新冠疫情影响下直馏航煤加工路线优化选择

围绕新冠疫情影响下炼厂直馏航煤组分加工路线优化问题,采用COILSIM和Petro-SIM模拟平台,分别建立了乙烯裂解单元和催化裂化单元机理模型,并以此为基础进行模拟仿真,提出了炼厂航煤组分加工路线优化方案.在工业操作条件下,计算了航煤组分作为乙烯裂解原料以及加氢裂化尾油作为催化裂化原料的产品分布,并获得关键产品的性质数据.结合产品市场价格数据,计算了航煤组分作为乙烯裂解原料的加工效益,并将计算结果进行对比.结果表明,航煤组分作为乙烯原料同时加氢裂化尾油作为催化原料,可缓解航煤产品出厂压力,并提高高附加值产品产量,创造较好的经济效益,为炼化企业资源优化配置提供了有益思路.     

加氢裂化尾油收率预测、优化模型开发研究

从裂解炉的工业生产过程出发,对加氢裂化尾油裂解的模型化及优化进行理论和应用研究。对于复杂的重质原料裂解过程,基于严格、系统的模拟裂解试验,选用适当的数学处理方法建立模型,具有方便、快捷、准确的优点;对于工业裂解装置产品收率预测、工艺条件优化,具有实际应用价值。在模拟试验基础上建立产品收率预测和工艺优化操作模型。采用工业数据校正试验收率预测模型,调整模型参数,建立SL-Ⅰ型裂解炉的加氢裂化尾油收率预测、优化模型。     

镇海炼化公司两套加氢裂化装置的优化运行

通过分析比较镇海炼化公司两套不同类型的加氢裂化装置的运行工况,提出两套装置优化运行的建议和方案,从而为加氢裂化装置的节能降耗、降本增效、提高目的产品收率以及运行工况优化提供参考意见,实现企业整体经济效益最大化。     

基于PCA权重的化工报警阈值优化

为避免海量报警影响正常操作,对报警阈值进行了优化.考虑了变量相关性,利用主元分析法(PCA)分析变量的重要程度,并求出变量权重;结合国际标准,根据变量权重分配变量报警数,优化报警阈值;在平行坐标图上将变量数据及报警阈值可视化,查看正常和异常工况区域,考察各变量之间的变化趋势,提取有效报警值;对某工业原油常减压操作数据进行验证,结果表明优化方法能有效减少报警数.     

加氢装置氢气高效利用催化剂的工业应用

介绍HC-120催化剂在某公司年产2.20 Mt加氢裂化装置上的工业应用情况。结果表明,HC-120催化剂活性、选择性好,产品质量高且产品分布合理。与上一周期使用的多产柴油型裂化催化剂HC-115相比,初期操作反应温度相对较低,氢气消耗降低,重石脑油收率提高约5.3个百分点,航煤提高3.4个百分点,柴油收率降低5.0个百分点,能有效降低柴汽比。在达到重石脑油+航煤收率提高8.7个百分点的同时,氢气收率仍有所降低,大大提高了氢气利用效率;同时因催化剂活性提高,降低了装置能耗,提升了装置的经济效益。     

均匀实验设计及智能可视化优化软件合成某产品的应用

在某精细化学品合成实验中采用均匀实验设计法进行设计、试验,利用智能可视化软件对试验数据进行优化.结果表明,采用均匀实验设计与智能可视化软件相结合,减少了试验次数,缩短了研发周期;优化后的合成工艺稳定、可靠,产品收率≥98.5％,产品纯度≥99.5％.     

基于混合遗传算法的催化重整过程多目标优化

为实现催化重整过程生产指标的综合优化,基于已实现工业应用的催化重整17集总反应动力学模型和催化重整过程机理模型,考虑相应的多种约束条件,建立了以最大化总芳烃收率和最小化重芳烃收率为目标的多目标操作优化模型。提出了一种将遗传算法与局部优化方法相结合的多目标混合遗传算法HNAGA,并用于多目标操作优化模型的求解。现场工业数据的仿真研究表明,HNAGA在寻找Pareto最优解前沿方面比原遗传算法具有一定的优越性。将该多目标优化模型和求解方法应用于工业催化重整装置的操作优化,可以有效提高决策的准确性。     

MTO烯烃分离过程的多目标操作优化

现代煤化工中,甲醇制烯烃 （MTO） 是一个非常重要的装置。其烯烃分离过程面临着原料变动大、烯烃产品损失以及较高的公用工程消耗等问题。这就需要在满足产品规格和需求的情况下,优化操作条件以实现最大效益。以Lummus前脱丙烷的烯烃分离工艺为研究对象,以增加乙烯与丙烯的总收率和降低总能耗为优化目标,对该工艺流程进行建模模拟与多目标优化。采用非支配排序遗传算法（NSGA-II）进行多目标优化的求解,实现了15个操作变量的同时优化。在维持产品收率不变的前提下,可通过降低脱丙烷塔、脱乙烷塔和1^#丙烯精馏塔的回流比等优化措施找到了当前最优操作点。结果表明,该最优操作点与现有操作点相比可降低20 MW能耗。通过对决策变量的综合分析,确定了不同目标权衡下对应的各个操作变量的优化区间,发现精馏塔可以在多个最佳操作区间内运行。     

BP神经网络在加氢裂化装置航煤性质软测量中的应用

应用Matlab软件构建单隐层BP神经网络,并对中压加氢裂化装置航煤性质进行软测量应用。以700组样本数据作为训练集,对预测航煤闪点、终馏点模型进行训练。结果表明,在152组验证数据集上模型对闪点、终馏点预测分别取得1.57 ℃和2.74 ℃的均方误差(RMSE),随之在80组测试数据集上模型取得的泛化RMSE分别为1.87 ℃和1.98 ℃。以300组样本数据作为训练集,对预测航煤密度的模型进行训练。结果表明,在100组验证集上模型RMSE为2.18 kg·m^-3,随之在70组测试数据集上的泛化RMSE为2.72 kg·m^-3。BP神经网络的泛化RMSE表明,通过合理选择特征变量和设计网络结构,单隐层BP神经网络能够满足航煤性质的工业软测量要求。     

基于主元相似度的间歇过程操作曲线递推优化

针对一类较长周期的间歇过程操作优化问题，提出了一种基于正常运行批次的数据驱动型操作曲线递推优化方法。首先采用分段离散化方法将原非线性优化问题转化为线性优化问题，再利用主元分析对离散化后的高维时段变量进行降维处理，然后在降维后的主元平面中，基于时段变量与最终产品指标间的余弦相似度，实现对原操作曲线的摄动优化。考虑到时段变量方差和相似度随批次会发生变化，建立了递推算法以实现操作曲线的递推更新。最后将该方法应用于某化工产品的间歇结晶过程中，结果验证了所提方法的有效性。     

基于可拓理论的过程工业智能操作优化方法及应用

在生产过程装置及工艺确定的前提下,操作方案恰当与否直接影响产品质量产量及物耗能耗。由于生产过程操作变量繁多且关系复杂,传统操作调节方法已不能满足操作优化需求。本文提出了基于可拓理论的智能操作优化方法以寻求最佳操作方案。通过建立操作条件、生产目标及约束条件的物元、事元模型,采用可拓性分析及可拓变换得到操作调节方案,设定操作变量给定值形成操作方案集,并进行筛选评价得到最佳方案。结合某企业高密度聚乙烯(HDPE)串级反应过程生产操作验证了其有效性,为过程工业操作条件优化调节提供了新思路。     

水煤浆气化装置操作优化技术及其应用

针对水煤浆气化装置操作优化问题,提出了一种多种群竞争型协同文化差分进化算法(MCCDE),算法中建立了基于差分进化算法的竞争型协同策略及竞争适应度评判方法,并引入了文化算法的部分思想。同时,建立了德士古气化炉操作优化模型,将MCCDE算法用于优化模型的求解。采用某化工厂气化系统实际运行数据进行仿真,经过操作优化计算,能够获得优化的控制参数,并提高气化炉有效气产率。最后,开发了水煤浆气化操作优化系统应用软件,能够实现将建模、控制、优化技术应用于实际生产中,以提高装置的经济效益。     

一种应用于换热网络综合的阻尼优化方法

针对启发式方法优化换热网络在优化后期易陷入局部极值的问题,提出一种阻尼优化方法,即通过引入延缓概率的概念,以一定的概率不接受费用下降的结构,延缓该结构形成固定匹配,避免因连续变量优化过快导致整型变量优化不充分而陷入局部最优。通过探讨不同阶段的优化特点及优化陷入局部极值的成因,进而提出分阶段延缓策略,合理调控延缓条件以及延缓概率的取值,从而提高算法的全局搜索能力。最后采用四个不同规模的算例进行验证,结果表明该方法可有效地跳出局部最优解,促进结构的进一步优化。     

Domain reduction for Benders decomposition based global optimization

While domain reduction has been successfully applied in branch-and-bound based global optimization over the last two decades, it has not been systematically studied for decomposition based global optimization, which is usually more efficient for problems with decomposable structures. This paper discusses integration of domain reduction in Benders decomposition based global optimization, specifically, generalized Benders decomposition (GBD) and nonconvex generalized Benders decomposition (NGBD). Revised GBD and NGBD frameworks are proposed to incorporate bound contraction operations or/and range reduction calculations, which can reduce the variable bounds and therefore improve the convergence rate and expedite the solution of nonconvex subproblems. Novel customized bound contraction problems are proposed for GBD and NGBD, and they are easier to solve than the classical bound contraction problems because they are defined on reduced variable spaces. The benefits of the proposed methods are demonstrated through a gas production operation problem and a power distribution system design problem.     

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。     

Modeling and optimization of an industrial hydrocracking unit to improve the yield of diesel or kerosene

Hydrocracking is used in the petroleum industry to convert low-quality feedstocks into highly-valued transportation fuels. This process is the best source of low-sulfur and low-aromatics diesel fuel as well as high-smoke point jet fuel. Many approaches have been proposed for solving optimization of hydrocracking units in the last decades, but they usually neglect the reaction in hydrotreater where hydrocarbon cracking often occurs, thus leading to suboptimal solutions in industrial problems. Unlike existing literature, this paper considers the hydrocarbon cracking reactions in hydrotreater and hydrocracker simultaneously. The models are based on energy balance, mass balance and a discrete lumped model approaches for kinetic modeling. Before optimization, the properties of feedstock are predicted with ASPEN PLUS by using laboratory data from the refinery, and then the model parameters are estimated with genetic algorithm (GA) based on industrial data and validated by comparing the simulating results with industrial data. To improve the yield of the lighter products, the operation conditions are optimized by GA and Sequential Quadratic Programming (SQP). The yields of the diesel or kerosene increase with the proposed approach.     

Kinetic parameter estimation in hydrocracking using hybrid particle swarm optimization

This paper provides a continuous lumping model for hydrocracking using molecular weight and true boiling point as the basis. Isomerization and cracking are the two typical global reactions which occur on acid sites of a bifunctional acid-metal hydrocracking catalyst. The latter reaction is considered to model the hydrocracking process. It is assumed that the rate coefficient of the cracking reactions depend on the size of the feed hydrocarbons. The kinetic parameters involved in the continuous lumping model are estimated by using a hybrid particle swarm optimization method. The proposed kinetic methodology is validated with the experimental data cited in the literature.     

基于P-graph的乙烯裂解原料调度建模与优化

乙烯工业不同的裂解装置间存在着设备、技术上的差别，每一种原料在乙烯工厂不同炉型或工艺的裂解装置的乙烯产品收率、能耗也存在着差别。随着新的乙烯工厂的投产，需要同时运行台数众多的差异化裂解装置，从而为通过优化调度乙烯裂解原料实现提高物效、降低能耗提供了空间。对于此类工厂间原料调度及能耗优化问题提出了一种基于P-graph的建模和优化方法(scheduling generation based on P-graph, SGBP算法)，该算法通过P-graph本身提取过程结构信息的能力，在加速求解的同时，保留了次优解集。之后以两个实际的乙烯厂为研究实例，采用提出的SGBP方法实现了原料调度的建模和优化，该方法与MINLP优化算法的对比分析验证了提出方法的优势：(1)可以同时提供较为丰富的最优解与次优解方案；(2)提出方法的最优结果与MINLP的优化效果相当；(3)优化后的整体能耗下降明显，为生产计划人员选择可采用灵活的原料调配方案提供了多种可选择的运行方案。     

A multi-priority hierarchical optimization method for double-layer model predictive control

Considering the demand for the sequential regulation of manipulated variables in actual industrial process control, the conventional solution of double-layer model predictive control faces the problem that the weight coefficients are difficult to tune. This paper proposes an improved hierarchical optimization method for manipulated variables in the steady-state optimization layer of double-layer model predictive control. The proposed method can adjust the manipulated variables sequentially without an accurate weight coefficient to avoid difficulty in tuning the weight coefficients. The relation between the optimal solution and the feasible region of the steady-state optimization layer is analysed to describe the reoptimization of the key manipulated variables. The impact of the economic cost coefficient on the optimal solution with the sensitivity analysis method is studied, and the complexity of using the weight coefficient to solve the priority optimization problem of the manipulated variables is assessed. The steady-state optimization solution procedure is improved based on the theory of the multiobjective complete hierarchical method. The hierarchical and sequential optimization of the manipulated variables results in expanding the space and freedom of the key manipulated variables, increasing efficiency, reducing consumption, and improving economic performance. The improved hierarchical optimization method is direct and simple in achieving optimization sequentially and satisfies the need for adjusting the manipulated variables according to human intentions.     

Multi-stage intelligent operation optimization for a hydrocracking fractionation system with a multi-fractionator series-parallel structure

A fractionation system is an essential unit in the hydrocracking process. Its optimal operation is challenging because of the complexity in the structure of the distillation tower and composition of the stream. In addition, the series-parallel structure between the distillation towers of different techniques aggravates the coupling and complexity of the hydrocracking fractionation system (HFS). This, in turn, increases the time complexity of the optimization problem. In this paper, a rigorous mechanism model of an actual HFS is first applied to describe the operating conditions of the HFS. Then, an improved state transition algorithm (STA) with a staged evaluation strategy is proposed to solve the above problem. To overcome problems caused by the series-parallel structure of HFS, the model is divided into multiple stages for evaluation by mechanism analysis. Furthermore, several typical convergence estimation criteria are introduced to reduce unnecessary model calculations. To solve time-consuming problems associated with HFS optimization, the adaptive change operator is used to improve the search function of the original algorithm and two performance criteria are presented to reduce the optimization time. The proposed algorithm is successfully applied to the operational parameter optimization problem of HFS with a multi-fractionator series-parallel structure. The experimental results indicated that the staged evaluation strategy improved the fast convergence probability of the HFS mechanism model and reduced unnecessary calculations, whereas the improved algorithm increased accuracy and significantly decreased optimization time.