دانلود رایگان مقاله لاتین فرآیند شیمیایی با عدم قطعیت پارامتر از سایت الزویر
عنوان فارسی مقاله:
مدل اقتصادی کنترل پیش بینی فرآیندهای شیمیایی با عدم قطعیت پارامتر
عنوان انگلیسی مقاله:
Economic model predictive control of chemical processes with parameter uncertainty
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Chemical Plants are designed with the task of transforming raw materials into more valuable products. These transformations must occur in the most efficient way in order to attain different goals such as maximization of product yield, minimization of the amount of contaminants or by-products, minimization of the energy employed in the process etc. Furthermore, these transformations have to be carried out under economical, physical and environmental constraints and they must be robust to variations in process settings like temperature, input flows and pressures or variations in raw material quality. To achieve these goals advanced model based controllers such as MPC are widely used since they can optimally deal with multivariable interactions while accounting for process constraints. The conventional hierarchical control structure (see (Findeisen et al., 1980; Luyben et al., 1990)) implemented in most process industries involves an RTO (real time optimization) (Naysmith and Douglas, 1995) level above a multivariate control level realized by an MPC or other multivariable control strategy followed by lower level single-input single-output controllers (e.g. PIDs)to effect control of actuators. The RTO is generally executed to maximize a steady state economic cost with respect to steady state values of process variables that are used as set points in the lower level multivariable control strategy. Thus, the RTO provides targets (setpoints) and the multivariable controller (e.g. MPC) controls the system around these targets. Although this hierarchical strategy has resulted in good performance in industrial applications there is an opportunity for improvement since chemical processes are rarely at steady state. Hence, the steady state set points calculated by the RTO and enforced by the MPC controller may not be optimal during transient scenarios. There are several additional drawbacks related to this two layer structure. Often the RTO and MPC layers employ different models, with RTO commonly using a detailed steady state model whereas MPC generally uses simplified dynamic models which steady state values may not exactly match those calculated by the RTO algorithm. Hence the set points computed by the RTO may be sometimes unreachable by the MPC layer. Moreover, the frequency of calculation is typically different for the two layers: MPC is optimized at every sampling period whereas RTO is optimized once a new steady state has been reached. Thus,the RTO’s sampling period is typically in the order of hours or even days whereas for MPC it is in the order of minutes-seconds (Ellis et al., 2014). Since industrialprocesses are subjectedto continuousdisturbances theprocess may never reach a steady state. The fact that the steady state does not always correspond to the optimal economic operation (Budman and Silveston, 2008; Huang et al., 2011, 2012; Limon et al., 2014; Budman et al., 1996) has motivated Economic Model Predictive Control (EMPC) (Ellis and Christofidies, 2013, 2014; Angeli et al., 2012). EMPC maintains many of the strengths of MPC such as the use of dynamic MIMO models, the explicit handling of constraints, feedback etc (Morari and Lee, 1999; Rawlings, 2000; Grune and Pannek, 2011). However, in contrast with conventional MPC, it directly optimizes an economic costinstead ofthe typical quadratic stage costthat penalizes tracking errors with respect to set-points in controlled and manipulated variables. To ensure stability most EMPC algorithms previously reported (Amrit et al., 2011; Diehl et al., 2011; Angeli et al., 2009; Rawlings et al., 2012) used terminal constraints based on a particular steady state value butthesemay lead to conservative results. The need to avoid terminal constraints to reduce conservatismhas beenidentifiedbyHeidarinejadet al.(Heidarinejadet al., 2012) that proposed a two-stage algorithm to control and optimize the system in each stage respectively. In addition to the economic cost, most previously reported EMPC methods used tracking terms in the objective function that penalize deviations in controlled and manipulated variables with respect to the chosen steady state. The calculation of the steady state has to be done off-line by the RTO level. Also, robustness of EMPC to bounded disturbances has been studied in (Heidarinejad et al., 2012) but robustness to model variations has not been explicitly studied.
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کلمات کلیدی:
Economic Model Predictive Control of Chemical Processes with ... https://www.researchgate.net/.../307613981_Economic_Model_Predictive_Control_of_... References, authors & citations for 'Economic Model Predictive Control of Chemical Processes with Parameter Uncertainty' on ResearchGate. [PDF]Economic Model Predictive Control of Chemical Processes - UWSpace https://uwspace.uwaterloo.ca/bitstream/handle/10012/9982/Santander_Omar.pdf;...5 by O Santander - 2015 The objective of any chemical process is to transform raw materials into more ... available Model Predictive Control (MPC) is widely used in industry due to its ... parameters are also explored and the algorithm is devised to compensate for ... [PS]Linear Model Predictive Control of Chemical Processes jbrwww.che.wisc.edu/theses/muske.ps by KR MUSKE - 1995 - Cited by 39 - Related articles cess industries to control constrained, multivariable chemical processes. The ... both linear model predictive control and linear quadratic regulator/estimator. theory ... valid tuning parameters and eliminates the need to tune for nominal stability. [PDF]Nonlinear Model Predictive Control of Chemical Processes with a ... https://www.tu-ilmenau.de/fileadmin/public/.../Mitarbeiter/Dr.../ICIT_IF_006998.pdf by MM Arefi - Cited by 19 - Related articles nonlinear model predictive controller for pH neutralization and. CSTR processes using ... using wiener model for a highly nonlinear chemical process is proposed. .... which minimized for estimation of the vector parameters θ is: 2. 1. 1. 1. 1. 1. 1. [PDF]SLIDING MODE PREDICTIVE CONTROL FOR CHEMICAL PROCESS ... https://ai2-s2-pdfs.s3.amazonaws.com/.../3240117e3030b3f35a3273602969a13d224... by W Garcıa-Gabın - Cited by 16 - Related articles This is a nonlinear process with variable time delay. Finally, the conclusions are presented. 2. SLIDING MODE PREDICTIVE CONTROL. Most SISO plants when ... [PDF]A Lecture on Model Predictive Control - CEPAC cepac.cheme.cmu.edu/pasilectures/lee/LecturenoteonMPC-JHL.pdf by JH Lee - Cited by 2 - Related articles School of Chemical and Biomolecular Engineering. Center for Process Systems Engineering. Georgia Inst. of .... Must be coupled with on-line state / model parameter update ... Model Predictive Control (Receding Horizon Control). Implicitly ... [PDF]Model Predictive Control in the Chemical Process Industry hosted by ... ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/13-161.pdf by B Huyck - 2013 - Cited by 1 - Related articles Model Predictive Control in the Chemical Process. Industry hosted by Industrial ..... 4.1 Model quality and selection parameters for the transfer function models.
