Comparison of configurations of a four-column simulated moving bed process by multi-objective optimization

Configurations of a four-column simulated moving bed chromatographic process are investigated by multi-objective optimization. Various existing column configurations are compared through a multi-objective optimization problem. Furthermore, an approach based on an SMB superstructure is applied to find novel configurations which have been found to outperform the standard SMB configuration. An efficient numerical optimization technique is applied to the mathematical model of the SMB process. It has been confirmed that although the optimal configuration highly depends on the purity requirement, the superstructure approach is able to find the most efficient configuration without exploring various existing configurations.     

Multiobjective Optimization of Industrial Petroleum Processing Units Using Genetic Algorithms

For many years most of refining processes were optimized using single objective approach, but practically such complex processes must be optimized with several objectives. Multiobjective optimization allows taking all of desired objectives directly and provide search of optimal solution with respect to all of them. Genetic algorithms proved themselves as a powerful and robust tool for multi-objective optimization. In this article, the review for a last decade of multi-objective optimization cases is provided. Most popular genetic algorithms and techniques are mentioned. From a practical point it is shown which objectives are usually chosen for optimization, what constraint and limitations might impose multi-objective optimization problem formulation. Different types of petroleum refining processes are considered such as catalytic and thermal.     

Artificial intelligence based modelling and multi-objective optimization of vinyl chloride monomer (VCM) plant to strike a balance between profit,energy utilization and environmental degradation

The current research focuses on the creation of a general technique that solves the key issue of any operational chemical plant, namely, how to strike a delicate balance between profit and environmental impact. As a case study, a commercial vinyl chloride monomer (VCM) production unit was used. This research produced a new modelling and optimization tool that commercial chemical plants can use to measure their environmental impact and strike a careful balance between profit and environmental damage. This paper demonstrates how to model commercial complex reactors using Aspen and ANN in an easy-to-use manner. The current study used a multi-objective hybrid ANN and genetic algorithm to find a delicate balance between profit and environmental damage. A case study of a commercial VCM manufacturing process demonstrates the efficacy of the proposed methodology. The suggested methodology creates optimal VCM reactor operating parameters, which can be used in commercial plants to increase profit. Furthermore, the suggested methodology creates a set of Pareto optimal solutions platform to acquire insight into the profit-environmental impact balance. These insights could be extremely beneficial to plant management in making educated decisions about plant operations.     

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.     

Standing wave optimization of SMB using a hybrid simulated annealing and genetic algorithm (SAGA)

In this paper we draw on two stochastic optimization techniques, Simulated Annealing and Genetic Algorithm (SAGA), to create a hybrid to determine the optimal design of nonlinear Simulated Moving Bed (SMB) systems. A mathematical programming model based on the Standing Wave Design (SWD) offers a significant advantage in optimizing SMB systems. SAGA builds upon the strength of SA and GA to optimize the 16 variables of the mixed-integer nonlinear programming model for single- and multi-objective optimizations. The SAGA procedure is shown to be robust with computational time in minutes for single-objective optimization and in a few hours for a multi-objective optimization, which is comprised of more than one hundred points.     

Optimization of preparative chromatographic separation of multiple rare earth elements

This work presents a method to optimize multi-product chromatographic systems with multiple objective functions. The system studied is a neodymium, samarium, europium, gadolinium mixture separated in an ion exchange chromatography step. A homogeneous Langmuir Mobile Phase Modified model is calibrated to fit the experiments, and then used to perform the optimization task. For the optimization a multi-objective Differential Evolution algorithm was used, with weighting based on relative value of the components to find optimal operation points along the Pareto front. The objectives of the Pareto front are weighted productivity and weighted yield with purity as an equality constraint. A prioritizing scheme based on relative values is applied for determining the pooling order. A simple rule of thumb for pooling strategy selection is presented. The multi-objective optimization gives a Pareto front which shows the rule of thumb, as a gap in one of the objective functions.     

Optimal control of a two-stage reactor system

Optimal operation policies were investigated for a batch reactor system with two different operation stages. At the end of the first nonisothermal stage one of the reactants was added. Since that moment the reactor was operated isothermally. In each stage behavior of the reactor was described by a set of differential equations. The maximum conversion problem was investigated subject to various operating constraints. Dynamic optimization based on the control vector parametrization was used to find the optimal control profile. Gradients of the resulting nonlinear programming problem were obtained by adjoint method based on the optimal control theory.     

Optimization of multi-classifiers for computational biology: application to gene finding and expression

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further.     

Multi-objective optimization of detergent pre-formulations using machine learning techniques

The right combination of surfactants and stabilizers in the detergent formulations plays a significant role in their cleaning performance. However, it becomes a complex optimization problem when the formulation is composed of multiple ingredients and the solution has to be optimized for competing performance metrics. In recent times, machine learning techniques have been used extensively to study such processes. In this research, a detergent pre-formulation has been designed using an aqueous solution of Tween-20, Ethanol and 1-Octanol. To determine the optimal values of the ingredients of the formulations, supervised machine learning models were developed and optimized for the Ross Miles Index 30 ml (RMI 30) and cleaning time (CT). A full factorial experimental design was performed and three regression models based on linear, 2FI and Quadratic designs were developed respectively for RMI30 and CT. ANOVA analysis of trained models reported an optimal p-value of 0.0018 for RMI 30 and less than 0.0001 for CT. The optimal values for RMI30 and CT obtained through regression models are 72.32 ml and 17.67 s. For multi-objective optimization, grey relational analysis was performed. Two pairs of optimal values corresponding to Rank 1 were recorded as 88.9 ml, 20 s (RMI30, CT); and 81.2 ml, 14 s (RMI30, CT) respectively. As a result, the optimal combination of Tween-20, Ethanol and 1-Octanol for maximizing the RMI30 and minimizing the CT are reported. The obtained optimal values were experimentally validated.     

用数值遗传算法计算配合物的稳定常数

提出了一种新的合局优化方法－数值遗传算法，该法可以处理连续变量参数的优化问题，能在很多局部较优中找到全局最优点，特别适合处理复杂的非线性问题，该法通过遗传操作不断改变个体和群体，使之逐渐适应环境，除新设计了交配和突变数值遗传操作外，本文还提出了记忆遗传操作，从而加快了运算的速度，采用这种算法，测定了新型化学发光材料的重要中间体－三氯水杨酸的酸常数及其与铜和铁的配合物的稳定常数。     

Optimization of startup and shutdown operation of simulated moving bed chromatographic processes

This paper presents new multistage optimal startup and shutdown strategies for simulated moving bed (SMB) chromatographic processes. The proposed concept allows to adjust transient operating conditions stage-wise, and provides capability to improve transient performance and to fulfill product quality specifications simultaneously. A specially tailored decomposition algorithm is developed to ensure computational tractability of the resulting dynamic optimization problems. By examining the transient operation of a literature separation example characterized by nonlinear competitive isotherm, the feasibility of the solution approach is demonstrated, and the performance of the conventional and multistage optimal transient regimes is evaluated systematically. The quantitative results clearly show that the optimal operating policies not only allow to significantly reduce both duration of the transient phase and desorbent consumption, but also enable on-spec production even during startup and shutdown periods. With the aid of the developed transient procedures, short-term separation campaigns with small batch sizes can be performed more flexibly and efficiently by SMB chromatography.     

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.     

Pareto optimization in computational protein design with multiple objectives

The optimization for function in computational design requires the treatment of, often competing, multiple objectives. Current algorithms reduce the problem to a single objective optimization problem, with the consequent loss of relevant solutions. We present a procedure, based on a variant of a Pareto algorithm, to optimize various competing objectives in protein design that allows reducing in several orders of magnitude the search of the solution space. Our methodology maintains the diversity of solutions and provides an iterative way to incorporate automatic design methods in the design of functional proteins. We have applied our systematic procedure to design enzymes optimized for both catalysis and stability. However, this methodology can be applied to any computational chemistry application requiring multi-objective combinatorial optimization techniques.     

基于人工蜂群优化的MR图像分割算法研究

针对传统图像阈值分割算法在MR图像分割时存在的易受采集图像灰度不均、医学图像易受噪声干扰,因而难以得到准确分割阈值的问题,本文将人工蜂群算法与二维OSTU阈值分割算法相结合,提出一种基于人工蜂群优化的MR图像分割算法。使用医学图像的离散度矩阵的迹作为人工蜂群优化的目标函数,得到二维OSTU的最佳分割阈值;根据得到的最佳阈值,对图像采用二维OSTU分割的方法进行分割。实验结果证明,对于医学MR图像,本文所提出的算法具有精度高和鲁棒性强的特点,能够得到精确的分割后图像。     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.     

Global optimization for parameter estimation of differential-algebraic systems

The estimation of parameters in semi-empirical models is essential in numerous areas of engineering and applied science. In many cases, these models are described by a set of ordinary-differential equations or by a set of differential-algebraic equations. Due to the presence of non-convexities of functions participating in these equations, current gradient-based optimization methods can guarantee only locally optimal solutions. This deficiency can have a marked impact on the operation of chemical processes from the economical, environmental and safety points of view and it thus motivates the development of global optimization algorithms. This paper presents a global optimization method which guarantees ɛ-convergence to the global solution. The approach consists in the transformation of the dynamic optimization problem into a nonlinear programming problem (NLP) using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are employed within the spatial branch-and-bound method and solved to global optimality. The proposed method was applied to two example problems dealing with parameter estimation from time series data.     

模式识别法用于反相液相色谱等度分离条件优化

以分离条件参数为特征变量构筑模式空间，串行指标ＨＣＲＦ为目标划分样本类别，通过主成分分析揭示模式空间的可视优化区，再由低维优化点回复到高维原始空洞，获得了最佳分离条件，此方法用于７种水溶性维生素分离，取得满意结果。