事件-时间触发的慢时变工业过程动态调度方法

doi:10.11949/0438-1157.20240784

摘要/Abstract

摘要：

流程工业中的慢时变现象普遍存在，持续变化的工况会导致生产调度的最优操作条件偏移，原有的决策方案不再适用。提出了一种基于事件-时间触发的动态调度方法。首先，分析了慢时变参数对长周期调度决策的影响，提出以操作变量的变化表征设备性能衰减的策略，并构建了动态触发函数，在动态调度的触发条件中融合了事件和时间触发的特点，设计了时变约束条件。其次，在调度过程中嵌入生产系统的动态信息，建立了过程过渡模型作为输入输出动力学的简化表达，从而降低了计算的复杂度。最后，采用闭环滚动的动态调度框架，依据实时的运行状态更新调度模型。在示例研究中，以工业换热网络为例验证了所提方法的有效性，该方法能提供及时的动态调度，表现出较好的经济性能，为考虑工况随时间缓慢迁移条件下的生产调度问题提供了新的解决方案。

关键词: 过程系统, 动态调度, 事件-时间触发, 滚动优化, 换热网络

Abstract:

In process industries, the phenomenon of slow time-varying transitions is prevalent. Continuous changes in operating conditions will lead to shifts in the optimal operating conditions for production scheduling, rendering original decision schemes inapplicable. Therefore, in this paper, a dynamic scheduling method based on event-time triggering mechanism is proposed. First, the impact of slow time-varying parameters on long-term scheduling decisions is analyzed. A strategy is proposed to characterize equipment performance degradation through changes in operational variables, and a dynamic triggering function is constructed. The dynamic scheduling trigger conditions integrate event and time-triggered characteristics, incorporating time-varying constraints. Secondly, dynamic information of the production system is embedded in the scheduling process. A process transition model is established as a simplified expression of input-output dynamics, thereby reducing computational complexity. Finally, a dynamic scheduling framework of closed -loop rolling is used to update the scheduling model according to the real-time operating status. In the case study, the proposed method is validated using an industrial heat exchanger network. This method provides timely dynamic scheduling, demonstrates good economic performance, and offers a new solution for production scheduling problems considering slow time-varying operating conditions.

Key words: process system, dynamic scheduling, event-time triggered, rolling optimization, heat exchanger network

中图分类号:

TB 114.1

任超, 王凯, 韩洁, 阳春华. 事件-时间触发的慢时变工业过程动态调度方法[J]. 化工学报, 2025, 76(1): 256-265.

Chao REN, Kai WANG, Jie HAN, Chunhua YANG. Event-time triggered slow time-varying industrial process dynamic scheduling method[J]. CIESC Journal, 2025, 76(1): 256-265.

图/表 11

图1 慢时变过程示意图

Fig.1 Schematic diagram of slow time-varying process

图2 常规考虑动态信息的调度决策框架

Fig.2 Conventional scheduling decision framework considering dynamic information

图3 本研究提出的动态调度决策框架

Fig.3 Proposed framework for dynamic scheduling

图4 事件触发机制的对比

Fig.4 Comparison of event-triggering mechanisms

图5 保守轨迹的设计原理

Fig.5 Design principles of conservative trajectory

图6 选用的换热网络结构图

Fig.6 Selected heat exchange network structure

表1 换热网络的物流数据

Table 1 Flow data of heat exchanger network

参数	热流		冷流
参数	H1	H2	C1	C2
质量流量/(kg·s^-1)	50	40	30	200
输入温度/K	423	443	323	353
目标出口温度/K	323	313	393	383
比热容/(J·kg^-1·K^-1)	4000	2500	1000	2500
黏度/(mPa·s)	0.25	1.2	1.2	2

表2 3种触发机制的对比

Table 2 Comparison of three triggering mechanisms

触发机制	描述
EET	操作变量持续调节，直至到达边界值
HPET	融合时间触发与预设性能事件触发的方法
HDET	融合时间触发与动态事件触发的方法

图7 EET条件下的操作变量变化趋势

Fig.7 Trend of manipulated variable changes under EET condition

图8 HPET和HDET条件下操作变量的变化趋势

Fig.8 Trend of manipulated variable changes under HPET and HDET conditions

图9 3种触发机制下的能源消耗对比

Fig.9 Comparison of energy consumption under three triggering mechanisms

参考文献 36

1	Binazadeh T, Shafiei M H. Robust stabilization of uncertain nonlinear slowly-varying systems: application in a time-varying inertia pendulum[J]. ISA Transactions, 2014, 53(2): 373-379.
2	Xie F M, Xu F, Liang Z S, et al. Full cycle dynamic optimisation maintaining the operation margin of acetylene hydrogenation fixed-bed reactor[J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 108: 29-42.
3	Chen C B, Luo X L, Wang T Y, et al. Minimum motive steam consumption on full cycle optimization with cumulative fouling consideration for MED-TVC desalination system[J]. Desalination, 2021, 507: 115017.
4	陈春波, 罗雄麟, 孙琳. 多效蒸发海水淡化系统可行域时变分析与全周期操作优化[J]. 化工学报, 2021, 72(11): 5686-5695.
	Chen C B, Luo X L, Sun L. Time-varying analysis of feasible region and full-cycle operating optimization in multi-effect distillation seawater desalination system[J]. CIESC Journal, 2021, 72(11): 5686-5695.
5	王天媛, 陈春波, 孙琳, 等. 基于全周期缓慢结垢的多效蒸发海水淡化慢时变系统优化设计[J]. 化工学报, 2022, 73(2): 759-769.
	Wang T Y, Chen C B, Sun L, et al. Optimal design of slow-time-varying system for multi-effect distillation desalination based on full-cycle slow fouling[J]. CIESC Journal, 2022, 73(2): 759-769.
6	Markowski M, Trafczynski M, Urbaniec K. Validation of the method for determination of the thermal resistance of fouling in shell and tube heat exchangers[J]. Energy Conversion and Management, 2013, 76: 307-313.
7	Patil P, Srinivasan B, Srinivasan R. Monitoring fouling in heat exchangers under temperature control based on excess thermal and hydraulic loads[J]. Chemical Engineering Research and Design, 2022, 181: 41-54.
8	Trafczynski M, Markowski M, Urbaniec K. Energy saving and pollution reduction through optimal scheduling of cleaning actions in a heat exchanger network[J]. Renewable and Sustainable Energy Reviews, 2023, 173: 113072.
9	任超, 孙琳, 罗雄麟. 换热器因应结垢慢时变的控制系统重构分析[J]. 化工学报, 2021, 72(10): 5273-5283.
	Ren C, Sun L, Luo X L. Analysis on the reconfiguration of the control system of the heat exchanger in response to the slow and time-varying fouling[J]. CIESC Journal, 2021, 72(10): 5273-5283.
10	Cao X Y, Xu F, Luo X L. A novel strategy of continuous process transition and wide range throughput fluctuating ethylene column[J]. Journal of the Taiwan Institute of Chemical Engineers, 2021, 121: 61-73.
11	Yang D, Li X D, Qiu J L. Output tracking control of delayed switched systems via state-dependent switching and dynamic output feedback[J]. Nonlinear Analysis: Hybrid Systems, 2019, 32: 294-305.
12	Liu H, Shen Y, Zhao X D. Finite-time stabilization and boundedness of switched linear system under state-dependent switching[J]. Journal of the Franklin Institute, 2013, 350(3): 541-555.
13	Tabuada P. Event-triggered real-time scheduling of stabilizing control tasks[J]. IEEE Transactions on Automatic Control, 2007, 52(9): 1680-1685.
14	Linsenmayer S, Dimarogonas D V, Allgöwer F. Event-based vehicle coordination using nonlinear unidirectional controllers[J]. IEEE Transactions on Control of Network Systems, 2018, 5(4): 1575-1584.
15	Ge X H, Han Q L, Ding L, et al. Dynamic event-triggered distributed coordination control and its applications: a survey of trends and techniques[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(9): 3112-3125.
16	Wen S X, Guo G, Wong W S. Hybrid event-time-triggered networked control systems: scheduling-event-control co-design[J]. Information Sciences, 2015, 305: 269-284.
17	Hu B, Guan Z H, Chen G R, et al. A distributed hybrid event-time-driven scheme for optimization over sensor networks[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7199-7208.
18	Touretzky C R, Harjunkoski I, Baldea M. Dynamic models and fault diagnosis-based triggers for closed-loop scheduling[J]. AIChE Journal, 2017, 63(6): 1959-1973.
19	Engell S, Harjunkoski I. Optimal operation: scheduling, advanced control and their integration[J]. Computers & Chemical Engineering, 2012, 47: 121-133.
20	Pistikopoulos E N, Diangelakis N A. Towards the integration of process design, control and scheduling: are we getting closer?[J]. Computers & Chemical Engineering, 2016, 91: 85-92.
21	Du J, Park J, Harjunkoski I, et al. A time scale-bridging approach for integrating production scheduling and process control[J]. Computers & Chemical Engineering, 2015, 79: 59-69.
22	Tsay C, Baldea M. Integrating production scheduling and process control using latent variable dynamic models[J]. Control Engineering Practice, 2020, 94: 104201.
23	Charitopoulos V M, Papageorgiou L G, Dua V. Closed-loop integration of planning, scheduling and multi-parametric nonlinear control[J]. Computers & Chemical Engineering, 2019, 122: 172-192.
24	Sun L, Ren C, Luo X L. Online control system reconfiguration towards long period energy-saving optimization of heat exchanger networks[J]. Journal of Cleaner Production, 2022, 367: 132940.
25	Ren C, Sun L, Xu F, et al. Full cycle on-line autonomous reconfiguration of MIMO control system based on REGA pairing principle for heat exchanger networks[J]. Journal of Process Control, 2023, 130: 103081.
26	谢府命, 许锋, 罗雄麟. 慢时变化工过程裕量释放机制分析[J]. 化工学报, 2020, 71(S2): 216-224.
	Xie F M, Xu F, Luo X L. Release mechanism analysis of design margin for slowly-time-varying chemical processes[J]. CIESC Journal, 2020, 71(S2): 216-224.
27	Shafiei M H, Yazdanpanah M J. Stabilization of nonlinear systems with a slowly varying parameter by a control Lyapunov function[J]. ISA Transactions, 2010, 49(2): 215-221.
28	Binazadeh T, Shafiei M H. Extending satisficing control strategy to slowly varying nonlinear systems[J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(4): 1071-1078.
29	Binder T, Cruse A, Cruz Villar C A, et al. Dynamic optimization using a wavelet based adaptive control vector parameterization strategy[J]. Computers & Chemical Engineering, 2000, 24(2/3/4/5/6/7): 1201-1207.
30	Ouyang H P, Lin Y. Adaptive fault-tolerant control for actuator failures: a switching strategy[J]. Automatica, 2017, 81: 87-95.
31	Ouyang H P, Lin Y. Adaptive fault-tolerant control and performance recovery against actuator failures with deferred actuator replacement[J]. IEEE Transactions on Automatic Control, 2021, 66(8): 3810-3817.
32	Bechlioulis C P, Rovithakis G A. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems[J]. IEEE Transactions on Automatic Control, 2010, 55(5): 1220-1226.
33	Wang W, Wen C Y. Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance[J]. Automatica, 2010, 46(12): 2082-2091.
34	Hang P, Zhao L W, Liu G L. Optimal design of heat exchanger network considering the fouling throughout the operating cycle[J]. Energy, 2022, 241: 122913.
35	Sun L, Zha X L, Luo X L. Coordination between bypass control and economic optimization for heat exchanger network[J]. Energy, 2018, 160: 318-329.
36	Abuhalima O, Sun L, Chang R X, et al. Synthesis of a multipass heat exchanger network based on life cycle energy saving[J]. Applied Thermal Engineering, 2016, 100: 1189-1197.

[1]	卢昕悦, 陈锐莹, 姜夏雪, 梁海瑞, 高歌, 叶正芳. 耦合LNG冷能的液态空气储能系统和液态CO₂储能系统对比分析[J]. 化工学报, 2024, 75(9): 3297-3309.
[2]	李洪瑞, 黄纯西, 洪小东, 廖祖维, 王靖岱, 阳永荣. 基于自适应变步长同伦法的循环流程收敛算法[J]. 化工学报, 2024, 75(7): 2604-2612.
[3]	黄志鸿, 周利, 柴士阳, 吉旭. 耦合加氢装置优化的多周期氢网络集成[J]. 化工学报, 2024, 75(5): 1951-1965.
[4]	陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902.
[5]	郑玉圆, 葛志伟, 韩翔宇, 王亮, 陈海生. 中高温钙基材料热化学储热的研究进展与展望[J]. 化工学报, 2023, 74(8): 3171-3192.
[6]	李贵贤, 曹阿波, 孟文亮, 王东亮, 杨勇, 周怀荣. 耦合固体氧化物电解槽的CO₂制甲醇过程设计与评价研究[J]. 化工学报, 2023, 74(7): 2999-3009.
[7]	邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537.
[8]	贠程, 王倩琳, 陈锋, 张鑫, 窦站, 颜廷俊. 基于社团结构的化工过程风险演化路径深度挖掘[J]. 化工学报, 2023, 74(4): 1639-1650.
[9]	王子宗, 索寒生, 赵学良. 数字孪生智能乙烯工厂研究与构建[J]. 化工学报, 2023, 74(3): 1175-1186.
[10]	李孟原, 崔祎, 王彧斐, 杨路, 李海东, 张奇琪, 常承林. 具有柔性拓扑结构的厂际多周期换热网络优化[J]. 化工学报, 2023, 74(11): 4634-4644.
[11]	陈哲文, 魏俊杰, 张玉明, 张炜, 李家州. CO₂近零排放的光煤互补耦合SOFC发电系统热力学分析[J]. 化工学报, 2023, 74(11): 4688-4701.
[12]	赵丽文, 刘桂莲. 扰动换热网络负荷迁移规律及瓶颈辨识策略[J]. 化工学报, 2023, 74(11): 4611-4621.
[13]	王雅琳, 潘雨晴, 刘晨亮. 基于GSA-LSTM动态结构特征提取的间歇过程监测方法[J]. 化工学报, 2022, 73(9): 3994-4002.
[14]	王琨, 侍洪波, 谭帅, 宋冰, 陶阳. 局部时差约束邻域保持嵌入算法在故障检测中的应用[J]. 化工学报, 2022, 73(7): 3109-3119.
[15]	杨岭, 崔国民, 周志强, 肖媛. 精细搜索策略应用于质量交换网络综合[J]. 化工学报, 2022, 73(7): 3145-3155.