ABSTRACT The presence of contaminants in diesel oil is severely controlled. These components affect the diesel properties, causing impacts on the emissions of pollutants and compromising its commercialization. The Brazilian legislation imposes the use of diesel S10. In this work, a hydrotreatment unit fed with multiple diesel streams was optimized using a phenomenological mathematical model. A three-phase model of a tricked-bed reactor was considered. The optimization of the process was performed with the interior point method considering the hydrodesulfurization reaction of diesel. It was proposed to minimize the operating cost, keeping the amount of sulfur in the final product below the maximum allowed value. The EMSO process simulator was used to model, simulate and optimize the hydrotreatment process. It was observed that the optimization strategy drove the contaminant content into the diesel S10 specification range, as well as reducing the process operating cost by increasing the feed temperature and hydrogen flowrate and adjusting the feed composition blend of the available diesel streams.

Abstract The hydrodealkylation process of toluene (HDA) has been used as a case study in a large number of control studies. However, in terms of industrial application, this process has become obsolete and is nowadays superseded by new technologies capable of processing heavy aromatic compounds, which increase the added value of the raw materials, such as the process of transalkylation and disproportionation of toluene (TADP). TADP also presents more complex feed and product streams and challenging operational characteristics both in the reactor and separator sections than in HDA. This work is aimed at proposing the TADP process as a new benchmark for plantwide control studies in lieu of the HAD process. For this purpose, a nonlinear dynamic rigorous model for the TADP process was developed using Aspen Plus™ and Aspen Dynamics™ and industrial conditions. Plantwide control structures (oriented to control and to the process) were adapted and applied for the first time for this process. The results show that, even though both strategies are similar in terms of control performance, the optimization of economic factors must still be sought.

Abstract The use of carbon-based waste biomass in the production of plastics can partially meet the growing demand for plastics in the near future. An interest in the production of ethylene from bioethanol has been renewing, motivated mainly by environmental appeal and economics. The main objective of this work is the development of a mathematical model for simulation and optimization of the production of ethylene by the dehydration of ethanol, improving the performance of the process. The phenomenological model proposed is based on mass, momentum and energy balances for the process. The results obtained are satisfactory in comparison with theoretical results and experimental data found in the literature.

Abstract The high octane number of pyrolysis gasoline (PYGAS) explains its insertion in the gasoline pool. However, its use is troublesome due to the presence of gum-forming chemicals which, in turn, can be removed via hydrogenation. The use of Langmuir-Hinshelwood kinetic models was evaluated for hydrogenation of styrene, a typical gum monomer, using Pd/9%Nb2O5-Al2O3 as catalyst. Kinetic models accounting for hydrogen dissociative and non-dissociative adsorption were considered. The availability of one or two kinds of catalytic sites was analyzed. Experiments were carried out in a semi-batch reactor at constant temperature and pressure in the absence of transport limitations. The conditions used in each experiment varied between 16 - 56 bar and 60 - 100 ºC for pressure and temperature, respectively. The kinetic models were evaluated using MATLAB and EMSO software. Models using adsorption of hydrogen and organic molecules on the same type of site fitted the data best.

Abstract In this work, we present a highly-accurate technique of model order reduction applied to staged processes. The proposed method reduces the dimension of the original system based on null values of moment-weighted sums of heat and mass balance residuals on real stages. To compute these sums of weighted residuals, a discrete form of Gauss-Lobatto quadrature was developed, allowing a high degree of accuracy in these calculations. The locations where the residuals are cancelled vary with time and operating conditions, characterizing a desirable adaptive nature of this technique. Balances related to upstream and downstream devices (such as condenser, reboiler, and feed tray of a distillation column) are considered as boundary conditions of the corresponding difference-differential equations system. The chosen number of moments is the dimension of the reduced model being much lower than the dimension of the complete model and does not depend on the size of the original model. Scaling of the discrete independent variable related with the stages was crucial for the computational implementation of the proposed method, avoiding accumulation of round-off errors present even in low-degree polynomial approximations in the original discrete variable. Dynamical simulations of distillation columns were carried out to check the performance of the proposed model order reduction technique. The obtained results show the superiority of the proposed procedure in comparison with the orthogonal collocation method.

A method for analysis of the electric potential profile in saline solutions was developed for systems with one or two infinite flat plates. A modified Poisson-Boltzmann equation, taking into account nonelectrostatic interactions between ions and surfaces, was used. To solve the stated problem in the steady-state approach the finite-difference method was used. For the formulated pseudo-transient problem, we solved the set of ordinary differential equations generated from the algebraic equations of the stationary case. A case study was also carried out in relation to temperature, solution concentration, surface charge and salt-type. The results were validated by the stationary problem solution, which had also been used to verify the ionic specificity for different salts. The pseudo-transient approach allowed a better understanding of the dynamic behavior of the ion-concentration profile and other properties due to the surface charge variation.

A well-evaluated state covariance matrix avoids error propagation due to divergence issues and, thereby, it is crucial for a successful state estimator design. In this paper we investigate the performance of the state covariance matrices used in three unconstrained Extended Kalman Filter (EKF) formulations and one constrained EKF formulation (CEKF). As benchmark case studies we have chosen: a) a batch chemical reactor with reversible reactions whose system model and measurement are such that multiple states satisfy the equilibrium condition and b) a CSTR with exothermic irreversible reactions and cooling jacket energy balance whose nonlinear behavior includes multiple steady-states and limit cycles. The results have shown that CEKF is in general the best choice of EKF formulations (even if they are constrained with an ad hoc clipping strategy which avoids undesired states) for such case studies. Contrary to a clipped EKF formulation, CEKF incorporates constraints into an optimization problem, which minimizes the noise in a least square sense preventing a bad noise distribution. It is also shown that, although the Moving Horizon Estimation (MHE) provides greater robustness to a poor guess of the initial state, converging in less steps to the actual states, it is not justified for our examples due to the high additional computational effort.

The molecular weight distribution (MWD) and its parameters are of the fundamental importance in the characterization of polymers. Therefore, the development of techniques for faster MWD determination is a relevant issue. This paper aims at implementing one of the relaxation models from double reptation theory proposed in the literature and analyzing the numeric strategy for the evaluation of the integrals appearing in the relaxation model. The inverse problem, i.e., the determination of the MWD from rheological data using a specified relaxation model and an imposed distribution function was approximated. Concerning the numerical strategy for the evaluation of the integrals appearing in the relaxation models, the use of Gauss-Hermite quadrature using a new change of variables was proposed. In the test of samples of polyethylene with polydispersities less than 10, the application of this methodology led to MWD curves which provided a good fit of the experimental SEC data.

This work deals with a new numerical methodology to solve the Navier-Stokes equations based on a finite volume method applied to structured meshes with co-located grids. High-order schemes used to approximate advective, diffusive and non-linear terms, connected with multiblock partition techniques, are the main contributions of this paper. Combination of these two techniques resulted in a computer code that involves high accuracy due the high-order schemes and great flexibility to generate locally refined meshes based on the multiblock approach. This computer code has been able to obtain results with higher or equal accuracy in comparison with results obtained using classical procedures, with considerably less computational effort.

This work explores a two-step, complexity reducing methodology, to analyze heat integration opportunities of an existing Olefins Plant, identify and quantify reduction of energy consumption, and propose changes of the existing heat exchanger network to achieve these goals. Besides the analysis of plant design conditions, multiple operational scenarios were considered to propose modifications for handling real plant operation (flexibility). On the strength of plant complexity and large dimension, work methodology was split into two parts: initially, the whole plant was evaluated with traditional Pinch Analysis tools. Several opportunities were identified and modifications proposed. Modifications were segregated to represent small and independent portions of the original process. One of them was selected to be re-analyzed, considering two scenarios. Reduction of problem dimension allowed mathematical methodologies (formulation with decomposition, applying LP, MILP and NLP optimization methods) to synthesize flexible networks to be applied, generating a feasible modification capable of fulfilling the proposed operational scenarios.

Fluidized-bed Catalytic Cracking (FCC) is a process subject to frequent variations in the operating conditions (including feed quality and feed rate). The production objectives usually are the maximization of LPG and gasoline production. This fact makes the FCC converter unit an excellent opportunity for real-time optimization. The present work aims to apply a dynamic optimization in an industrial FCC converter unit, using a mechanistic dynamic model, and to carry out a numerical analysis of the solution procedure. A simultaneous approach was used to discretize the system of differential-algebraic equations and the resulting large-scale NLP problem was solved using the IPOPT solver. This study also does a short comparison between the results obtained by a potential dynamic real-time optimization (DRTO) against a possible steady-state real-time optimization (RTO) application. The results demonstrate that the application of dynamic real-time optimization of a FCC converter unit can bring significant benefits in production.

A mathematical model for the free radical polymerization of styrene is developed to predict the steady-state and dynamic behavior of a continuous process. Special emphasis is given for the kinetic and thermodynamic models, where the most sensitive parameters were estimated using data from an industrial plant. The thermodynamic model is based on a cubic equation of state and a mixing rule applied to the low-pressure vapor-liquid equilibrium of polymeric solutions, suitable for modeling the auto-refrigerated polymerization reactors, which use the vaporization rate to remove the reaction heat from the exothermic reactions. The simulation results show the high predictive capability of the proposed model when compared with plant data for conversion, average molecular weights, polydispersity, melt flow index, and thermal properties for different polymer grades.

Computational Fluid Dynamics (CFD) is widely used by polymer processing industries in order to evaluate polymeric fluid flows. A successful computational code must provide reliable predictions (modeling) in a fast and efficient way (simulation). In this work, a new approach to solve the governing equations of viscoelastic fluid flows is proposed. It is based on the finite-volume method with collocated arrangement of the variables, using high-order approximations for the linear and nonlinear average fluxes in the interfaces and for the nonlinear terms obtained from the discretization of the constitutive equations. The approximations are coupled to the Weighted Essentially Non-Oscillatory (WENO) scheme to avoid oscillations in the solution. The Oldroyd-B model is used to describe the rheological behavior of the viscoelastic fluid. The average values of the variables in the volumes are used during the resolution, and the point values are recovered in the post-processing step by deconvolution of the average values. The nonlinear system, resulting from the discretization of the equations, is solved simultaneously using a Newton-like method. The obtained solutions are oscillation-free and accurate, demonstrated by the application on a classic problem in computational fluid dynamics, the slip-stick flow.

Product development and advanced control applications require models with good predictive capability. However, in some cases it is not possible to obtain good quality phenomenological models due to the lack of data or the presence of important unmeasured effects. The use of empirical models requires less investment in modeling, but implies the need for larger amounts of experimental data to generate models with good predictive capability. In this work, nonlinear phenomenological and empirical models were compared with respect to their capability to predict the melt index and polymer yield of a low-density polyethylene production process consisting of two fluidized bed reactors connected in series. To adjust the phenomenological model, the optimization algorithms based on the flexible polyhedron method of Nelder and Mead showed the best efficiency. To adjust the empirical model, the PLS model was more appropriate for polymer yield, and the melt index needed more nonlinearity like the QPLS models. In the comparison between these two types of models better results were obtained for the empirical models.

The modeling, simulation, and dynamic optimization of an industrial reaction system for acetylene hydrogenation are discussed in the present work. The process consists of three adiabatic fixed-bed reactors, in series, with interstage cooling. These reactors are located after the compression and the caustic scrubbing sections of an ethylene plant, characterizing a front-end system; in contrast to the tail-end system where the reactors are placed after the de-ethanizer unit. The acetylene conversion and selectivity profiles for the reactors are optimized, taking into account catalyst deactivation and process constraints. A dynamic optimal temperature profile that maximizes ethylene production and meets product specifications is obtained by controlling the feed and intercoolers temperatures. An industrial acetylene hydrogenation system is used to provide the necessary data to adjust kinetics and transport parameters and to validate the approach.