化工过程设计的多目标不确定优化研究

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.     

种群分布式并行遗传算法解化工多目标优化问题

带精英策略的非支配排序遗传算法(NSGA-II)在与流程模拟软件Aspen Plus结合求解化工多目标优化问题方面耗时较高.为了解决这一问题,本文提出了一种种群分布式的并行遗传算法(populations distributed parallel genetic algorithm,PDPGA),将模拟计算任务分配给局域网的多台子节点计算机并行执行.以氯乙烯精制的多目标优化过程为研究对象,选取氯乙烯采出量最大化和系统总能耗最小化为两个目标,低沸塔和高沸塔的质量回流比、塔顶馏出率和塔压6个操作参数为优化变量.分别应用PDPGA和NSGA-II对上述过程进行优化求解,二者的种群规模均设为70,进化代数均设为70,PDPGA使用1主节点和2子节点共3台计算机.结果表明,与直接应用NSGA-II进行串行优化相比,PDPGA优化方法能充分利用闲置的计算机资源、有效提高解得质量和大幅降低优化计算的时间.     

改进的粒子群优化算法及其在多目标过程设计中的应用

介绍了粒子群优化算法的基本思想和步骤,对基于动态Pareto解集的多目标粒子群优化算法进行了分析,提出了随机更新个体粒子最优位置的策略,并把改进的粒子群优化算法用于实际的多目标过程设计中.对甲苯加氢脱烷基化过程进行了分析,利用此方法对以经济和环境为目标的加氢脱烷基化过程设计进行了计算.结果表明,此种方法可以用于实际的多目标过程设计中.     

分批补料发酵过程多目标优化的分布式强化学习策略

发酵过程优化问题通常包含有互相冲突的多重优化目标,另外反应本身具有诸多复杂性。提出一种基于Pareto的分布式Q学习多目标策略,用以求解赖氨酸分批补料发酵过程流加速率轨迹的Pareto最优解。该策略中,Q学习算法和Pareto排序法将结合来产生非支配解集,并使之逼近真实的Pareto前沿,利用奖赏机制来描述多重目标之间的关系,并同时使用多组含有随机初始值的agent共同作用改善搜索能力。将所提出的方法应用于赖氨酸分批补料发酵过程的优化中,并与粒子群优化进行了对比,验证策略的性能。     

Systems engineering approach for chemical process energy optimization

Process energy integration and continuous improvement of process technology are everlasting issues to ensure profitability of chemical productions. And both objectives become increasingly important due to long-term environmental effects of energy degradation, such as resource depletion, emissions and the release of “waste” heat. The key success factor for process improvement lies in combining up-to-date expertise from different areas in an overall approach. One such approach is the systems engineering concept. It helps to structure and to organize problem-solving process. We strongly believe that the increasing complexity of large and interlinked chemical production system and the tightening of global economic pressure force us to use more than ever systematic analysis and design methods to guarantee optimality throughout the entire product life. First we present a brief introduction to systems engineering in general. Then, in the main part of the paper, we give examples for optimizing the use of energy in chemical plants in order to illustrate advantages of the systems engineering concept. The examples range from improving the performance of individual pieces of equipment over changes in the process structure up to optimizing process clusters.     

基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

以C8芳烃混合物的吸附分离过程作为研究对象, 应用多目标教学优化算法(multi-objective teaching-learning-based optimization algorithm, MOTLBO)对模拟移动床多目标优化问题进行求解。采用TMB方法, 建立了模拟移动床模型, 并对两个典型的模拟移动床多目标操作优化问题进行了优化设计。通过与NSGA-Ⅱ算法的比较, 证明了多目标教学优化算法在求解模拟移动床多目标优化问题上的有效性和优势。此外, 还分析了抽出液流量、抽余液流量以及步进时间等对多目标优化非劣解的影响, 优化结果为模拟移动床分离过程的工艺设计和操作提供了依据。     

碳五隔壁反应精馏预处理工艺模拟及多目标优化

针对碳五综合利用预处理工段能耗较高的现状,在反应精馏(RD)预处理流程基础上提出了隔壁反应精馏(RDWC)预处理流程。首先,利用化工模拟软件Aspen Plus搭建RDWC四塔等效严格模型,并对其进行自由度和单变量分析。在此基础上,以响应面Box-Behnken Design(BBD)方法作为模型拟合工具,拟合出目标变量与决策变量之间的函数关系,并对拟合结果进行方差分析(ANOVA)。最后采用基于分解的多目标进化算法(MOEA/D)对RDWC预处理流程进行多目标优化,得到一系列Pareto最优解,选出其中年总成本(TAC)最小的一组解与RD预处理流程进行对比。结果显示：与RD预处理流程相比,RDWC预处理流程可以节约TAC 12.8%,节省再沸器负荷27.8%,选择性也有所提高。     

基于多指标综合评分法的开炼机炼胶工艺参数多目标优化

本文采用青岛科技大学自行研制的XK-160E型开炼机智能炼胶实验平台对开炼机炼胶工艺进行正交实验,并运用基于模糊映射的质量指标加权综合评分法对混炼胶的门尼粘度、炭黑分散度、300%定伸应力,拉伸强度,撕裂强度进行综合评分。通过极差分析确定辊距、辊速、速比、辊筒温度、混炼时间五个工艺参数对综合评分值的影响大小,并分析得出最优的开炼机炼胶工艺参数组合方案。     

混合多目标骨干粒子群优化算法在污水处理过程优化控制中的应用

通过对污水生化处理过程的分析,选取能耗和罚款最低为优化目标,建立污水生化处理过程多目标优化控制模型。为了提高Pareto最优解集的收敛性和多样性,提出一种基于Pareto支配和分解的混合多目标骨干粒子群优化算法（HBBMOPSO）。该方法采用带自适应惩罚因子的分解方法选取个体引导者,采用Pareto支配和拥挤距离法维护外部档案和选取全局引导者。此外,采用精英学习策略增强粒子跳出局部Pareto前沿的能力。最后,将HBBMOPSO与自组织模糊神经网络预测模型和自组织控制器相结合,实现污水生化处理过程溶解氧和硝态氮设定值的动态寻优、智能决策和底层跟踪控制。利用国际基准仿真平台BSM1进行实验验证,结果表明所提HBBMOPSO方法在保证出水水质参数达标的前提下,能够有效降低污水处理过程的能耗。     

A generalized cutting-set approach for nonlinear robust optimization in process systems engineering

We propose a novel computational framework for the robust optimization of highly nonlinear, non-convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting-set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second-stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine-based CO₂ capture. We thus verify that the generalization of the robust cutting-set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.     

化学工程中多目标遗传算法的应用

多目标遗传算法(MOGA)是在遗传算法的基础上发展起来的,它可以进行多个目标之间直接权衡.介绍了多目标遗传算法的概念及发展历程,重点介绍了非劣排序遗传算法(NSGA)和NSGA-Ⅱ,以及多目标遗传算法和过程模拟器结合在化学工程中的应用情况.     

基于动态代谢通量分析的发酵过程多目标优化

通过动态代谢通量分析方法建立发酵过程模型,提出了一种基于微观代谢信息的发酵过程多目标优化策略,该策略基于所建微观模型,根据动态特性将发酵过程分为菌体生长和产物合成两个阶段,进行特征分析并从微观通量层面分别设计优化目标与约束条件,采用多目标粒子群算法求得最优解。该方法用于青霉素发酵过程底物流加速率和pH的操作轨迹优化,仿真实验结果表明,采用基于微观通量的多目标优化策略能够提高产物终端浓度,表明优化策略的有效性。     

Genetic-based multi-objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

The purpose of this research is to find the optimal operating point in the production process of the cumene. Therefore, the production process was optimized through statistical and genetic algorithm-based methods. The performance of an alkylation reactor was optimized through maximizing the yield of cumene production. Response surface methodology (RSM) with design type of central composite was applied for design of experiment, modelling, and optimizing the process. The analysis of variance (ANOVA) was performed for finding the important operative parameters as well as their effects. The effects of three parameters including temperature, reactor length, and pressure on the alkylation process were investigated. Further, two types of feed-forward neural network were applied to model the alkylation reactor. To develop the neural network model, leave-one-out method was used. The best prediction performance belonged to a fitting network with 2 and 8 neurons in the hidden layer, respectively. This model was used for optimizing the performance of the alkylation reactor. The statistical and artificial intelligence systems were capable of prediction of cumene production yield in different conditions with R² of 0.9098 and 0.9986, respectively. Genetic algorithm-based optimization was performed by the developed neural network model. The maximum accessible value of cumene production yield was 0.7771, which can be achieved when the temperature, length of reactor, and column pressure are 160°C, 2 m, and 4000 kPa, respectively. By finding the optimal operating point in the cumene production process, capital cost, energy consumption, and other operating costs can be significantly reduced.     

Inverse problems of biological systems using multi-objective optimization

Mathematical modeling for dynamic biological systems is a central theme in systems biology. There are still many challenges in using time-course data to obtain an inverse problem of nonlinear dynamic biological systems. In this study, a multi-objective optimization technique is introduced to determine kinetic parameter values of biochemical reaction systems. The multi-objective parameter estimation was converted into the minimax problem through the satisfying trade-off method. The aspiration value was assigned as the minimum solution to the corresponding single objective estimation. The aim of this trade-off estimation was to obtain a compromised result by simultaneously minimizing both concentration and slope error criteria. Hybrid differential evolution was applied to solve the minimax problem and to yield a global estimation.     

Toward agent-based process systems engineering: proposed framework and application to non-convex optimization

Agent-based computer systems are surprisingly effective at solving complex problems. Built by combining autonomous computer routines, or agents, with low-bandwidth communication capabilities, these systems typically perform significantly better than the individual routines operating alone. One source of this improvement lies in the cooperative collaboration among the individual agents that compose the system. This work proposes a modular framework for implementing agent-based systems for engineering design. Using a variety of different algorithmic agents, the key ideas are highlighted by identifying multiple identical global optima for a non-convex optimization problem with numerous local minima. The results show that inter- and intra-agent collaboration have a significant impact on system performance. Further, the system can be parallelized with little or no penalty. By observing and analyzing the interactions among individual agents, we gain insights that will aid in the development and management of a conceptual design system for truly hard and large problems.     

一种基于梯度信息的多目标优化算法

传统的多目标进化算法多是基于Pareto最优概念的类随机搜索算法,求解速度较慢,特别是针对动态多目标优化问题。就此提出了一种新的基于梯度信息的多目标寻优算法（hybrid optimization algorithm based on single and multi-objective gradient information,HSMGOA）,该算法首先利用种群中每个个体对各目标的负梯度方向,以有效保证种群个体能沿单个目标函数值减小的方向加快搜索;同时为避免由于多目标问题之间的冲突性而导致其他目标函数的显著增大,将多个目标的梯度信息方向整合为一个方向进行协同搜索;并且还提出了一种新的选择置点法,以加快算法初始寻优速度并提供优良的初始种群。通过对ZDT系列测试函数的仿真可以看出,HSMGOA在较少的运行次数下,其性能远远优于NSGA2算法。最后将HSMGOA与NSGA2混合以解决补料分批生化反应过程的动态多目标优化问题,并将取得的Pareto最优解集与NSGA2、MOPSO比较可知,该混合算法在解决该化工问题时表现出了更好的性能。     

Successive approximate model based multi-objective optimization for an industrial straight grate iron ore induration process using evolutionary algorithm

Multi-objective optimization of any complex industrial process using first principle computationally expensive models often demands a substantially higher computation time for evolutionary algorithms making it less amenable for real time implementation. A combination of the above-mentioned first principle model and approximate models based on artificial neural network (ANN) successively learnt in due course of optimization using the data obtained from first principle models can be intelligently used for function evaluation and thereby reduce the aforementioned computational burden to a large extent. In this work, a multi-objective optimization task (simultaneous maximization of throughput and Tumble index) of an industrial iron ore induration process has been studied to improve the operation of the process using the above-mentioned metamodeling approach. Different pressure and temperature values at different points of the furnace bed, grate speed and bed height have been used as decision variables whereas the bounds on cold compression strength, abrasion index, maximum pellet temperature and burn-through point temperature have been treated as constraints. A popular evolutionary multi-objective algorithm, NSGA II, amalgamated with the first principle model of the induration process and its successively improving approximation model based on ANN, has been adopted to carry out the task. The optimization results show that as compared to the PO solutions obtained using only the first principle model, (i) similar or better quality PO solutions can be achieved by this metamodeling procedure with a close to 50% savings in function evaluation and thereby computation time and (ii) by keeping the total number of function evaluations same, better quality PO solutions can be obtained.     

GAMS supported optimization and predictability study of a multi-objective adsorption process with conflicting regions of optimal operating conditions

In process systems engineering, it is critical to design an effective and optimized process in a short period with minimum experimental trials. However, improvement of some process variables may deteriorate some other criteria due to conflicting regions of factor interests for optimal solution in multi-objective optimization (MOO) processes. Here, the global optimization of an adsorption case study with conflicting optimal solutions based on multi-objective Response Surface Methodology (RSM) design is facilitated with the implementation of BARON solver based on General Algebraic Modeling System (GAMS) with identical factor variables, levels, and model equations. RSM suggested fifteen different optimum settings of which the validation is quite expensive and onerous, whereas GAMS suggested a single optimum setting which makes it more economically viable especially for large scale systems. In addition, the GAMS-based optimization provided more accurate and reliable results when experimentally validated as compared to the RSM-based solution.     

Energy,exergy, economic and environmental comprehensive analysis and multi-objective optimization of a sustainable zero liquid discharge integrated process for fixed-bed coal gasification wastewater

For a long time, China’s regional water resource imbalance has restricted the development of coal chemical industry, and it is imperative to achieve zero liquid discharge(ZLD). Therefore, the game relationship between technical indicators, costs and emissions in ZLD process of fixed-bed coal gasification wastewater treatment process should be explored in detail. According to the accurate model, the simulation for ZLD of fixed-bed coal gasification wastewater treatment process is established, and...     

Stochastic modeling and multi-objective optimization for the APECS system

The Advanced Process Engineering Co-Simulator (APECS), developed at the U.S. Department of Energy's (DOE) National Energy Technology Laboratory, is an integrated software suite that enables the process and energy industries to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. The APECS system uses the process-industry standard CAPE-OPEN (CO) interfaces to combine equipment models and commercial process simulation software with powerful analysis and virtual engineering tools. The focus of this paper is the CO-compliant stochastic modeling and multi-objective optimization capabilities provided in the APECS system for process optimization under uncertainty and multiple and sometimes conflicting objectives. The usefulness of these advanced analysis capabilities is illustrated using a simulation and multi-objective optimization of an advanced coal-fired, gasification-based, zero-emissions electricity and hydrogen generation facility with carbon capture.