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.     

Optimization of membrane gas separation systems using genetic algorithm

Genetic algorithm is applied for the optimization of the membrane gas separation systems. Air separation for enriched oxygen production is the selected system for investigation. Optimizations for single and triple objective functions are studied. The optimization problem involves the selection of the optimal system configurations from three alternatives, including continuous membrane column (CMC), single stripper permeator (SSP), and two stripper in series permeator (TSSP), as well as the optimal operating conditions. Models of the three configurations and the genetic algorithm procedure are computerized. The objective functions discussed are the Rony separation index, power consumption per unit equivalent pure oxygen, and the membrane area. Both high-pressure and low-pressure (vacuum) operation modes are optimized and the effects of different oxygen product purity and feed rate are analyzed. For single objective function optimization, the solutions obtained using genetic algorithm are slightly inferior in one case but superior in other cases compared to those by pure mathematical optimization methods. For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated. In general, compared to high-pressure operation mode, the product recovery and power consumption for low-pressure operation mode are lower. For almost all the cases studied, CMC configuration with its high flexibility appears in the optimal solutions.     

Mathematical modeling and genetic algorithm optimization of clove oil extraction with supercritical carbon dioxide

In the present study, a mathematical modeling for extraction of oil from clove buds using supercritical carbon dioxide was performed. Mass transfer is based on local equilibrium between solvent and solid. The model was solved numerically, and model estimation was validated using experimental data. For optimization, the clove oil equilibrium constant between solid and supercritical phase was determined by a theoretical method using fugacity concept, consequently the genetic algorithm for obtaining optimal operational conditions was used. The optimal conditions which obtained the highest amount of clove oil were pressure of 10 MPa and temperature of 304.2 K.     

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.     

Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach

Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) of oily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimize operating parameters involved in the process model result in attaining desired permeate flux, is of fundamental great interest from an economical and technical point of view. Accordingly, this current research proposed a hybrid process modeling and optimization based on computational intelligence paradigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meets the challenge of specified-objective based on two steps: first the development of bio-inspired approach based on ANN, trained, validated and tested successfully with experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale in which the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and filtration time as inputs; and gave permeate flux as an output. Subsequently, the 5-dimensional input space of the ANN model portraying process input variables was optimized by applying GA, with a view to realizing maximum or minimum process output variable. The results obtained validate the estimates of the ANN–GA technique with a good accuracy. Finally, the relative importance of the controllable operation factors on flux decline is determined by applying the various correlation statistic techniques. According to the result of the sensitivity analysis based on the correlation coefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.     

Mathematical modeling and numerical simulation of ammonia removal from wastewaters using membrane contactors

This study investigates simulation of ammonia transport through membrane contactors. The system studied involves feed solution of NH₃, a dilute solution of sulfuric acid as solvent and a membrane contactor. The model considers coupling between equations of motion and convection-diffusion. Finite element method was applied for numerical calculations. The effect of different parameters on the removal of ammonia was investigated. The simulation results revealed that increasing feed velocity decreases ammonia removal in the contactor. The modeling findings also showed that the developed model is capable to evaluate the effective parameters which involve in the ammonia removal by means of contactors.     

Experimental investigation,modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm

In this work, treatment of oily wastewaters with commercial polyacrylonitrile (PAN) ultrafiltration (UF) membranes was investigated. In order to do these experiments, the outlet wastewater of the API (American Petroleum Institute) unit of Tehran refinery, is used as the feed. The purpose of this paper was to predict the permeation flux and fouling resistance, by applying artificial neural networks (ANNs), and then to optimize the operating conditions in separation of oil from industrial oily wastewaters, including trans-membrane pressure (TMP), cross-flow velocity (CFV), feed temperature and pH, so that a maximum permeation flux accompanied by a minimum fouling resistance, was acquired by applying genetic algorithm as a powerful soft computing technique. The experimental input data, including TMP, CFV, feed temperature and pH, permeation flux and fouling resistance as outputs, were used to create ANN models. This fact that there is an excellent agreement between the experimental data and the predicted values was shown by the modeling results. Eventually, by multi-objective optimization, using genetic algorithm (GA), an optimization tool was created to predict the optimum operating parameters for desired permeation flux (i.e. maximum flux) and fouling resistance (i.e. minimum fouling) behavior. The accuracy of the model is confirmed by the comparison between the predicted and experimental data.     

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%.     

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.     

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.     

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.     

Reflective color reduction using genetic algorithm optimization

A subset of colors often needs to be selected to represent a full set. In one such application, a multi‐band color sensor is used to measure reflective color samples, and a matrix transformation method is used to recover the reflectance spectrum of the measured sample. To achieve this, a group of training colors needs to be selected to calculate the transformation matrix. A genetic algorithm (GA) has been developed to optimize the selection of the subset of training colors, and the result is compared with those obtained using random selection or a traditional culling algorithm. In a simulation study, the GA gives better results.     

Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm

A new method for catalyst design was discussed based on artificial neural network, which was developed to simulate the relations between catalyst components and catalytic performance in the previous research. For enhancing efficiency of catalyst design, a new hybrid GA tested by TSP was generated for global optimization to design the ‘optimal’ catalyst. A multi-turn design strategy was described. Based on the previous research, the design method was applied for designing multi-component catalyst for methane oxidative coupling, some better catalysts, in which C₂ hydrocarbon yields were greater than 25% were designed. When reacting on the best catalyst, GHSV was , CH₄:O₂ was 3, reaction temperature was , methane conversion and C₂ hydrocarbon selectivity were 37.79% and 73.50%, respectively (C₂ hydrocarbon yield was 27.78%), which was higher than that of previous reported catalysts on no diluted gas condition, and showed a better prospect for industrialization of methane oxidative coupling. The research also showed that the new catalyst design method is highly efficient and universal.     

A hybrid approach to global optimization using a clustering algorithm in a genetic search framework

The concern of this work is global optimization using genetic algorithms (GAs). In this work we propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA to accomplish global optimization. This synergy minimizes redundant searches around local optima and enhances the capability of the GA to explore new areas in the search space. The proposed methodology demonstrates superior performance when compared with the simple GA on benchmark cases. We also report our solution of the optimal pumps configuration synthesis problem.     

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simulta-neous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus al ows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.     

Non-equilibrium sorption modeling for boron removal from geothermal water using sorption-microfiltration hybrid method

In this study, the boron removal performance of a hybrid system composed of ground ion exchange resin particles coupled with a microfiltration separation unit was investigated. A non-equilibrium sorption modeling approach was introduced so as to understand the contributions of mass transfer resistances on the effluent stream concentration profiles, as well as on the resin loading scheme of this sorption-microfiltration hybrid system. This modeling approach allowed us to suggest new system operations and/or scale-up processes of sorption-microfiltration hybrid systems. In this study, the highly porous crosslinked boron selective chelating resins Diaion CRB02 and Dowex XUS 43594.00 containing N-methyl-glucamine group were used. Geothermal water that has high levels of boron was fed into the stirred cell element of the microfiltration system. Kinetic behaviour of boron selective resins for boron removal from geothermal water by the microfiltration system was evaluated to investigate the effects of resin particle size, resin concentration, and permeate flow rate.     

基于差分进化粒子群混合优化算法的软测量建模

针对乙烯生产过程中,用传统方法难以直接完成对乙烯收率的在线测量的问题,提出了一种新型差分进化粒子群混合优化算法,建立了乙烯收率软测量建模。改进算法将优化过程分成两阶段,两分群分别采用粒子群算法和差分进化算法同时进行。迭代过程中引入进化速度因子进行算法局部收敛性判断,通过两个群体间的信息交流阻止算法陷入局部最优。对高维复杂函数寻优测试表明,算法的整体优化性能均强于基本粒子群算法和差分进化算法。应用结果表明,基于改进算法的软测量模型具有测量精度较高、泛化性能较好等优点。     

Emission control in palm oil mills using artificial neural network and genetic algorithm

The present study utilized a combination of artificial neural network (ANN) and genetic algorithms (GA) to optimize the release of emission from the palm oil mill. A model based on ANN is developed from the actual data taken from the palm oil mill. The predicted data agree well with the actual data taken. GA is then employed to find the optimal operating conditions so that the overlimit release of emission is reduced to the allowable limit.     

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.     

Modeling of a reaction network and its optimization by genetic algorithm

Continuous endeavors are going on in many research works to find out the strategy to mathematically model and optimize complex reaction networks in order to maximize the main product and at the same time keeping the reactor dimensions within some acceptable limits. The aim of this work is to provide with a strategy for efficient modeling and optimization of reaction networks for reaction controlled processes. Genetic algorithm (GA) has been used for optimizing complex search spaces with multiple optima. Formation of styrene monomer from the ethylbenzene dehydrogenation, with several by-products in a fixed bed reactor, is taken as an example for this study. Two activation energies are found to be the best in term of maximizing styrene productivity.