基于减法聚类的焦炉集气管压力操作模式提取及迁移重构

基于集气管压力控制特点和生产过程中产生的大量历史数据和以往操作经验的基础上，提出一种基于减法聚类的焦炉集气管压力控制的操作模式提取及迁移重构方法。基于减法聚类算法进行操作模式的提取，最终形成操作模式优化数据库，对压力设定值进行优化，采用基于模型迁移思想的模式重构策略实现操作模式的修正。工业应用试验验证了所提方法的有效性。     

基于同步优化的代价敏感支持向量机优良类操作模式识别

针对氧化铝蒸发过程操作模式集中类别不平衡和噪声特征问题,提出基于同步优化的代价敏感支持向量机操作模式识别方法。对氧化铝蒸发过程机理进行分析,该过程的输入条件、操作参数和状态参数被选为原始操作模式,利用离散的粒子群算法优化操作模式的特征集,选择最优特征子集作为最终的操作模式;同时利用连续的粒子群算法优化代价敏感支持向量机的核参数和误分类代价参数,自动搜索和确定最优的核参数和误分类代价参数。工业应用结果表明,与粒子群优化操作模式特征子集或粒子群优化核参数和误分类代价参数相比,所提出的方法优良类操作模式识别高,误分类代价低。     

基于装置级优化控制与厂级调度优化集成的过程模型方法

随着全球化市场竞争日趋激烈,炼油生产过程的系统工程方法引起学术界和工业界的普遍关注。由于炼油生产过程的复杂性,现有方法远未有效解决过程模型准确描述问题,直接导致系统工程应用效果欠佳;过程模型的准确描述成为阻碍过程系统工程成功应用的关键。从困扰过程模型准确描述的症结出发,提出了一种基于装置级优化控制与厂级调度优化集成策略的模型描述方法,意在打破当前装置级底层控制系统和上层过程系统工程应用相互孤立情况下带来的系统间互为排斥、相互抑制的困局。在炼油生产过程装置分类的基础上,基于装置级优化控制的操作运行大数据所蕴含的多个最优操作模态信息,提出分装置类别和分操作模式的多模型描述方法,解决变化原料及操作工况的影响;基于该模型描述机制,将能够为智能炼油提供基础。     

基于粗糙集的智能可视化优化方法用于加氢裂化反应操作优化

加氢裂化反应的历史生产数据包含了丰富的操作规律,而目前没有被充分挖掘和利用。针对该问题,本文 提出了基于粗糙集的智能可视化优化方法,并应用该方法对加氢裂化反应的操作变量进行了优化。采用自适应离 散化的方法对加氢裂化反应的生产操作数据进行离散化,得到离散化的数据集。针对数据集属性约简的N-P 难题, 提出了基于列队竞争算法的属性约简计算方法。通过对加氢裂化反应的数据集约简计算,使预设的12 个操作变量 约简到了8 个,除去了4 个冗余变量,有效降低了优化搜索空间。在此基础上,应用智能可视化优化方法将约简 后的加氢裂化反应数据降维映射到平面,并生成航煤收率的等值线,据此,直观地确定出了航煤收率的优化区域 和优化操作点。结果说明,选出的优化工况点与原工况航煤收率33.98%相比,能使航煤收率提高到37.58%.     

基于装置级优化控制与厂级调度优化集成的过程模型方法

随着全球化市场竞争日趋激烈,炼油生产过程的系统工程方法引起学术界和工业界的普遍关注。由于炼油生产过程的复杂性,现有方法远未有效解决过程模型准确描述问题,直接导致系统工程应用效果欠佳;过程模型的准确描述成为阻碍过程系统工程成功应用的关键。从困扰过程模型准确描述的症结出发,提出了一种基于装置级优化控制与厂级调度优化集成策略的模型描述方法,意在打破当前装置级底层控制系统和上层过程系统工程应用相互孤立情况下带来的系统间互为排斥、相互抑制的困局。在炼油生产过程装置分类的基础上,基于装置级优化控制的操作运行大数据所蕴含的多个最优操作模态信息,提出分装置类别和分操作模式的多模型描述方法,解决变化原料及操作工况的影响;基于该模型描述机制,将能够为智能炼油提供基础。     

基于多目标优化的两段提升管重油催化裂解自优化控制

针对两段提升管重油催化裂解过程经济运行要求和工艺特点,从多目标优化角度出发,提出一种自优化控制方法。首先,基于过程稳态模型,考虑操作约束条件,构造同时最大化丙烯产量和最小化干气产量的多目标操作优化问题,并采用标准化法向约束方法求解获得完整、均匀分布的Pareto最优解;然后,根据多目标优化结果所揭示的最优操作条件与积极约束之间的关系,提出了一种基于串级控制的自优化控制策略。仿真结果表明,与传统的提升管出口温度设定值跟踪控制相比,本文方法在干扰作用下能够及时调整操作条件,降低干扰对过程优化运行的不利影响。     

多周期操作工况下无分流换热网络的弹性设计

提出了基于多周期无分流换热网络的超结构形式及弹性设计方法——同步优化方法。首先建立网络多周期操作工况下的超结构模型;然后用遗传算法对上述模型进行求解。该方法能自动地合成多周期操作工况的换热网络最优流程结构。最后,通过换热网络的最优合成设计实例,说明本方法的有效性和应用前景。     

多周期操作工况下换热网络的弹性设计

提出了基于专家系统的多周期换热网络的超结构形式及弹性设计方法。首先通过专家系统确定各周期操作工况下物流的分流及匹配禁止情况,在此基础上建立网络多周期操作工况下的超结构模型;然后用遗传算法对上述模型进行求解;最后用分解协调法对网络结构和匹配单元同时进行优化。该方法不仅能自动地合成多周期操作工况的换热网络最优流程结构,而且能同时满足网络在各操作工况下的各种工艺限制条件。最后,通过某个换热网络的最优合成设计,说明本方法的有效性和应用前景。     

多周期操作工况下热换网络的弹性设计

提出了基于专家系统的多周期换热网络的超结构形式及弹性设计方法。首先通过专家系统确定周期操作工况下物流的分流及匹配禁止情况。在此基础上建立网络多周期操作工况下的超结构模型;然后用遗传算法对上述模型进行求解。最后用分解协调法和网络结构和匹配单元同时进行优化。该方法不仅自动地合成多周期操作工况的换热网络最优流程结构,而且能同时满足网络在各操作工况下的各种工艺限制条件,最后,通过某个换热网络的最优合成设计     

基于多目标优化的两段提升管重油催化裂解自优化控制

针对两段提升管重油催化裂解过程经济运行要求和工艺特点,从多目标优化角度出发,提出一种自优化控制方法。首先,基于过程稳态模型,考虑操作约束条件,构造同时最大化丙烯产量和最小化干气产量的多目标操作优化问题,并采用标准化法向约束方法求解获得完整、均匀分布的Pareto最优解;然后,根据多目标优化结果所揭示的最优操作条件与积极约束之间的关系,提出了一种基于串级控制的自优化控制策略。仿真结果表明,与传统的提升管出口温度设定值跟踪控制相比,本文方法在干扰作用下能够及时调整操作条件,降低干扰对过程优化运行的不利影响。     

Surrogate-model-based,particle swarm optimization,and genetic algorithm techniques applied to the multiobjective operational problem of the fluid catalytic cracking process

Abstract

This article provides a concise multiobjective optimization methodology for an industrial fluid catalytic cracking unit (FCCU) considering stochastic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), based on surrogates or meta-models in order to approximate the objective function. A FCCU was considered and simulated in an AspenONE process simulator. In addition the article examines the claim that PSO has the same effectiveness (finding the optimal global solution) as GA, but with significantly better computational efficiency (fewer function evaluations). The optimization results obtained with the PSO technique, based on the evaluation of less functions and adjustment of less parameters, showed a 3% increase in yield of naphtha as compared to results obtained with the GA technique. Finally, the results of the optimization obtained with the stochastic optimization techniques were compared and analyzed with a deterministic one. The performance targets of the multiobjective operational optimization supported the FCCU design and production planning to ensure refinery profitability and a regulatory environment.     

Operational optimization of industrial steam systems under uncertainty using data-Driven adaptive robust optimization

This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16500 2019     

基于PSO-SVM的催化裂化装置故障诊断研究

提出基于粒子群优化算法和支持向量机的催化裂化装置反应再生子系统故障诊断方法。利用粒子群优化算法的全局搜索特性,实现支持向量机的参数优化算法。根据支持向量机算法构建了催化裂化装置反应再生子系统故障诊断模型。结果显示,该诊断方法准确率高,具有较高的使用价值。     

基于子系统关联变量轮换的大系统在线协调优化

化工过程复杂大系统的在线优化过程中,存在流程前后的关联导致优化时间过长或得不到优化解的问题,需要按子系统优化并协调的优化方法来解决,而协调优化方法存在关联变量寻优方向不一致的问题。提出了一种基于子系统间关联变量轮换思想的分解协调优化方法,对大系统分解得到的子系统以轮换的方式进行优化,子系统中包含的多个优化问题分别在固定关联变量优化独立变量和固定独立变量优化关联变量的条件下求解,此轮换过程迭代进行,直至满足优化终止条件。将提出的方法应用在催化裂化装置仿真实例中与整体优化方法的结果作比较,证明了方法的有效性。最后,将基于关联变量轮换的协调优化方法应用在化工过程的在线优化中,结果表明本方法在在线优化应用中是可行的。     

Pattern‐driven color pattern recognition for printed fabric motif design

Pattern‐driven design method is an important data‐driven design method for printed fabric motif design in textiles and clothing industry. We introduce a novel framework for automatic design of color patterns in real‐world fabric motif images. The novelty of our work is to formulate the recognition of an underlying color pattern element as a spatial, multi‐target tracking, classification, segmentation and similarity association process using a new and efficient color feature encoding method. The proposed design method is based on pattern‐driven color pattern recognition and indexing from the element image database. A series of color pattern recognition algorithms are used for color and pattern feature extraction. The local statistical corner features and Markov random field model are used for motif unit tiling detection and conversion. The color feature encoding problem is modeled in a gray‐scale color difference optimization problem, which can be solved quickly by existing algorithms. Color pattern feature matching, segmentation and indexing techniques are then used to locate and replace the elements in the motif unit image with similar elements in the database. Experiments show that the approach proposed in this study is effective for color pattern recognition and printed fabric motif design.     

催化裂化装置主分馏塔模拟方法研究

催化裂化装置主分馏塔的准确模拟对于进行全塔分析及装置操作、优化和节能研究具有重要意义。针对目前催化裂化装置模拟存在主分馏塔脱过热段模拟不准确、关键数据无法获得等问题,根据脱过热段特点提出了4类模拟方法,并进行了对比分析,同时结合实际案例进行了研究。通过对比研究得到,对于催化裂化装置主分馏塔的模拟采用脱过热段与分馏塔分开处理、脱过热段利用2个闪蒸罐和1个混合器的模拟方式,能够有效提高主分馏塔的模拟准确度,从而为催化裂化装置生产操作提供有效指导。     

吸收稳定系统集成化的设计、优化和分析方法

以某催化裂化装置吸收稳定系统为例，提出一个工业化装置集成化设计、优化和分析的方法。它从整体上确定工艺参数的最佳匹配，从而可同时达到最大产量和最低能耗，实现最大经济效益。通过对该吸收稳定系统的全局模拟和优化，真正揭示了工艺参数之间的内在联系和相互影响，并给出了定量的概念，同时提出了某些不同于传统观念的新论点。对于众多催化裂化装置的设计、改造和生产管理均有较重要的现实指导意义。     

A multiobjective optimization framework for design of integrated biorefineries under uncertainty

A systematic approach for development of a reliable optimization framework to address the optimal design of integrated biorefineries in the face of uncertainty is presented. In the current formulation, a distributed strategy which is composed of different layers including strategic optimization, risk management, detailed mechanistic modeling, and operational level optimization is applied. In the strategic model, a multiobjective stochastic optimization approach is utilized to incorporate the tradeoffs between the cost and the financial risk. Then, Aspen Plus models are built to provide detailed simulation of biorefineries. In the final layer, an evolutionary algorithm is employed to optimize the operating condition. To demonstrate the effectiveness of the framework, a hypothetical case study referring to a multiproduct lignocellulosic biorefinery is utilized. The numerical results reveal the efficacy of the proposed approach; it provides decision makers with a quantitative analysis to determine the optimum capacity plan and operating conditions of the biorefinery. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3208–3222, 2015     

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier☆

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP (Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm (DE), genetic algorithm (GA), and parti-cle swarm optimization algorithm (PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO (3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algo-rithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effective-ly. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms (EAs) can be improved, and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coal-water slurry gasifier shows outstanding computing results than actual industry use and other algorithms.