MOLECULAR MODELING FOR PLANT-WIDE OPTIMIZATION

In this article, molecular modeling of process streams and processes is combined with overall refinery optimization to select feedstocks and products, optimize the operation of different processes, and determine the most economic modifications. The molecular compositions of the hydrocarbon mixture are described using a MTHS (Molecular Type Homologous Series) matrix. To model reaction processes, structure-reactivity correlations are developed by regressing a wide range of pilot plant and commercial data. Molecular level kinetics is combined with current reactor modeling techniques. To integrate molecular modeling with plant-wide optimization, the interface between molecular compositions and bulk properties is also developed. The plant-wide optimization consists of a two-level optimization procedure, namely the site level and process level. The site level optimization addresses the overall trade-offs among the selections of feedstocks, products, processes, and the use of utilities. The process level optimization optimizes the individual processes by adjusting the detailed operating parameters. With the detailed molecular information available from the molecular model, it is possible to distinguish the contributions from different feedstocks and optimize the operation conditions to produce the most desirable products. A feedback mechanism is built for the coordination between these two levels. By optimizing the overall plant using molecular models, overall profit will be maximized by maximizing desired molecules to form valuable products, minimizing undesired ones, and routing all the molecules to their most appropriate destinations.     

MOLECULAR MODELING AND OPTIMIZATION FOR CATALYTIC REFORMING

In this paper, molecular modeling and optimization for the naphtha catalytic reforming process is studied. The catalytic reforming process is for producing high octane number gasoline by reforming reactions in three sequencing fixed bed reactors. Feed naphtha coming from an atmospheric distillation unit consisted of molecules from C5 to C10 including paraffin, iso-paraffin, naphthene, and aromatic. The molecular reaction network consisted of paraffin cracking, naphthene side-chain cracking, aromatic side-chain cracking, ring opening, ring closure, paraffin isomerization, dehydrogenation, and hydrogenation. A molecular model for catalytic reforming was built. On the basis of the simulation model, a process optimization was performed for feed temperature and pressure under constraints such as benzene content, aromatic content, and RON (Research Octane Number) limitations. High RON was contrasted to low benzene and aromatic content requirements. By optimizing and controlling the reaction pathway, we can obtain a final product with the highest profit and appropriate benzene and aromatic contents and RON value. This example shows significant benefits from applying molecular modeling to optimization in the process level. Since gasoline production is related to many different processes such as reforming, FCC, isomerization, alkylation, and so on, more benefits can be obtained by applying molecular modeling to plant-wide optimization.     

MOLECULAR MODELING AND OPTIMIZATION FOR CATALYTIC REFORMING

In this paper, molecular modeling and optimization for the naphtha catalytic reforming process is studied. The catalytic reforming process is for producing high octane number gasoline by reforming reactions in three sequencing fixed bed reactors. Feed naphtha coming from an atmospheric distillation unit consisted of molecules from C5 to C10 including paraffin, iso-paraffin, naphthene, and aromatic. The molecular reaction network consisted of paraffin cracking, naphthene side-chain cracking, aromatic side-chain cracking, ring opening, ring closure, paraffin isomerization, dehydrogenation, and hydrogenation. A molecular model for catalytic reforming was built. On the basis of the simulation model, a process optimization was performed for feed temperature and pressure under constraints such as benzene content, aromatic content, and RON (Research Octane Number) limitations. High RON was contrasted to low benzene and aromatic content requirements. By optimizing and controlling the reaction pathway, we can obtain a final product with the highest profit and appropriate benzene and aromatic contents and RON value. This example shows significant benefits from applying molecular modeling to optimization in the process level. Since gasoline production is related to many different processes such as reforming, FCC, isomerization, alkylation, and so on, more benefits can be obtained by applying molecular modeling to plant-wide optimization.     

基于分子矩阵的炼油过程的全厂模拟

针对复杂的炼油生产过程及其物流成分,采用分子矩阵表征相关的物流分子组成,对催化重整、加氢脱硫、汽油稳定塔和油品调和四个过程建立了基于分子矩阵的过程模型. 通过物性-分子矩阵转换关系可以方便地从已知物流物性计算分子矩阵以及由分子矩阵得到物流物性. 基于分子矩阵的模型计算不仅可以提供比传统虚拟组分模型更为详尽的分子信息,而且可以统一传统建模中不同过程虚拟组分和集总参数之间的差异,将多个过程模型集成实现全厂的模拟.     

Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

Applications of process systems engineering (PSE) in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past 10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multi-level control and optimization methods are exhibited,including at the operational,cycle,plant and enter-prise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.     

大型尿素装置模拟优化系统开发与应用

建立适于大型尿素装置模拟与优化分析的数学模型。开发面向对象的具有信息查询、物性计算、设备核算、流程模拟等多种功能的尿素生产流程模拟优化系统。对现场生产装置的优化讲话和于指导实际的生产操作和设备改造,优化生产操作参数,提高创业的计算机管理水平。     

Mathematical approach to compute the molecular composition of hydrothermal liquefaction-derived renewable crude oil

Hydrothermal liquefaction (HTL), a thermo-chemical conversion process, uses water as a reaction medium at elevated pressure and temperature, to convert biomass to renewable liquid fuel and recovers fertilizer-rich water. To assess the techno-economic screening of HTL oils from various feedstock, it is crucial to have information on molecular composition of the feed and products. There are limitations of existing analytical methods to identify and quantify all the molecules present in the bio-fuel. Therefore, there is a need to find alternate ways to quantify the molecular composition of feed and expected products. The modeling work on bio-oil is developed and validated on mathematical approach using simple analytical results like CHNO along with structural analysis of oil like Fourier-transform infrared, nuclear magnetic resonance analysis for HTL derived oil from microalgae. This mathematical framework is further extended to predict the molecular composition of oil obtained from HTL of feedstocks like mixed plastic waste, sludge, and so on. A multi-dimensional molecular matrix is developed based on the distributions of side chains, aromatic rings, and olefinic carbon on top of core molecules. The parameters of the distributions are estimated computationally using global optimization algorithm (genetic algorithm) and local optimization algorithm to predict a mixture composition that matches closely with bulk properties of the product.     

Optimization of the sulfolane extraction plant based on modeling and simulation

An optimization system, based on modeling and simulation, was developed for a sulfolane extraction plant. The primary objective of the operation of this plant is to increase benzene composition, which is mostly affected by the recycle streams in the plant. In this work the optimal recycle streams were identified and resulting product compositions were evaluated. In the optimization, suitable parametric models for each process unit were obtained first from the steady-state rigorous modeling and simulation of the sulfolane extraction plant. The parametric models were then employed to develop the optimization system based on the SQP scheme. Results of simulations show promise for further economic improvements over present operation states.     

Process systems engineering tools in the pharmaceutical industry

The purpose of this paper is to provide a summary of the current state of the application of process systems engineering tools in the pharmaceutical industry. In this paper, we present the compiled results of an industrial questionnaire submitted to pharmaceutical industry professionals. The topics covered in the questionnaire include process analytics, process monitoring, plant-wide information systems, unit operation modeling, quality control, and process optimization. A futuristic view of what process systems engineering tools will enable the pharmaceutical industry will be also be presented. While the industry is regularly using the traditional Design of Experiments approach to identify key parameters and to define control spaces, these approaches result in passive control strategies that do not attempt to compensate for disturbances. Special new approaches are needed for batch processes due to their essential dependence on time-varying conditions. Lastly, we briefly describe a novel data driven modeling approach, called Design of Dynamic Experiments that enables the optimization of batch processes with respect to time-varying conditions through an example of a simulated chemical reaction process. Many more approaches of this type are needed for the calculation of the design and control spaces of the process, and the effective design of feedback systems.     

Robust distributed economic model predictive control based on differential dissipativity

In this article, a robust distributed economic model predictive control (DEMPC) approach is developed for plant-wide chemical processes. The proposed approach achieves arbitrary feasible setpoints that may vary frequently, attenuates the plant-wide effects of unknown disturbances and minimizes a plant-wide economic cost. In this approach, a plant-wide process is represented as a network of process units and each process unit is controlled by an individual controller which shares a plant-wide optimization economic objective and stability conditions through the network. To ensure the convergence of process variables to arbitrary setpoints, a contraction condition is developed for the DEMPC, based on the contraction theory. To deal with the effects of interactions among process units, the concept of dissipativity is adopted. Using sum-separable control contraction metrics, a reference-independent robust stability condition is developed to ensure the plant-wide disturbance effects (under interactions among process units) to be attenuated in terms of differential ℒ₂ gain and represented by a plant-wide differential dissipativity condition, which is converted into the differential dissipativity conditions that individual controllers need to satisfy. This approach facilitates the optimization of plant-wide economic costs with global constraints in a distributed way, allowing efficient implementation of alternating direction method of multipliers (ADMM). The proposed approach is illustrated using a reactor-separator process.     

Modeling and simulation of energy distribution systems in a petrochemical plant

A systematic method for analysis and design of plant-wide energy distribution systems is proposed to minimize the net cost of providing energy to the plant. The method is based on the steady-state modeling and simulation of steam generation process and steam distribution network. Modeling of steam generation process and steam distribution network were performed based on actual plant operation data. Heuristic operational knowledges are incorporated in the modeling of steam distribution network. Newton’s iteration method and a simple linear programming algorithm were employed in the simulation. The letdown amount from superheated high-pressure steam (SS) header and the amount of SS produced at the boiler showed good agreement with those of actual operational data.     

基于混合分块DMICA-PCA的全流程过程监控方法

分块策略被广泛运用于全流程过程监控领域,以解决全流程过程变量关系复杂性较高的问题,但传统的分块策略与子块建模方法都未考虑过程的动态性问题,并且传统的分块策略都片面依赖于过程知识或过程数据信息,影响了过程监控的效果,为此提出了一种基于混合分块DMICA-PCA的过程监控方法。在分析过程的动态性后,先利用已知的部分过程知识进行变量的初步分块,接着利用各分块变量之间改进的广义Dice's系数（MGDC）进行进一步的分块。然后采用DMICA-PCA方法对每个子块进行建模得到子块的统计量,并通过加权方法得到总的联合指标进行故障检测。同时对每个子块采用改进的故障诊断方法,提高了诊断效果。最后将该方法应用在TE过程的过程监控中,证明了该方法的有效性。     

Iterative lumping approach for representing lipid feedstocks in fatty acid distillation simulation and optimization

The complexity of lipid feedstocks and the lack of data on physical properties hinder the simulation of oleochemical processing units. In this work, an iterative lumping approach is proposed to define an adequate number of key compounds such that diversification between lipid feedstocks becomes possible, while keeping the determination of physical properties as required for process modeling manageable. As a case study, the iterative lumping approach is used for simulation and optimization of a fatty acid distillation plant. For predicting vapor–liquid equilibria of fatty acids, the best results were acquired using the property method universal quasichemical-Hayden–O'Conell. Using the iterative lumping approach, 11 key compounds were selected to represent the feedstock. The process model properly predicts the product composition, yield, purity and heat duty. The most important process parameters are found to be side-reflux-ratio, reboiler-outlet-temperature, and heat-duty of the pitch-distiller. For optimization, an increase of the side-reflux-ratio and reboiler-outlet-temperature is recommended.     

炼焦生产过程质量产量能耗的集成优化控制

针对炼焦生产过程强非线性、大时滞等特点,基于过程参数的主元分析和灰色关联分析,建立了焦炭质量、产量及焦炉能耗的神经网络预测模型和以焦炭质量为约束条件,产量、能耗为目标函数的优化控制模型。提出一种融合模糊C均值聚类粗优化和差分进化细优化的集成优化控制方法,进行过程参数的优化并给出操作优化指导。系统仿真结果表明,该方法能有效地抑制工况的波动,达到高产、优质、低耗的生产目标,为复杂工业过程的建模和优化控制提供了一种新思路。     

A two‐zone model for fluid catalytic cracking riser with multiple feed injectors

Developments in modeling of the fluid catalytic cracking (FCC) process have progressed along two lines. One emphasizes composition‐based kinetic models based on molecular characterization of feedstocks and reaction products. The other relies on computational fluid dynamics. The aim is to develop an FCC model that strikes a balance between the two approaches. Specifically, we present an FCC riser model consisting of an entrance‐zone and a fully developed zone. The former has four overlapping, fan‐shaped oil sprays. The model predicts the plant data of Derouin et al. and reveals an inherent two‐zone character of the FCC riser. Inside the entrance zone, cracking intensity is highest and changes rapidly, resulting in a steep rise in oil conversion. Outside the entrance zone, cracking intensity is low and varies slowly, leading to a sluggish increase in conversion. The two‐zone model provides a computationally efficient modeling approach for FCC online control, optimization, and molecular management. © 2014 American Institute of Chemical Engineers AIChE J, 61: 610–619, 2015     

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。     

应用Aspen Batch对年产25吨鲁拉西酮原料药工艺设计优化

采取专利CN 102863437 A的工艺,设计间歇工艺过程,将Aspen Batch Process Developer软件应用于盐酸鲁拉西酮原料药车间设计的全流程模拟和优化。间歇操作具有显著的优势：生产灵活,同一设备可生产不同产品,可根据市场需要调节生产能力及变更产品。适合于小批量,高收益的精细化学品。过去的四十年里,使用计算机对化工连续化生产进行模拟和设计已经十分普及。制药工业与传统化工最大的区别是生产过程多采用间歇法操作。目前世界上应用于化工间歇生产的计算机软件有BATCHES、gPROMS和Aspen Batch Process Developer。本文所用版本为Aspen Tech V8.6,以年产25t盐酸鲁拉西酮原料药车间为例,对车间进行全流程模拟及优化。整个设计贯彻质量源于设计理念,运用元葱模型,将盐酸鲁拉西酮的生产工艺分为磺化、氨解、氢化、缩合、成盐、精烘包等6个模块。     

实时优化技术在乙烯装置在线优化中的应用

乙烯产业是重要的石化基础产业,对国民经济、石油化工产业及工业社会起着举足轻重的作用,近年来 在我国经济增长过程中保持着蓬勃发展的态势,但是,由于我国乙烯生产总体技术水平的相对滞后以及乙烯工 业所供原料复杂等因素,导致我国目前乙烯装置与世界乙烯生产先进水平存在一定差距,为了减小这一差距, 达到世界先进水平,离不开工业过程自动化和运用实时优化技术。为此,本文介绍了实时优化技术的国内外发 展现状和该技术在乙烯装置的应用情况,并通过实时优化系统的实施,建立了乙烯装置的全流程严格机理模型, 可以实时跟踪乙烯装置的生产情况,持续不断对装置进行在线优化,使装置的操作达到最佳的经济效益操作点, 此外,在优化中以装置的原料、产品和公用工程等价格为导向,对全装置生产过程自动操作执行,减少了优化 计算和结果执行中的人为干预。分析表明实时优化技术有效实现了乙烯装置增产、节能和降耗的目标,可为乙 烯生产企业创造新增经济效益。     

煤制烯烃企业全厂能量系统优化策略及应用

为了实现煤制烯烃企业全厂能量系统优化,提出了煤制烯烃企业全厂能量系统基于单元、子系统和全局划分的渐进协同优化策略,包括全厂用能状况分析、工艺装置用能及换热网络优化、低品位热回收和优化利用、全厂蒸汽动力系统优化等步骤。应用上述优化策略,对某60万t/a煤制烯烃企业全厂能量系统优化进行研究,得到全厂各单元能耗分布,提出了甲醇合成和甲醇制烯烃单元换热网络优化、全厂蒸汽动力系统降低蒸汽减温减压量及低品位热合理匹配利用等节能方案,理论上可以提高全厂系统能效3%以上,年节约标煤10万t以上,投资回收期小于1 a。     

Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol

The reactor modeling and recipe optimization of conventional semibatch polyether polyol processes, in particular for the polymerization of propylene oxide to make polypropylene glycol, is addressed. A rigorous mathematical reactor model is first developed to describe the dynamic behavior of the polymerization process based on first‐principles including the mass and population balances, reaction kinetics, and vapor‐liquid equilibria. Next, the obtained differential algebraic model is reformulated by applying a nullspace projection method that results in an equivalent dynamic system with better computational performance. The reactor model is validated against plant data by adjusting model parameters. A dynamic optimization problem is then formulated to optimize the process recipe, where the batch processing time is minimized, given a target product molecular weight as well as other requirements on product quality and process safety. The dynamic optimization problem is translated into a nonlinear program using the simultaneous collocation strategy and further solved with the interior point method to obtain the optimal control profiles. The case study result shows a good match between the model prediction and real plant data, and the optimization approach is able to significantly reduce the batch time by 47%, which indicates great potential for industrial applications. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2515–2529, 2013