{"title":"工业高速浓缩机的约束模型预测控制","authors":"Ridouane Oulhiq , Khalid Benjelloun , Yassine Kali , Maarouf Saad , Hafid Griguer","doi":"10.1016/j.jprocont.2023.103147","DOIUrl":null,"url":null,"abstract":"<div><p>High-rate thickeners are used in the mining industry to improve water recovery from slurries and increase their solids ratio. High-rate thickeners operate under strict constraints and several disturbances. To control this process, a constrained <strong>m</strong>odel <strong>p</strong>redictive <strong>c</strong>ontrol (MPC) is developed in this paper. For process identification, a historical data-driven methodology is used and a <strong>v</strong>ector <strong>a</strong>uto<strong>r</strong>egressive with e<strong>x</strong>ogenous variables (VARX) model structure is considered. The model takes underflow slurry density as both a state variable and the process output, along with turbidity, bed level, rake torque, and cone pressure as additional state variables. It takes feed slurry and flocculant flow rates as manipulated inputs and considers inlet slurry density, slurry circulation flow rate, and underflow slurry flow rate as disturbances. The VARX model structural parameters (orders and delays) and coefficients are estimated using a bilevel optimization method. From the model obtained, a discrete state-space representation is derived. This latter is augmented to obtain a standard formulation without delays. The MPC is then formulated considering the process constraints. To evaluate the control performance, simulations are conducted and a baseline comparison is established using proportional integral (PI) control. Simulation results demonstrate that the proposed control method outperforms the baseline method by providing reduced settling times (−32%), minimized peak errors (−20%), and constraints handling ability. Accordingly, the proposed MPC is implemented in an industrial environment and compared to existing manual control based on an <strong>o</strong>bject <strong>l</strong>inking and <strong>e</strong>mbedding (OLE) for <strong>p</strong>rocess <strong>c</strong>ontrol (OPC) architecture. Finally, the industrial results show that the proposed control method effectively stabilizes the underflow slurry density and handles process constraints, resulting in a minimized average error (−90%) and a reduced standard deviation (−50%) compared to existing manual control.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0959152423002354/pdfft?md5=61e7091f282d572d028a56ece8eb8d54&pid=1-s2.0-S0959152423002354-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Constrained model predictive control of an industrial high-rate thickener\",\"authors\":\"Ridouane Oulhiq , Khalid Benjelloun , Yassine Kali , Maarouf Saad , Hafid Griguer\",\"doi\":\"10.1016/j.jprocont.2023.103147\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>High-rate thickeners are used in the mining industry to improve water recovery from slurries and increase their solids ratio. High-rate thickeners operate under strict constraints and several disturbances. To control this process, a constrained <strong>m</strong>odel <strong>p</strong>redictive <strong>c</strong>ontrol (MPC) is developed in this paper. For process identification, a historical data-driven methodology is used and a <strong>v</strong>ector <strong>a</strong>uto<strong>r</strong>egressive with e<strong>x</strong>ogenous variables (VARX) model structure is considered. The model takes underflow slurry density as both a state variable and the process output, along with turbidity, bed level, rake torque, and cone pressure as additional state variables. It takes feed slurry and flocculant flow rates as manipulated inputs and considers inlet slurry density, slurry circulation flow rate, and underflow slurry flow rate as disturbances. The VARX model structural parameters (orders and delays) and coefficients are estimated using a bilevel optimization method. From the model obtained, a discrete state-space representation is derived. This latter is augmented to obtain a standard formulation without delays. The MPC is then formulated considering the process constraints. To evaluate the control performance, simulations are conducted and a baseline comparison is established using proportional integral (PI) control. Simulation results demonstrate that the proposed control method outperforms the baseline method by providing reduced settling times (−32%), minimized peak errors (−20%), and constraints handling ability. Accordingly, the proposed MPC is implemented in an industrial environment and compared to existing manual control based on an <strong>o</strong>bject <strong>l</strong>inking and <strong>e</strong>mbedding (OLE) for <strong>p</strong>rocess <strong>c</strong>ontrol (OPC) architecture. Finally, the industrial results show that the proposed control method effectively stabilizes the underflow slurry density and handles process constraints, resulting in a minimized average error (−90%) and a reduced standard deviation (−50%) compared to existing manual control.</p></div>\",\"PeriodicalId\":50079,\"journal\":{\"name\":\"Journal of Process Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0959152423002354/pdfft?md5=61e7091f282d572d028a56ece8eb8d54&pid=1-s2.0-S0959152423002354-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Process Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0959152423002354\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152423002354","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Constrained model predictive control of an industrial high-rate thickener
High-rate thickeners are used in the mining industry to improve water recovery from slurries and increase their solids ratio. High-rate thickeners operate under strict constraints and several disturbances. To control this process, a constrained model predictive control (MPC) is developed in this paper. For process identification, a historical data-driven methodology is used and a vector autoregressive with exogenous variables (VARX) model structure is considered. The model takes underflow slurry density as both a state variable and the process output, along with turbidity, bed level, rake torque, and cone pressure as additional state variables. It takes feed slurry and flocculant flow rates as manipulated inputs and considers inlet slurry density, slurry circulation flow rate, and underflow slurry flow rate as disturbances. The VARX model structural parameters (orders and delays) and coefficients are estimated using a bilevel optimization method. From the model obtained, a discrete state-space representation is derived. This latter is augmented to obtain a standard formulation without delays. The MPC is then formulated considering the process constraints. To evaluate the control performance, simulations are conducted and a baseline comparison is established using proportional integral (PI) control. Simulation results demonstrate that the proposed control method outperforms the baseline method by providing reduced settling times (−32%), minimized peak errors (−20%), and constraints handling ability. Accordingly, the proposed MPC is implemented in an industrial environment and compared to existing manual control based on an object linking and embedding (OLE) for process control (OPC) architecture. Finally, the industrial results show that the proposed control method effectively stabilizes the underflow slurry density and handles process constraints, resulting in a minimized average error (−90%) and a reduced standard deviation (−50%) compared to existing manual control.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.