The optimal design of membrane systems

This paper introduces a novel optimal design strategy for membrane separation systems. This strategy is characterised by two main features: firstly, detailed models are used. This is essential if sub-optimal and inaccurate solutions are to be avoided. Secondly, an optimisation technique based on genetic algorithms is implemented. The feasibility of the optimal design strategy is investigated using a pervaporation case study. To this end, an existing pervaporation plant is evaluated and a significantly improved design is found.     

CFD modelling of inorganic membrane modules for gas mixture separation

This work is aimed at investigating the capability of a computational fluid dynamics (CFD) approach to reliably predict the fluid dynamic and the separation performances of inorganic membranes modules for gas mixture separation.The simulations are based on the numerical solution of the Navier-Stokes equations on the three dimensional domain representing quite closely the selected module geometry. The membrane is modelled as a selective layer, which allows the permeation of different components as a function of the transport mechanism and the driving force.The computational strategy is strictly evaluated by comparing the results with available experimental data. The simulation predictions show fairly good agreement with the measured permeation data and allow to recognise the critical local fluid dynamic features of the separation module.     

Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm

The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial grinding operation of a lead-zinc ore beneficiation plant. Two objective functions have been identified in this study: (i) throughput of the grinding operation is maximized to maximize productivity and (ii) percent passing of one of the most important size fractions is maximized to ensure smooth flotation operation following the grinding circuit. Simultaneously, it is also ensured that the grinding product meets all other quality requirements, to ensure least possible disturbance in the following flotation circuit, by keeping two other size classes and percent solid of the grinding product and recirculation load of the grinding circuit within the user specified bounds (constraints). Three decision variables used in this study are the solid ore flowrate and two water flowrates at two sumps, primary and secondary, each of them present in each of the two stage classification units. Nondominating (equally competitive) optimal solutions (Pareto sets) have been found out due to conflicting requirements between the two objectives without violating any of the constraints considered for this problem. Constraints are handled using a technique based on tournament selection operator of genetic algorithm which makes the process get rid of arbitrary tuning requirement of penalty parameters appearing in the popular penalty function based approaches for handling constraints. One of the Pareto points, along with some more higher level information, can be used as set points for the previously mentioned two objectives for optimal control of the grinding circuit. Implementation of the proposed technology shows huge industrial benefits.     

Fuzzy modeling and hybrid genetic algorithm optimization of virus removal from water using microfiltration membrane

Membranes are finding increasing applications in disinfection processes including virus removal from water for municipal effluent reuse. The capability of virus removal from water by microfiltration membranes has previously been demonstrated. In this study, the capability of fuzzy logic for modeling and simulation of dead-end microfiltration process for removal of IBR and FMD viruses from water was elucidated. The main parameters indicating membrane performance i.e. flux and rejection were experimentally obtained under different conditions and compared with theoretically calculated flux and rejection using fuzzy inference system. The genetic algorithm which is an efficient and systematic method was employed in the design of fuzzy model for optimization of the poorly understood, irregular and complex membership function with improved performance. Hybrid genetic algorithm was used for optimizing the parameters that are located at the Gaussian membership functions in the premise and consequent of each rule.The results indicated that fuzzy inference system predicts the key parameters i.e. flux and rejection for different operating conditions with an acceptable error. In other words FIS is able to apply for modeling the microfiltration membrane which is mathematically difficult or in many cases an unpredictable process.     

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems.     

Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm

Multi-objective optimization of an operating domestic wastewater treatment plant is carried out using binary coded elitist non-dominated sorting genetic algorithm. Activated sludge model with extended aeration is used for optimization. For optimal plant operation, two different optimization problems are formulated and solved. The first optimization problem involves single-objective function to estimate kinetic parameters in activated sludge model using available plant data by minimizing the weighted sum-of-square errors between computed and plant values. The second optimization problem involves single-, two- and three-objective functions for efficient plant monitoring. In second category problem, objective functions are based on plant performance criteria (i.e., maximizing the influent flow rate of wastewater and minimizing the exit effluent concentration) and economic criteria (i.e., minimizing the plant operating cost). The important decision variables are: mean cell-residence time, mixed liquor suspended solid concentration in the reactor and underflow sludge concentration. Unique solution is obtained for the single-objective function optimization problem whereas a set of non-dominated solutions are obtained for the multi-objective optimization problems. A set of optimal operating conditions are proposed based on the present optimization study, which enhances the plant performance without affecting the discharge effluent quality. Finally, sensitivity analyses of the model results to the kinetic parameters and the kinetic parameters to the GA parameters are carried out to know the sensitivity of the obtained results with changes in the input parameter space.     

Dynamic optimization of membrane dual-type methanol reactor in the presence of catalyst deactivation using genetic algorithm

This paper presents a study on optimization of a membrane dual-type methanol reactor in the presence of catalyst deactivation. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol production in a membrane dual-type methanol reactor. A mathematical heterogeneous model has been used to simulate and compare the membrane dual-type methanol reactor with conventional methanol reactor. An auto-thermal dual-type methanol reactor is a shell and tube heat exchanger reactor which the first reactor is cooled with cooling water and the second one is cooled with synthesis gas. In a membrane dual-type reactor the wall of the tubes in the gas-cooled reactor is covered with a pd–Ag membrane, which is only hydrogen-permselective. The simulation results have been shown that there are optimum values of reacting gas and coolants temperatures to maximize the overall methanol production. Here, genetic algorithms have been used as powerful methods for optimization of complex problems. In this study, the optimization of the reactor has been investigated in two approaches. In the first approach, the optimal temperature profile along the reactor has been studied and then a stepwise approach has been followed to determine the optimal profiles for saturated water and gas temperatures in three steps during the time of operations to maximize the methanol production rate. The optimization methods have enhanced 5.14% and 5.95% additional yield throughout 4 years of catalyst lifetime for first and second optimization approaches, respectively.     

Multi-objective optimization of fuel oil blending using the jumping gene adaptation of genetic algorithm

Production and marketing of heavy fuel oil (HFO) are an easy, effective and economical way to dispose off certain very heavy refinery streams such as short residue (SR, available from the bottom of vacuum distillation units) and clarified liquid oil (CLO, available from the bottom of the main fractionators of fluidized-bed catalytic crackers). Certain lighter streams such as heavy cycle oil (HCO), light cycle oil (LCO) and kerosene, are added to the heavy residual stock to improve its quality in terms of fluidity, combustibility, etc., to be marketed as fuel oil. The present study aims at optimization of the fuel oil blending process to maximize profit, minimize quality give-away, maximize production, minimize use of lighter products such as LCO and kerosene, and maximize the calorific value, etc. Several multi-objective optimization problems have been formulated comprising of two and three-objective functions and solved using the elitist non-dominated sorting genetic algorithm (NSGA-II). This evolutionary technique produces a set of non-dominating (equally good) Pareto optimal solutions from which the operator can choose the one that is most suitable (preferred point). Also, a fixed-length macro–macro mutation operator, inspired by jumping genes in natural genetics, has been used with NSGA-II to solve this problem. This modified algorithm leads to a significant reduction in the computational effort. Indeed, this adaptation can be of immense use in reducing the computational effort for other problems in chemical engineering.     

Application of zeolite T membrane to vapor-permeation-aided esterification of lactic acid with ethanol

Zeolite T membranes were applied to vapor-permeation-aided esterification of lactic acid with ethanol. The hybrid process provided almost complete conversion within a short reaction time by removing water from the reaction mixture. Zeolite T membrane worked steadily for a long time. The reaction time-courses were described by a model based on the assumptions that the esterification obeyed second-order kinetics and the permeation flux of each component was proportional to its concentration in the reaction mixture. The final reaction liquid mixtures consisted mostly of ethyl lactate and ethanol with little ester of polylactic acids, although concentrated lactic acid solution was used as a source.     

Optimization of Petlyuk sequences using a multi objective genetic algorithm with constraints

In this work we use genetic algorithms to optimize Petlyuk sequences using a rigorous design model. A multi objective genetic algorithm (GA) with constraints was formulated and interconnected with the Aspen Plus process simulator to obtain each data point during the search process. In addition to providing more energy-efficient designs than some reported structures, two relevant trends were observed from the results of the case studies; one had to do with the feed location to the prefractionator as a function of the mixture properties, and the other one with optimal structures requiring four interconnecting stages instead of the two normally used for Petlyuk sequences. An application for the separation of azeotropic mixtures is also included. The optimal placement of vapor-liquid interconnections is again shown to be different for each interconnecting stream. The GA showed a robust performance, and was practically independent on the initial values for the search variables.     

Optimization of Helical Microreactors by a Genetic Algorithm Technique

Micromixing in coiled microreactors is attained by a higher pressure drop and more pumping power. The optimum geometries of helically coiled microreactors were determined by multiobjective optimization based on a genetic algorithm (GA). The segregation index (X_s) values of the Villermaux/Dushman reaction were measured in twelve coiled microchannels. The effects of the geometries including curvature diameter and coil pitch on the mixing performance and pressure drop were investigated. The mixing performance of the microreactors and the pressure drop were considered as the GA objectives. The optimum geometries of the studied coiled microchannels with a trade-off between X_s and friction factors were obtained using GA-based multiobjective optimization.     

Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

In this study, the heat transfer optimization(evaporation) and the specification of the FX-70 zeotropic refrigerant flow inside a corrugated pipe have been investigated. Despite the low HTC(HTC), this type of refrigerant is highly applicable in low or medium temperature engineering systems during the evaporation process. To eliminate this defect, high turbulence and proper mixing are required. Therefore, using heat transfer(HT) augmentation methods will be necessary and effective. In order to find the most favorable operating conditions that lead to the optimum combination of pressure drop(PD) and HTC, empirical data, neural networks, and genetic algorithms(GA) for multi-objective(MO)(NSGA II) are used. To investigate the mentioned cases, the geometric parameters of corrugated pipes, vapor quality, and mass velocity of refrigerant were studied. The results showed that with vapor quality higher than 0.8 and corrugation depth and pitch of 1.5 and 7 mm, respectively, we would achieve the desired optimum design.     

Prior detection of genetic algorithm significant parameters: Coupling factorial design technique to genetic algorithm

This work presents an extension of a previous proposed procedure [Costa, C.B.B., Wolf Maciel, M.R., Maciel Filho, R., 2005. Factorial design technique applied to genetic algorithm parameters in a batch cooling crystallization optimization. Computers and Chemical Engineering 29, 2229-2241] to be adopted as a prior analysis in optimization problems to be solved using genetic algorithm (GA). Chemical engineering problems are commonly highly non-linear and possess a large number of variables, sometimes with significant interactions among them. Such characteristics make the optimization problems really difficult to be solved by deterministic methods. GA is an increasing tool for solving this sort of problems. However, no systematic approach to establish the best set of GA parameters for any problem was found in the literature and a relatively easy to use and meaningful approach is proposed and proved to be of general application. The proposed approach consists of applying factorial design, a well-established statistical technique to identify the most meaningful information about the influences of factors on a specific problem, as a support tool to identify the GA parameters with significant effect on the optimization problem. This approach is very useful in conducting further optimization works, since it discharges GA parameters that are not statistically significant for the evolutionary search for the optimum, saving time and computation burden in evolutionary optimization studies.     

Separation of binary mixtures by dense membrane processes: influence of inert gas entrance under variable downstream pressure conditions

The influence of an inert gas on the separation performances of a dense polymeric membrane module working under partial vacuum on the downstream side, such as possibly encountered in gas permeation, vapor permeation or pervaporation, has been investigated through an experimental and theoretical study. A whole range of situations on the downstream side, covering ideal vacuum pumping (i.e. zero downstream pressure under leak free conditions) to inert gas sweeping under atmospheric pressure has been tested. A theoretical framework, previously developed for single permeant situation has been extended to the multicomponent case. The separation of methanol and 2-propanol by a dense silicone rubber membrane confirms the ability of this simple modelling strategy to offer quantitative predictions of the permeate composition under variable downstream pressure and inert gas flowrate conditions. Based on this observation, the implications of an inert gas contribution on pervaporation or gas separation operation are discussed, particularly in relationship to the global energy consumption of the system or to analytical devices making use of a gas sweep.     

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains. After the feasible region is obtained, the sequential quadratic programming (SQP) is implemented to improve the solution. The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm, and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment.     

Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm

Compact and economic processes for methanol synthesis from syngas demand a new catalyst that is active under low-pressure and low temperature. Combinatorial approach comprising a high-pressure high-throughput screening (HTS) reactor system, an artificial neural network (NN), and a genetic algorithm (GA) was applied for the catalyst development. A variety of 96 microplates were used in the HTS reactor system for both preparation and activity testing to handle 96 catalyst samples simultaneously. Activity test results were used as training data for NN. After training, the NN can map catalyst activity as a function of catalyst composition and parameters for catalyst preparation. GA was used as an optimization tool to find maximum catalyst activity in the trained artificial neural network. Composition of methanol synthesis catalyst (Cu–Zn–Al–Sc–B–Zr), calcination temperature and the amount of precipitant were optimized simultaneously under pressure (1 MPa) because optimum catalyst composition is usually affected by both preparation and reaction conditions. The composition of the optimum catalyst was Cu/Zn/Al/Sc/B/Zr=43/17/23/11/0/6 prepared using 2.2 times the equivalent of oxalic acid and calcined at 605 K. The activity (427 g-MeOH/kg-cat./h) was much higher than that of industrial catalyst (250 g-MeOH/kg-cat./h) at 1 MPa, 498 K.     

Optimization of ODHE membrane reactor based on mixed ionic electronic conductor using soft computing techniques

This work presents the optimization of the operating conditions of a membrane reactor for the oxidative dehydrogenation of ethane. The catalytic membrane reactor is based on a mixed ionic–electronic conducting material, i.e. Ba_0.5Sr_0.5Co_0.8Fe_0.2O_δ−3, which presents high oxygen flux above 750 °C under sufficient chemical potential gradient. Specifically, diluted ethane is fed into the reactor chamber and air (or diluted air) is flushed to the other side of the membrane. A framework based on Soft Computing techniques has been used to maximize the ethylene yield by simultaneously varying five operation variables: nominal reactor temperature (Temp); gas flow in the reaction compartment (QHC); gas flow in the oxygen-rich compartment (QAir); ethane concentration in the reaction compartment (%C₂H₆); and oxygen concentration in oxygen-rich compartment (%O₂). The optimization tool combines a genetic algorithm guided by a neural network model. This shows how the neural network model for this particular problem is obtained and the analysis of its behavior along the optimization process. The optimization process is analyzed in terms of: (1) catalytic figures of merit, i.e., evolution of yield and selectivity towards different products and (2) framework behavior and variable significance. The two experimental areas maximizing the ethylene yield are explored and analyzed. The highest yield reached in the optimization process exceeded 87%.     

Multifunctional system for treatment of wastewaters from adhesive-producing industries: separation of solids and oxidation of dissolved pollutants using doted microfiltration membranes

To eliminate non-biodegradable organic compounds from wastewater application of semiconductor photocatalysis has been done. Experiments have been performed on immobilizing the photocatalyst titanium dioxide in an organic PAN microfiltration membrane and illuminated by UV-A light in order to improve oxidation performance and avoid particle separation. The organic pollutants are oxidized by in situ-produced hydroxyl radicals or directly by the catalyst. The membrane causes a convective flow of the pollutant towards the catalyst. The separation properties of the membrane can be used in a multifunctional way to extract remaining solid particles. A module containing membranes and a UV light source was developed. 4-Chlorophenol was completely mineralized at a high reaction rate. A two-step process has been developed for the clarification of highly polluted waste waters from adhesive-producing plants. First, the suspended solids which reach up to 10% of the mass stream are precipitated, flocculated and separated by means of a decanting centrifuge and flotation. Then the photocatalytic process was applied on the dissolved organic contents.     

Removal of chlorinated volatile organic contaminants from water by pervaporation using a novel polyurethane urea-poly (methyl methacrylate) interpenetrating network membrane

Polymer membranes are potentially selective for separation of organic compounds from a mixture by pervaporation. A novel crosslinked hydroxyterminated polybutadiene based (HTPB) polyurethane urea (PUU)-poly (methyl methacrylate) (PMMA) interpenetrating network (IPN) membrane has been developed for the selective removal of chlorinated volatile organic compounds (VOCs) such as 1,1,2,2-tetrachloroethane, chloroform, carbon tetrachloride, trichloroethylene present in water in very low concentration by pervaporation. IPNs of different PMMA content and also different crosslink density were used. Since the selective permeation and diffusion of the VOCs through the membrane are dependent on their interaction with the membrane material, their sorption and diffusion behaviors through the membrane were also investigated by swelling the membrane in pure VOCs. The sorption and diffusion behaviors were explained with the help of their solubility parameter data and calculated interaction parameter data of the membrane polymers with the VOCs. From the swelling kinetics data, diffusion coefficients of the VOCs through the membrane were calculated. Diffusion coefficients increased with the increase in crosslink density and PMMA content in the membrane. In pervaporation experiment, concentrations of chlorinated organic compounds in feed were varied from 100 ppm (0.01%) to 1000 ppm (0.1%). All the three IPN membranes showed excellent separation performances of the chlorinated VOCs from water. One IPN containing 26% PMMA (PUU-PMMA-3) produced 88.7% trichloroethylene in permeate, trichloroethylene flux and a separation factor of 7842 from a 0.1% aqueous feed after a pervaporation run of 3 h at . All the three IPN membranes of different compositions have shown the separation performances, viz., flux and separation factor for all the VOCs in the order .     

Surrogate-model-based,particle swarm optimization,and genetic algorithm techniques applied to the multiobjective operational problem of the fluid catalytic cracking process

Abstract

This article provides a concise multiobjective optimization methodology for an industrial fluid catalytic cracking unit (FCCU) considering stochastic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), based on surrogates or meta-models in order to approximate the objective function. A FCCU was considered and simulated in an AspenONE process simulator. In addition the article examines the claim that PSO has the same effectiveness (finding the optimal global solution) as GA, but with significantly better computational efficiency (fewer function evaluations). The optimization results obtained with the PSO technique, based on the evaluation of less functions and adjustment of less parameters, showed a 3% increase in yield of naphtha as compared to results obtained with the GA technique. Finally, the results of the optimization obtained with the stochastic optimization techniques were compared and analyzed with a deterministic one. The performance targets of the multiobjective operational optimization supported the FCCU design and production planning to ensure refinery profitability and a regulatory environment.