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摘要: 在闡述煉鋼廠多尺度建模與協同制造技術架構的基礎上，分別從單體工序尺度、車間區段尺度與煉鋼廠運行尺度開展了煉鋼廠協同制造的研究。從工序/裝置過程控制系統（PCS）到煉鋼廠制造執行系統（MES）進行了較為系統的建模研發，構建了包括轉爐工序、精煉工序與連鑄工序在內的工序工藝控制模型以及以生產計劃與調度模型為核心的物質流運行優化模型，并通過工序工藝控制和生產計劃與調度的動態協同，實現了煉鋼廠多工序/裝置的高效運行。研發了煉鋼?連鑄過程工序工藝控制模型、生產計劃與調度模型同MES之間的數據接口，實現了MES與生產工藝控制、流程運行控制、生產計劃與調度系統的有機融合，形成了以機理模型與數據模型協同驅動的工藝精準控制、多工序協同運行、基于“規則+算法”的生產計劃與調度為支撐的煉鋼?連鑄過程集成制造技術，通過多層級的縱向協同與多工序的橫向協同，實現了煉鋼廠的協同運行與控制。研究成果是煉鋼?連鑄過程智能制造的有益探索與實踐，對流程工業智能制造企業具有很強的參考價值，對冶金工業綠色化、智能化發展具有示范與借鑒作用。應用后，明顯提升了煉鋼廠的協同制造水平，取得了顯著的經濟與社會效益。Abstract: With the recent, rapid developments of metallurgical theory and intelligent steelmaking technology, the intelligent upgrading of iron and steel enterprises has attracted increased attention and become a topic of discussion in the steel industry. Collaborative manufacturing is an important feature of intelligent manufacturing in steel enterprises, and it plays an important role in improving the production efficiency and reducing the carbon emissions of iron and steel enterprises. This study elaborated the structure and the contents of multiscale modeling and the collaborative manufacturing of steelmaking plants in detail. The collaborative control of steelmaking plants was studied from the scales of individual processes, workshop sections, and the operation of steelmaking plants. Systematic modeling studies had been conducted from the process control system of processes/devices to the manufacturing execution system (MES). The process control models, including the converter steelmaking process, secondary metallurgy process, and continuous casting process, and mass flow operation optimization models with the production planning and scheduling model as the core were established. In addition, the high-efficiency operation of multi processes/devices was realized through the dynamic coordination of process control and production planning and scheduling in the steelmaking plants. The data interface between process control models, production planning and scheduling models, and MES had been developed to realize the comprehensive integration of MES, production process control, process operation control, production planning, and scheduling system. It had formed the steelmaking-continuous casting process integrated manufacturing technology supported by the precise process control co-driven by mechanism and data models, collaborative process operation, and production planning and scheduling based on “rules + algorithms.” Through multilevel vertical coordination and multiprocess horizontal coordination, the coordinated operation and the control of steelmaking plants were realized. The study results demonstrated a beneficial exploration and the practice of intelligent manufacturing in the steelmaking-continuous casting process, which had strong reference value for intelligent manufacturing enterprises in the process industry, and had a demonstration effect for the green and the intelligent development of the metallurgical industry. After the application, the collaborative manufacturing level of the steelmaking plant had been considerably improved, and significant economic and social benefits had been achieved.
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圖 1 煉鋼廠多尺度建模與協同制造技術架構
Figure 1. Technological structure of multiscale modeling and collaborative manufacturing of steelmaking plants
圖 2 基于熔池混勻度的轉爐冶煉過程模型驗證. (a)碳含量預報; (b)溫度預報[15]
Figure 2. Validation of converter steelmaking process model based on molten bath mixing degree: (a) carbon content prediction; (b) temperature prediction
圖 3 基于熔池混勻度的指數模型終點碳含量預報誤差分布[5]
Figure 3. Prediction error distribution of end-point carbon content of the exponential model based on bath mixing degree
圖 4 LF精煉造渣模型預報結果. (a) 石灰加入量預報; (b)石灰加入量命中率[8]
Figure 4. Prediction results of LF refining slag-making model: (a) comparison between the calculated and actual weights of lime; (b) hit ratio of predicting the required weight of lime
圖 5 凝固冷卻配水優化結果. (a) 配水優化后鑄坯特征溫度曲線; (b) 優化前/后連鑄坯寬面中心溫度變化對比[21]
Figure 5. Optimization results of solidification cooling water distribution: (a) characteristic temperature curves of the slab after optimization; (b) comparison of the temperature change at the center of the broad face before and after optimization
圖 7 模型應用前后爐?機對應關系. (a)應用前; (b)應用后[27]
Figure 7. Furnace-caster coordinating scenario: (a) before application; (b) after application
圖 9 “定爐對定機”生產模式運行甘特圖（使用鋼包15個）
Figure 9. Gantt chart of ladle operation based on “furnace?caster coordinating” strategy (use 15 ladles)
圖 10 基于多智能體技術的煉鋼?連鑄過程協同調度系統架構[46]
Figure 10. System architecture of collaborative scheduling for steelmaking plant based on multi-agent technology
圖 12 煉鋼廠的集成制造技術路線圖[47]
Figure 12. Integrated manufacturing technology roadmap for steelmaking plants
表 1 RELM中心碳偏析預測模型的基本參數[22]
Table 1. Basic parameters of the central carbon segregation prediction model based on RELM[22]
Parameters Setting value Parameters Setting value Number of input layer neurons 7 Number of output layer neurons 1 Number of hidden layer neurons 50 Activation function sigmoid Regularization coefficient($ \lambda $) 0.1 
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表 2 三種智能算法求解算例的結果對比[33]
Table 2. Results of calculation examples solved by three algorithms
Calculation examples Heats Production mode Objective function /min Maximum waiting time between processes /min Proportion of waiting time more than 30 min between processes /% Maximum deviation of the cast starting time /min A1 A2 A3 A1 A2 A3 A1 A2 A3 A1 A2 A3 1 90 4BOF?3CCM 1952 2036 5735 65 34 103 6 16 57 0 0 105 2 93 3BOF?3CCM 4302 3944 4932 94 76 100 30 29 39 0 0 59 3 65 4BOF?3CCM 1371 1272 2926 45 43 97 9 7 40 0 0 94 4 77 4BOF?3CCM 2456 2014 4658 78 60 92 27 12 54 0 0 78 5 84 4BOF?3CCM 2932 2811 5518 106 88 110 22 18 60 0 0 110 6 77 4BOF?4CCM 2878 3055 6052 84 78 114 28 30 67 0 0 108 7 80 4BOF?4CCM 2968 2280 2532 116 84 88 27 15 23 0 0 52 8 67 4BOF?3CCM 2286 2091 4046 75 72 102 25 18 52 0 0 44
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表 3 A、B兩廠2019年4月~7月期間系統層流運行指數RM與工序匹配度R[35]
Table 3. System laminar flow operation index RM and process matching index R for steelmaking plants A and B from April to July, 2019
Plant RM R April May June July April May June July A 0.647 0.638 0.639 0.629 0.608 0.601 0.631 0.599 B 1.000 1.000 1.000 1.000 0.749 0.789 0.720 0.776
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表 4 A廠4種調度模型的可用性評價指數
$ {\mathit{\varepsilon }}_{\mathit{p}} $ Table 4. Scheduling model availability degree
$ {\varepsilon }_{p} $ of the four scheduling models of Plant AScheduling model $ {\varepsilon }_{1,p} $ $ {\varepsilon }_{2,p} $ $ {\varepsilon }_{3,p} $ $ {\varepsilon }_{4,p} $ $ {\varepsilon }_{p} $ Model p1 0.965 0.85 0.918 0.897 0.919 Model p2 0.603 0.456 0.595 0.557 0.579 Model p3 0.124 0.146 0.069 0.344 0.115 Model p4 0.285 0.571 0.588 0.422 0.509
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