Rita M.B. Alves

Modeling the kinetics of the coalescence of water droplets in crude oil emulsions subject to an electric field, with the cellular automata technique

In this study, the concept of cellular automata is applied in an innovative way to simulate the separation of phases in a water/oil emulsion. The velocity of the water droplets is calculated by the balance of forces acting on a pair of droplets in a group, and cellular automata is used to simulate the whole group of droplets. Thus, it is possible to solve the problem stochastically and to show the sequence of collisions of droplets and coalescence phenomena. This methodology enables the calculation of the amount of water that can be separated from the emulsion under different operating conditions, thus enabling the process to be optimized. Comparisons between the results obtained from the developed model and the operational performance of an actual desalting unit are carried out. The accuracy observed shows that the developed model is a good representation of the actual process.

ANALYSIS AND DETECTION OF OUTLIERS AND SYSTEMATIC ERRORS IN INDUSTRIAL PLANT DATA

This article describes the analysis of industrial process data to detect outliers and systematic errors. Data reconciliation is an important step in adjusting mathematical models to plant data. The quality of the data directly affects the quality of adjustment of the model for modeling, simulation, and optimization purposes. To detect these errors in a multivariable system is not an easy task. If the origin of the abnormal values is known, these values can be immediately discarded. On the other hand, if an error or an extreme observation is not clearly justified, the decision whether or not to discard these values must be based on statistical analysis. In this work, in addition to process knowledge, the methodology employed involves an approach based on statistical analysis, first-principle equations, neural network models, and a composite of these. The neural network based approach was used to represent the process in order to classify similar inputs and outputs, i.e., to identify clusters. The elimination of gross errors was performed by the similarity principle or by hypothesis testing for means. The system studied is the Isoprene Production Unit of BRASKEM, the largest Brazilian petrochemical plant. The analysis of the process was undertaken by using a one-year database. The frequency of the data collection of the monitoring variables was 15 minutes.

Modeling of Kinetics of Water Droplets Coalescence in Crude Oil Emulsion Subjected to an Electrical Field.

Abstract Water is used in petroleum desalting units to dilute and remove the salted water droplets that the crude oil contains. The basic processes promote the coalescence of small droplets of conducting water dispersed in a crude oil emulsion. In order to make separation easier, the emulsion is distributed horizontally between two electrodes and subjected to an electrical field, which generates an attractive force among the droplets, promoting coalescence phenomena and further sedimentation. The main purpose of this study is to reduce the demand of fresh water and the liquid effluent generation in refineries. This paper presents a new model developed in order to calculate the droplets velocity by using the balance of the acting forces. The model is able to determine the droplets trajectory in order to define if they can be separated from the continuous phase. Besides the deterministic approaches based on traditional equations, the model uses also the concept of cellular automata. Thus it is possible to solve the problem in a stochastic way and to show visually the sequence of droplets collisions and coalescence phenomena. This methodology enables to calculate the amount of water that can be separated of the emulsion for a number of different operating conditions and then to optimize the process. Comparisons between the obtained results by the developed model and the operational performance of a real desalting unit are carried out. A good accuracy is observed, which shows that the real process is very well represented by the developed model.

New approach for the prediction of azeotropy in binary systems

Abstract A new approach for the prediction of azeotrope formation between components in a mixture, that does not require vapor–liquid equilibrium calculations, is presented. The method employs neural networks to correlate azeotropic data for binary mixtures with a series of macroscopic and microscopic properties of the pure components, without explicit consideration of non-ideality of mixture. The model fails to make a clear prediction regarding azeotropy in only a relatively small number of situations in which structurally homologous molecules are known to exhibit quite distinct azeotropic behavior.

Surrogate-based Optimization Approach to Membrane Network Synthesis in Gas Separation

Abstract This paper is concerned with the synthesis of membranes networks for gas separation using a surrogate-based optimization approach. The developed methodology accounts for the main sources of non-ideality in membrane processes based on a mechanistic model. These non-idealities are typically neglected in membrane network synthesis formulations, which often results in inaccurate predictions. The optimization proceeds by a trust-region algorithm and relies on grey-box surrogates that combine a shortcut model with response surface models. The methodology is applied to a case study in natural gas sweetening, where it is shown that the surrogate predictions are within 0.1% of the mechanistic model upon convergence of the trust-region algorithm. Comparisons with classical, constant-permeability surrogates confirm the benefits of the proposed approach.

Mathematical Modeling of an Industrial Delayed Coking Unit

Abstract This paper describes the mathematical modeling of the physical-chemical phenomena in the coking furnace and coke drum in a steady-state delayed coking unit based in a lumped kinetic scheme and vapor-liquid equilibrium conditions. Feedstock and products composition, and molecular structure are also required. For better understanding of the coking process and effect of the feedstock physical-chemical properties on yield and quality parameter of the products, especially the petroleum coke, molecular characterization of crude oil and heavy fraction was carried out by MALDI TOF mass spectrometry. Mathematical algorithm has been developed based on experimental data and operating conditions from an industrial delayed coking unit. A good prediction capability of the model and an accurate industrial unit representation of the industrial unit is expected. The developed model will be used for monitoring the industrial process and implementing real time optimization (RTO). Meanwhile, the acquired knowledge obtained during the model development has already improved the quality of green coke produced and the operational procedure of industrial plant.

Carbon Dioxide as a Feedstock for the Chemical Industry. Production of Green Methanol

Carbon dioxide (CO2) has a restricted use as a feedstock in the chemical industry. Its emission and accumulation in the atmosphere in great quantities have been largely associated to the greenhouse effect. Thus, the conversion of CO2 into value-added chemical products will bring on not only economical benefits but also far greater importance for the environmental stewardship. The hydrogenation of CO2 into methanol (CH3OH) is a promising route to fix CO2 in the chemical industry. The reaction can be carried out by Cu and Zn-based catalysts, which are highly selective to CH3OH formation. However, the reaction is severely affected by the thermodynamic equilibrium. Therefore, the effect of reaction temperature and pressure needs to be known in order to achieve high conversion rates of CO2 into CH3OH, thus allowing the development of feasible and highly efficient conversion processes.

Operational Strategy for Water Supply in a Petrochemical Plant. Steady-State and Dynamic Approaches

Abstract The aim of this work is to present a mathematical model developed in order to simulate several operating scenarios involving water supply to a vapor generation unit in a petrochemical plant. The operational strategy suggested involves steady state and dynamic considerations. The case study is a petrochemical plant that uses water from two distinct sources with different qualities and prices. Thus, two case studies were carried out: 1. definition of the optimal proportion between both sources of water in case of supply stability: a model based on the minimum total cost per cubic meter of useful water was developed; 2. definition of the best operational strategy in case of failure in the main water source: a dynamic model was developed representing the flow rates as a time-dependent mathematical function in order to evaluate possible disturbances and all the operating conditions of interests, including the risk of total lack of water for vapor generation, which would cause the whole plant to shut down. The results show that it is possible to preserve operational continuity and stability, with lower water consumption and wastewater generation.

Water reuse: A successful almost zero discharge case

Abstract This paper presents a procedure to optimize a real problem of freshwater and wastewater reuse allocation. The case study is an industrial polypropylene unit and the solution, achieved is an almost zero discharge case. The problem was decomposed into subsystems according the type of approach used to water minimization: process changes, regeneration reuse and regeneration recycling. For the regeneration approach, an innovative photochemical process capable to remove all the organic compounds contained in the wastewater in order to make it suitable to be reused in the process was used. For process changes approach, since the major water used in factory is for the cooling process system, a hybrid system composed by air cooler and wet cooling tower is been proposed to replace the wet cooling tower. The air cooler system is used first and the final temperature approach is achieved by the wet cooling tower. Thus, the main scope of the present work is to show that is possible to reach the “almost zero discharge” for an industrial case by using innovative wastewater treatment technologies together with optimization of water/air cooling systems. The results obtained prove that the water minimization techniques used can effectively reduce overall fresh water demand and the overall effluent generated, resulting in lower costs of fresh water and effluent treatment costs.

Multi-objective optimization of an industrial isoprene production unit by using genetic algorithm approach

The present work deals with the multi-objective optimization of an industrial Isoprene production unit by using Genetic Algorithm (GA). The purpose of this plant is to produce high purity Isoprene for obtaining synthetic and thermoplastic rubber from a C5 cut arising from a pyrolysis gasoline unit. The chemical process consists basically of a dimerization reactor and a separation column train. The Isoprene industrial process is a complex process, and not easy to be solved by commercial simulators mainly due to the lack of thermodynamics properties. A neural network approach has been previously developed in order to model industrial process from historical data (Nascimento et al., 2000; Alves and Nascimento, 2002 and 2004a). In this work, a genetic algorithm (GA)-search, an optimization technique based on principle of natural genetics, is used to perform the optimization procedure. GAs are chosen as an optimization tool because of their successful application in many others engineering and industrial optimization problems (Alves et al., 2004b; Laquerbe et al., 2001; Pibouleau et al., 1999). The reason for a great part of their success is their ability to exploit the information accumulated about an initially unknown search space in order to bias subsequent searches into useful subspaces, i.e., their adaptation. Moreover, GAs use objective functions information and not derivatives or other auxiliary knowledge to perform an effective search for better and better structures. Then, the aim of this paper is to present and discuss the applicability of a genetic algorithm as an alternative procedure for a multi-objective optimization of an industrial process that may be difficult to handle by classical methods (Lim et al., 2001). In this case the optimization of the entire plant involves 21 variables to be optimized. So, in order to decrease the combinatorics of the problem, the global model was divided into three sections and each one was optimized separately, but sequentially, by using the optimal conditions from previous optimization section procedure. For this, a multi-objective genetic algorithm (MOGA) based on a Pareto sort (PS) procedure was implemented to manage this specific problem. The optimization procedure employed in this work does not require necessarily a formal objective function. It should deal with either qualitative set of process constraints and quantitative or economical analysis by using an objective functions that describe the technico-economical goasl. It is important to keep in mind the main objective to be achieved, for example, higher production for a given product specification at lower energy consumption.. A constrained optimization procedure was used to take into account product quality, safe operations conditions, and energy consumption. The GA model developed may find solutions near the global optimum within reasonable time and computational costs.