Gas separation in an internally staged permeator: experimental results and theoretical predictions

An internally staged membrane permeator was assembled using a silicone rubber membrane in the laboratory. Separation of CO₂/N₂ using this permeator was studied experimentally and theoretically. It has been shown experimentally that simultaneous use of two membranes of the same type in a permeator improves the degree of separation to the level which may not be possible to achieve in a single stage permeator. The mathematical model based on the perfect mixing case predicts the experimental results very well. The simulation results have shown that, in order to maximise the degree of separation, the values of both stage cuts and the pressure ratio across each membrane have to be properly selected to obtain the optimal operating conditions. At these conditions, the membrane area ratio between first and second stage decreases with increasing overall stage cut.     

Optimum design of reverse osmosis system under different feed concentration and product specification

The design of various multistage RO systems under different feed concentration and product specification is presented in this work. An optimization method using the process synthesis approach to design an RO system has been developed. First, a simplified superstructure that contains all the feasible design in present desalination process has been presented. It offers extensive flexibility towards optimizing various types of RO system and thus may be used for the selection of the optimal structural and operating schemes. A pressure vessel model that takes into account the pressure drop and concentration changes in the membrane channel has also been given to simulate multi-element performance in the pressure vessel. Then the cost equation relating the capital and operating cost to the design variables, as well as the structural variables of the designed system have been introduced in the objective function. Finally the optimum design problem can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which minimizes the total annualized cost. The solution to the problem includes optimal arrangement of the RO modules, pumps, energy recovery devices, the optimal operating conditions, and the optimal selection of types and number of membrane elements. The effectiveness of this design methodology has been demonstrated by solving several seawater desalination cases. Some of the trends of the optimum RO system design have been presented.     

Optimization methodology applied to feed‐forward artificial neural network parameters

This article recommends a methodology for developing a neural network with great chances to be an optimal one. The method is based on trial and error in determining the internal parameters of the network considered as having a significant influence over its performance: the number of hidden layers, activation function, number of neurons in the hidden layers, training epochs, learning rate, and momentum term. This optimization methodology is presented in two separate sections: first of them contains a series of practical considerations recommended for neural network modeling, and the second is represented by the proposed optimization algorithm, formulated in six steps and based on the practical statements. Two case studies are chosen to exemplify the use of the algorithm for finding the near optimal neural network: the dependence of the reduced and intrinsic viscosities of the siloxane‐organic copolymers of the solution concentration, temperature, and copolymer type, differing by the siloxane sequence length. The two siloxane‐organic polyazomethines resulted by the reaction of a fully aromatic bisazomethine diol with α,ω‐bis(chloromethyl)oligodimethylsiloxanes. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

Optimal design of spiral-wound membrane networks for gas separations

An optimal design strategy for spiral-wound membrane networks based on an approximate permeator model and a mixed-integer nonlinear programming (MINLP) solution strategy is proposed. A general permeator system superstructure is used to embed a very large number of possible network configurations. The superstructure allows the development of a MINLP design strategy which simultaneously optimizes the permeator configuration and operating conditions to minimize an objective function which approximates the total annual process cost. Case studies for the separation of CO₂/CH₄ mixtures in natural gas treatment and enhanced oil recovery are presented. Permeator configurations are derived for different number of separation stages for both continuous and discrete membrane areas. The proposed approach provides an efficient methodology for the preliminary design of multi-stage membrane separation systems for binary gas mixtures.     

Molecular Property Optimizations with Boundary Conditions through the Best First Search Scheme

There is an increasing interest in applying quantum chemistry to rationally design novel compounds with some desired characteristics. Furthermore, many applications require more than one property to be optimal. In this Concept, several inverse design strategies, based on the discrete best first search scheme, are introduced that allow for the simultaneous optimization of multiple properties or the optimization of the most vital target property with constraints for secondary properties. A detailed assessment of the different optimization techniques is carried out, and special attention is paid to improve the cost efficacy and performance by tuning the process parameters. Our suggested protocol allows for a more successful optimization routine when additional boundary conditions are desired.     

A computational model for enhancing recombinant Penicillin G Acylase production from Escherichia coli DH5α

An attempt was made to develop a computational model based on artificial neural network and ant colony optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase (PGA) enzyme from Escherichia coli DH5α strain harboring the plasmid pPROPAC. As a first step, an artificial neural network (ANN) model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium. Design of experiments employing central composite design technique was used to obtain the training samples. In the second step, ant colony optimization technique for continuous domain was employed to maximize the PGA activity by finding the optimal inputs for the developed ANN model. Further, the effect of a combination of ant colony optimization for continuous domain with a preferential local search strategy was studied to analyze the performance. For a comparative study, the training samples were fed into the response surface methodology optimization software to maximize the PGA production. The obtained PGA activity (56.94 U/mL) by the proposed approach was found to be higher than that of the obtained value (45.60 U/mL) with the response surface methodology. The optimum solution obtained computationally was experimentally verified. The observed PGA activity (55.60 U/mL) exhibited a close agreement with the model predictions.     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Short-cut structural design of reverse osmosis desalination plants

A design method for reverse osmosis desalination plants has been developed. It incorporates rigorous mathematical models for the prediction of the performance of various process units (reverse osmosis modules, pumps, energy recovery turbines) employed in the flowsheet and taken into account the network structure using an appropriate superstructure, which represents various reverse osmosis networks. Cost equations relating the capital and operating cost to the design variables, as well as the structural variables of the designed network have been introduced in the objective function. The total cost of the plant is minimized in order to determine the optimal operating and structural variables. The model is accurate enough to describe the process and yet simple enough to be used for design purposes. During the simulation and optimization studies, several structures for multistage reverse osmosis systems have been found. Results concerning the economics of the process are presented. Optimal results have also been used for the derivation of design curves concerning the effect of quality and quantity of produced water to the total annualized cost of the plant for various types of membrane modules.     

Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results.     

Dramatic destabilization of transmembrane helix interactions by features of natural membrane environments

Membrane proteins have evolved to fold and function in a lipid bilayer, so it is generally assumed that their stability should be optimized in a natural membrane environment. Yet optimal stability is not always in accord with optimization of function, so evolutionary pressure, occurring in a complex membrane environment, may favor marginal stability. Here, we find that the transmembrane helix dimer, glycophorin A (GpATM), is actually much less stable in the heterogeneous environment of a natural membrane than it is in model membranes and even common detergents. The primary destabilizing factors are electrostatic interactions between charged lipids and charged GpATM side chains, and nonspecific competition from other membrane proteins. These effects overwhelm stabilizing contributions from lateral packing pressure and excluded volume. Our work illustrates how evolution can employ membrane composition to modulate protein stability.     

Drug–target interaction prediction by integrating multiview network data

Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results.In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.     

基于遗传算法的手性毛细管电泳分离中多指标同时优化

将功效函数法结合遗传算法用于碱性药物肾上腺素的手性毛细管电泳的多指标同时优化.以均匀设计安排了4因素(电解质浓度、pH值、手性试剂浓度和分离电压)6水平实验,建立了峰分离度、迁移时间和灵敏度与实验因素间的多元线性回归方程.为获得毛细管电泳评价指标的同时优化,采用Derringer功效函数法,建立了总功效函数与实验因素的定量关系.采用实数编码的遗传算法搜寻总功效函数的最大值,获得了多个优化分离条件,在这些分离条件下得到的总功效函数与均匀试验设计中最佳条件相比,平均提高了10.4%,获得了三指标同时优化的结果.     

Application of artificial neural networks in multifactor optimization of an FIA system for the determination of aluminium

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) is proposed in the optimization of a new flow injection system for the spectrophotometric determination of Al(III) with Arsenazo DBM, which has for the first time been used as chromogenic reagent in the quantitative analysis of aluminium. An orthogonal design is utilized to design the experimental protocol, in which three variables are varied simultaneously. Feedforward-type neural networks with faster back propagation (BP) algorithm are applied to model the system, and then optimization of the experimental conditions is carried out in the neural network with 3-7-1 structure, which have been confirmed to be able to provide the maximum performance. In contrast to traditional methods, the use of this methodology has advantages in terms of a reduction in analysis time and an improvement in the ability of optimization. The method has been applied to the determination of Al(III) in steel samples and provided satisfactory results. Received: 26 May 1999 / Revised: 29 July 1999 / Accepted: 17 August 1999     

Optimal design of affinity membrane chromatographic columns

A method for the optimal affinity membrane column design, based in the solution of the Thomas kinetic model for frontal analysis in membrane column adsorption, is presented. The method permits to choose suitable membrane operating conditions, column dimensions and processing time, to maximize the throughput when an operating capacity restriction in the range of 80–95% of the column capacity is used. Two basic design charts were obtained by computer simulation, for residence and processing time calculation, respectively. These charts can be used and manipulated in a wide range of operational conditions, provided that four design specifications related to column axial and radial Peclet numbers, length and pressure drop, are fulfilled. The application of the method was illustrated using experimental data and a simple analytical procedure. The implications of the method and results on the design and optimization of affinity membrane chromatographic columns are discussed.     

Optimization diagram for membrane separations

A novel methodology has been developed which enables optimization of membrane separations. In multi-component separation processes, sieving coefficients for the individual solutes, defined as the ratio of the filtrate and feed concentrations, tend to reach optimum values under different process conditions. It is not possible to determine a priori the pair of sieving coefficients which will give the best combination of product yield and purification for a given application. A purification factor-yield diagram for such an optimization has been developed which utilizes a family of curves representing two dimensionless numbers plotted on yield versus purification-factor coordinates. Analysis can be performed with knowledge of only three experimental variables: the filtrate flux and the two solute sieving coefficients. Complete optimization of membrane processes can be achieved by combining these variables with membrane area, process time, and retentate-volume constraints. The methodology should be applicable to ultrafiltration, microfiltration, and high-performance tangential flow (selective) filtration processes.     

Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network

In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem. A data-driven identification method based on MLPNN and optimal design of experiments is described in detail. The nonlinear model of an extractive ethanol process, represented by a MLPNN, is optimized using real-coded and binary-coded genetic algorithms to determine the optimal operational conditions. In order to check the validity of the computational modeling, the results were compared with the optimization of a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature.     

Chemofiltration of eriochrome black T-zinc complex and determination by X-ray spectrometry

In the present work, a new methodology for zinc preconcentration and separation and its determination by X-ray spectrometry is proposed. The methodology is based on the formation of a complex using Eriochrome black T (1-naphtalensulfonic acid, 3-hidroxy-4-((1-hidroxy-2-naphtalenyl)azo)-7-nitro, monosodium salt), EBT, as complexing agent. Next, the complex is retained on a polyamide membrane by a chemofiltration process. The chemofiltrated material on the membrane constitutes a thin film, which minimizes or eliminates the interelemental effects when the instrumental detection is realized by X-ray fluorescence spectrometry. As the preconcentration method presented is based on the formation of a complex which will interact with the membrane during the chemofiltration, it was necessary first to establish the optimal conditions for the complex formation of Zn-EBT in aqueous medium and then to explore the optimal conditions for obtaining the thin film. The pH dependence of the metal ion chemofiltration on the membrane and other variables, such as contact time and retention capacity of the membrane, were also optimized. Chemofiltration step provided an enrichment factor of 30,000, improving considerably the instrumental detection limit, allowing the determination of trace zinc in environmental samples.     

Optimal control determination of MMA polymerization in non-isothermal batch reactor using bifunctional initiator

In this work, we determine the optimal control for free-radical methyl methacrylate polymerization using a bifunctional initiator in a non-isothermal batch reactor. A detailed unsteady-state model of the process is employed. Four different optimal control objectives are realized, each of which optimizes a given variable simultaneously with the specification of another. The first two objectives involve the maximization of monomer conversion in a specified operation time, and the minimization of operation time for a specified, final monomer conversion. The last two objectives involve the maximization of monomer conversion for specified, final number and weight average polymer molecular weights. The temperature of heat-exchange fluid inside reactor jacket is considered as a control function of an independent variable. To meet the specification of an optimization variable other than time, the differential model of batch process is derived in the range of specified variable. Equations are provided for Jacobian evaluations to help in the accurate solution of process model. A genetic algorithms-based optimal control method is applied to realize the four optimal control objectives. The results show that optimal control can significantly enhance the performance of the batch polymerization process.