Delba N.C. Melo

Off-line optimization and control for real time integration of a three-phase hydrogenation catalytic reactor

Abstract In order to develop and test the integration procedure, in this paper a real time process integration involving the optimization and control of the process is presented, in this case, with the two-layer approach. The used optimization algorithms were Levenberg–Marquardt and SQP that solve a non-linear least square problem subject to bounds on the variables. The two-layer approach is a hierarchical control structure where an optimization layer calculates the set points and manipulated variables to the advanced controller, which is based on the dynamic matrix control with constraints (QDMC). The non-isothermal dynamic model of the three-phase slurry catalytic reactor with appropriate solution procedure was utilized in this work (Vasco de Toledo, E. C., Santana, P. L., Maciel, M. R. W., & Maciel Filho, R. (2001). Dynamic modeling of a three-phase catalytic slurry reactor. Chemical Engineering Science , 56 , 6055–6061). The model consists on mass and heat balance equations for the catalyst particles as well as for the bulk phases of gas and liquid. The model was used to describe the dynamic behavior of hydrogenation reaction of o -cresol to obtain 2-methil-cyclohexanol, in the presence of a catalyst Ni/SiO 2 .

Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor

Abstract The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.

Real-time optimization for lactic acid production from sucrose fermentation by Lactobacillus plantarum

Abstract A great obstacle in the lactic acid fermentative process is the inhibition of the growth cell by final product as well as by substrate high concentration. A highly efficient process can be developed from a continuous fermentative process with low feed rate of substrate and continuous removal of product. In this work, an investigation study of the optimization and optimal control of operational parameters of a continuous fermentative process for lactic acid production from sucrose is presented. The general idea is to develop suitable tools for defining the optimal operational condition or the set points in the optimization layer in order to obtain an efficient process for lactic acid production. This is in fact a realt-time optimization procedure that should be able to avoid cell growth inhibition and increasing the final concentration of the product. The results show that it is possible to run the system at the best operating conditions, and a highly efficient process for lactic acid production from the sucrose fermentation can be obtained.

Hybrid strategy for real time optimization with feasibility driven for a large scale three-phase catalytic slurry reactor

Abstract In this work it is proposed a suitable hybrid optimization algorithm built up with the association of global and local optimization methods. The adopted computer assisted approach is driven by the need for a global optimization method characterized by efficiency in terms of reduced computational time and efforts whereas being robust. The basic idea is to join the fast convergence properties of gradient-based optimization methods with the wide exploration ability of population-based ones, which makes the developed algorithm a useful tool in real-time applications. Since unavoidable disturbances are present during process operation, efficient optimization algorithms must be available to deal in an on-line fashion with high dimensional and non-linear processes. In the developed code, a Genetic Algorithm (GA) is designed to provide an estimate of the global optimum. Then, a local method of search (the Sequential Quadratic Programming SQP) is used to improve this candidate solution. As case study, the optimization of a three-phase catalytic slurry hydrogenation reactor is considered. The optimization algorithm determines, in real time, the optimal operating condition, defined in terms of maximization of profit. This condition should then be used in an advanced control layer. The results of the hybrid approach are compared with those obtained only considering the micro-GA. The latter approach was able to, alone, solve the optimization problem, but using a large number of generations and, consequently, with higher computational time. The advantage of the hybrid algorithm are that fewer number of generations is employed prior to the SQP utilization. Thus, the new GA-SQP code was able to determine the final solution considerably faster than the isolated GA, reducing the number of functions evaluations for solutions when compared to the number required for the GA to stop the evolution. The hybrid algorithm drives to feasible solution translated into higher profits at reasonable computational costs, being identified as a robust optimization code, useful in real time optimization applications.

A hybrid GA-SQP multi-objective optimization methodology for carbon monoxide pollution minimization in Fluid Catalytic Cracking Process

Abstract In this work a multi-objective hybrid optimization strategy was developed considering genetic algorithms (GA) in series with sequential quadratic programming (SQP). This methodology is used to minimize carbon monoxide emissions of regenerator dense phase at the same time that maximize process conversion in Fluid Catalytic Cracking (FCC). The process is characterized for being a highly nonlinear with strong interactions between process variables. The combination of those optimization algorithms was developed considering final values of GA optimization as initial estimative of SQP algorithm. The reason for that is because initial estimative determined by a stochastic technique is not subject to local minimums and additionally, deterministic technique speed up the calculations and reach the final solution in shorter times in order to obtain optimization objectives with low computational burden and time.

Optimization of a Three-Phase Catalytic Slurry Reactor Using Reduced Statistical Models

The main contribution of this work is to propose the use of a statistical procedure to generate simplified models from a detailed deterministic one for Real Time Optimization. Such models are also used as a tool to map the optimal and feasible region. These simplified models are useful in online optimization coupled to control implementations, since detailed rigorous models may demand time and high computational burden for their solution, which hampers the success of online purposes. In order to illustrate the application of the procedure, a three-phase catalytic reactor was considered. A dynamic heterogeneous mathematical model formulation of the o-cresol hydrogenation reaction was used to simulate the reactor steady-state response to different inlet conditions. The proposed statistical procedure (factorial design) generated simplified models for the reactor exit temperature and the reactant conversion, two useful pieces of process information. The use of these models in an optimization problem is then presented as an illustration of their application in an online real time optimization in a two-layer fashion. The simplified models turn possible the identification of the range of optimal solutions as well as the mapping of the region of optimal solutions very quickly compared to the use of full models, allowing the system to be flexibly operated at high level of performance.

Nonlinear Fuzzy Identification of Batch Polymerization Processes

Abstract First-principles modelling of polymer systems is usually complex and time-consuming, often leading to correlations of restricted range of applicability with unavailable parameters. Thus, the optimal control of polymerization processes using such models is a demanding task, especially when tracked batch reactors in which the systems have typical transient behaviour. In this paper, the fuzzy logic is applied to model discontinuous polymerization reactors. The proposed fuzzy methodology allows the formulation of a global nonlinear long-range prediction model from the conjunction of a number of local linear fuzzy dynamic models. The pilot-plant-scale synthesis of poly(lactic acid) (PLA) and nylon-6 were adopted for performance evaluation of proposed method. Satisfactory results were achieved. Therefore, the proposed technique can be useful to obtain appropriate representations of systems of complex modelling.

Attainment of Kinetic Parameters and Model Validation for Nylon-6 Process

Abstract This work presents the simulation of the hydrolytic polymerization process of Nylon-6in a lab-scale semi-batch reactor, using e-caprolactam as monomer and acetic acid as monofunctional acid chain terminator. The kinetic scheme comprises 6 reactions: 3 main reactions, 2 side reactions associated with the cyclic dimer formation and one monofunctional acid termination. Operating conditions were obtained from previous definitions and kinetic parameters were estimated from experimental data. The proposed optimization problem to estimate the kinetic parameters was solved by the Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA). It was shown that both methods are able to determine the final solution with good precision. The validity of the model was confirmed by comparison of the results obtained by computer simulation using the software Aspen Polymer Plus® and the process real data.

Estimation of Kinetic Parameters and Mathematic Model Validation for Nylon-6 Process

Abstract This work presents the simulation of the hydrolytic polymerization process of nylon-6 in a lab-scale semi-batch reactor, using e-caprolactam as monomer and acetic acid as monofunctional acid chain terminator. The kinetic scheme comprises 6 reactions: 3 main reactions, 2 side reactions associated with the cyclic dimer formation and one monofunctional acid termination. Operating conditions were obtained from previous definitions and kinetic parameters were estimated from experimental data. The proposed optimization problem to estimate the kinetic parameters was solved by the Successive Quadratic Programming (SQP), a deterministic method. It was shown that the method is able to determine the final solution with good precision. The validity of the model was confirmed by comparison of the results obtained by computer simulation using the software Aspen Polymer Plus ® and the process real data.

An Evaluation of a Multi-method Tool for Real-Time Implementation of Two-layer Optimization

Abstract In this work an optimization tool based on Sequential Quadratic Programming (SQP), Levenberg-Marquardt (LM) and Genetic Algorithm (GA) is presented. For the matter of possible alternative computational platforms, it is convenient to have an open toll easily implemented with softwares at low costs. The tool evaluation is carried out in real-time optimization with the concept of two-layer approach. The tool is applied to a threephase catalytic slurry reactor, represented by a deterministic dynamic heterogeneous mathematical model. The kinetic law considers the hydrogenation reaction of o-cresol to obtain 2-methyl-cyclo-hexanol, in the presence of the catalyst Ni/SiO 2 . The advanced controller, which is based on the Dynamic Matrix Control with constraints (QDMC), is used. The present implementation aims to maintain the conversion at the exit of the reactor and to maximize the conversion. The challenge is then to conciliate better results of the optimization and less effort and computational time in the real-time process integration. The results presented showed that LM, SQP (local deterministic methods) and GA (stochastic method) algorithms were able to optimize the process both for the case of maintaining and maximizing o-cresol conversion, when perturbations are introduced into the process. The simulations showed that GA could optimize the process after perturbations were inserted but demanded a CPU time not applicable in real-ime optimizations. LM and SQP, on the other hand, optimized successfully the process, both in terms of achieved conversion and CPU time, presenting potential to be used in realtime applications for the studied three-phase catalytic reactor.