Control strategies for complex chemical processes. Applications in polymerization processes

Vieira, Rafael Augusto Mello; Embiruçu, Marcelo; Sayer, Cláudia; Pinto, José Carlos; Lima, Enrique Luis

doi:10.1016/s0098-1354(03)00055-3

Cited by 25 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include deterministic, gradient-based methods such as Gauss-Newton or Gauss-Marquardt (e.g., [61,89,100,101]), reduced-gradient methods [10,13,28,51,72,86], and successive quadratic programming, which is the most used among industrial DR works, as well as derivative-free, metaheuristic random search algorithms, such as particle swarm optimization (PSO) [18,64]. Applicable deterministic solvers for large-scale nonlinear optimization problems are available as commercial and noncommercial software [48,[125][126][127], and generally incorporate second-derivative information from the optimization model, exploit the sparsity of the KKT matrix and problem structure, deal efficiently with large sets of active constraints, and handle dependent constraints and negative curvature.…”

Section: Optimization Algorithms For Nonlinear Programmingmentioning

confidence: 99%

“…Calculation results will thus consequently depend on the technique used for gradient estimation, where the ideal size of the finite-difference step may depend on the application. Nevertheless, such an effect is rarely mentioned so that it is not possible to state unequivocally that data reconciliation procedures are being used successfully in actual industrial environments to improve the operation of large plants [89,100].…”

Section: Optimization Algorithms For Nonlinear Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Numerical Aspects of Data Reconciliation in Industrial Applications

et al. 2017

View full text Add to dashboard Cite

Abstract:Data reconciliation is a model-based technique that reduces measurement errors by making use of redundancies in process data. It is largely applied in modern process industries, being commercially available in software tools. Based on industrial applications reported in the literature, we have identified and tested different configuration settings providing a numerical assessment on the performance of several important aspects involved in the solution of nonlinear steady-state data reconciliation that are generally overlooked. The discussed items are comprised of problem formulation, regarding the presence of estimated parameters in the objective function; solution approach when applying nonlinear programming solvers; methods for estimating objective function gradients; initial guess; and optimization algorithm. The study is based on simulations of a rigorous and validated model of a real offshore oil production system. The assessment includes evaluations of solution robustness, constraint violation at convergence, and computational cost. In addition, we propose the use of a global test to detect inconsistencies in the formulation and in the solution of the problem. Results show that different settings have a great impact on the performance of reconciliation procedures, often leading to local solutions. The question of how to satisfactorily solve the data reconciliation problem is discussed so as to obtain improved estimates.

show abstract

Section: Optimization Algorithms For Nonlinear Programmingmentioning

confidence: 99%

Section: Optimization Algorithms For Nonlinear Programmingmentioning

confidence: 99%

Numerical Aspects of Data Reconciliation in Industrial Applications

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The appendix briefly summarizes the kinetic mechanism, the model equations and the polymer property models. For further details on model development, we refer to Pontes et al 33 and Embiruçu et al [34][35][36][37][38][39] Model parameters, including kinetic and physical properties constants, have been validated with experimental data from an industrial polymerization process in their work. MI and SE are used to infer the average molecular weight and the polydispersity, respectively.…”

Section: Process Description and Mathematical Modelmentioning

confidence: 99%

Optimal process operation for the production of linear polyethylene resins with tailored molecular weight distribution

et al. 2010

Self Cite

View full text Add to dashboard Cite

An optimization model is presented to determine optimal operating policies for tailoring high density polyethylene in a continuous polymerization process. Shaping the whole molecular weight distribution (MWD) by adopting an appropriate choice of operating conditions is of great interest when designing new polymers or when improving quality. The continuous tubular and stirred tank reactors are modeled in steady state by a set of differential-algebraic equations with the spatial coordinate as independent variable. A novel formulation of the optimization problem is introduced. It comprises a multi-stage optimization model with differential-algebraic equality constraints along the process path and inequality end-point constraints on product quality. The resulting optimal control problem is solved at high computational efficiency by means of a shooting method. The results show the efficiency of the proposed approach and the benefit of predicting and controlling the complete MWD as well as the interplay between operating conditions and polymer properties.

show abstract

“…It is now well established that phenomenological models typically provide a more accurate description of the process, especially for extrapolation, and empirical models are easier to obtain and manipulate during online applications in real time, especially when obtaining experimental data is facilitated (Vieira et al, 2003;Cubillos et al, 2007;Janakiraman et al, 2013). For this reason, some applications require an optimization/adaptation of the model developed and eventually the use of hybrid structures, which take into account empirical knowledge plus phenomeno-logical knowledge, may be considered.…”

Section: Introductionmentioning

confidence: 99%

“…Considering that polymer properties such as molecular weight, polydispersity index, and morphological characteristics are not easy to be obtained online in a polymerization system, models for estimation of these properties are required for the implementation of efficient control and monitoring systems (Prasad et al, 2002;Vieira et al, 2003;Bindlish and Rawlings, 2003;Santos et al, 2008). For polymerization chain reactions, temperature and initial initiator concentration has high influence on the reaction kinetics and the polymer molecular weight distribution, with a direct effect on polymer properties (Sacks et al, 1973;Erdogan et al, 2002;Hosen and Hussain, 2012).…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

Cancelier

Claumann

Bolzan

et al. 2016

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

-This study used a predictive controller based on an empirical nonlinear model comprising a threelayer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

show abstract

Control strategies for complex chemical processes. Applications in polymerization processes

Cited by 25 publications

References 30 publications

Numerical Aspects of Data Reconciliation in Industrial Applications

Numerical Aspects of Data Reconciliation in Industrial Applications

Optimal process operation for the production of linear polyethylene resins with tailored molecular weight distribution

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

Contact Info

Product

Resources

About