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In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution. 相似文献
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黄嘉珀 《化工自动化及仪表》2010,37(12):96-98,103
着重研究符合中国国情的石油化工多级实时数据库的数据结构与数据描述,提出多级实时数据库的更新和检索的操作原语,阐述了访问实时数据库的内存映射技术,提出构建多级石油化工实时信息系统的几个基本观点。呼吁建立实时数据库标准和开放标准,并期待对多级的MES系统的支持。 相似文献
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化工过程模拟及相关高新技术(Ⅳ)化工过程实时优化 总被引:8,自引:0,他引:8
先进控制与常规控制相比虽然带来了显著的效益,但是人们并不仅仅满足于这一点.因为先进控制解决的只是在给定的设定值下装置的平稳操作问题.至于各个设定值是否为最佳工艺条件,是否能够带来最大的经济效益,就不属于先进控制的管辖范围了. 相似文献
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基于多级流建模的间歇过程故障定位方法 总被引:1,自引:0,他引:1
针对间歇性供水系统故障定位问题的复杂性,提出了一种扩展多级流模型( MFM)的建模、故障警报分析和故障定位新方法.利用多级流模型和Petri网复合的方法,扩展了多级流模型以适应间歇过程的连续变化和离散变化,实现了分布式复杂系统的故障根源搜索和定位. 相似文献
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选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。 相似文献
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多组元多级绝热闪蒸过程的研究(Ⅲ)──多级闪蒸过程的工业应用 总被引:1,自引:0,他引:1
多组元多级绝热闪蒸过程的研究(Ⅲ)──多级闪蒸过程的工业应用杜英生,李鑫钢,赵汝文,郑陵,余国琮(天津大学化学工程研究所)在多组元多级闪蒸过程理论和实验研究的基础上,分析了多组元多级绝热闪蒸过程在原油脱气过程和原油常压蒸馏过程的工业应用。关键词多组元... 相似文献
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准确稳定的过程数据是选矿厂进行过程优化控制和决策管理的依据,今针对磨矿分级过程数据特点,建立了多层数据协调模型,包括总物料平衡层、粒度分布/品位层和不同粒度下的成分分析层(金属分布率层);针对模型维数较高的问题,引入粒子群优化(PSO)算法进行求解。根据不同的测量信息,可选择相应的层次进行协调,并采用从低层向高层逐层协调的方法,实现了部分非线性约束到线性约束的转化,提高了数据协调效率。将该多层模型和PSO算法用于某选矿厂磨矿分级过程实际生产数据的协调,结果表明协调后的数据更准确、更稳定,包含的信息更丰富完整。 相似文献
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Dinesh Krishnamoorthy Francis J. Doyle III 《American Institute of Chemical Engineers》2023,69(4):e17993
Conventional real-time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model-free RTO methods are an attractive alternative to circumvent the challenge of developing accurate models. Most model-free RTO methods are based on estimating the steady-state cost gradient with respect to the decision variables and driving the estimated gradient to zero using integral action. However, accurate gradient estimation requires clear time scale separation from the plant dynamics, such that the dynamic plant can be assumed to be a static map. For processes with long settling times, this can lead to prohibitively slow convergence to the optimum. To avoid the need to estimate the cost gradients from the measurement, this article uses Bayesian optimization, which is a zeroth order black-box optimization framework. In particular, this article uses a safe Bayesian optimization based on interior point methods to ensure that the setpoints computed by the model-free steady-state RTO layer are guaranteed to be feasible with high probability (i.e., the safety-critical constraints will not be violated at steady-state). The proposed method can thus be seen as a model-free variant of the conventional two-step steady-state RTO framework (with steady-state detection), which is demonstrated on a benchmark Williams-Otto reactor example. 相似文献
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An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter iden-tification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process. 相似文献
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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。 相似文献
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Natalie M. Isenberg Paul Akula John C. Eslick Debangsu Bhattacharyya David C. Miller Chrysanthos E. Gounaris 《American Institute of Chemical Engineers》2021,67(5):e17175
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. 相似文献
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针对氧化铝蒸发过程故障检测中标注者不切实际的假设和控制参数难以确定问题,提出改进的代价敏感主动学习方法。给出了代价敏感主动学习形式化描述和放松了标注者不切实际的假设。为了提高分类精度和减少标注代价,该方法结合粒子群优化和代价敏感主动学习。利用连续的粒子群优化代价敏感主动学习的控制参数,该参数用于最大化未标注样本的信息度和最小化标注代价。将所提出的方法应用于氧化铝蒸发过程故障检测,实验结果表明,该方法能正确地选择控制参数,有效地减少了误分类代价和标注代价,提高了故障检测率。 相似文献
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粒子群优化算法的发展及应用 总被引:1,自引:0,他引:1
讨论了粒子群优化算法的发展和应用。介绍了粒子群优化算法的基本原理和算法流程,并且与其他演化算法进行了比较,给出了一些经常用到的测试函数。针对粒子群优化算法在搜索后期存在的不足,介绍了改进的粒子群优化算法,重点介绍了在实际应用领域中用到的改进粒子群优化算法。 相似文献
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In order to deal with plant-model mismatch, iterative process optimization schemes use some adaptation strategy based on measurements. The modifier-adaptation approach consists in performing first-order corrections of the cost and constraint functions in the model-based optimization problem. The approach has the ability to converge to the true process optimum but the first-order corrections require the experimental estimation of the process gradients. Dual modifier-adaptation algorithms estimate the gradients by finite difference approximation based on the measurements obtained at the current and past operating points. In order to guarantee the accuracy of the estimated gradients a constraint is added to the optimization problem in order to position the next operating points with respect to the previous ones. This paper presents an alternative first-order correction, which provides an improved approximation of the cost and constraint functions, together with a new gradient error constraint for use in dual modifier adaptation. By means of the Williams–Otto reactor case study, the new dual modifier-adaptation approach is compared in simulation with a previous approach found in the literature showing faster convergence to a neighborhood of the plant optimum. 相似文献