33 置换前贵液流量 定性变量 置换 34 锌粉添加量 定性变量 置换 35 锌粉平均粒径 定性变量 置换 36 金泥品位 定性变量 置换 
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表 2 实验设计
Table 2 The experiment design
实验 描述 1 前100组数据运行状态等级为优(等级1), 后100组数据由于第二次浸出氰化钠添加量(子块3, 定量)不足, 导致运行状态等级变为差(等级3). 2 前100组数据运行状态等级为优(等级1), 后100组数据由于锌粉添加量(子块5, 定性)过量, 导致运行状态等级变为中(等级2).
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表 3 不同方法评价准确率对比
Table 3 The assessment accuracy rate comparison of different methods
PRS 两层分块
PRSGMM 两层分块
GMM两层分块
GMM-PRS评价准确率 75.3 % 81.0 % 86.2 % 97.6 % 97.9 %
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