化工过程运行系统的集成

提出了基于智能体方法的化工过程运行系统的集成策略.依据过程操作响应的时间尺度,把过程运行系统分成故障检测与诊断、模拟优化和调度3个子系统,分别建立相应的智能体模型.通过信息集成和任务集成实现化工过程运行系统的整体集成.报道了该集成方法对TE过程的案例应用.结果表明：基于智能体的方法能实现多个运行决策的全局协调和集成,达到过程运行系统整体优化的目标.     

Multistream heat exchangers: Equation‐oriented modeling and flowsheet optimization

Multistream heat exchangers (MHEXs), typically of the plate‐fin or spiral‐wound type, are a key enabler of heat integration in cryogenic processes. Equation‐oriented modeling of MHEXs for flowsheet optimization purposes is challenging, especially when streams undergo phase transformations. Boolean variables are typically used to capture the effect of phase changes, adding considerable difficulty to solving the flowsheet optimization problem. A novel optimization‐oriented MHEX modeling approach that uses a pseudo‐transient approach to rapidly compute stream temperatures without requiring Boolean variables is presented. The model also computes an approximate required heat exchange area to determine the optimal tradeoff between operating and capital expenses. Subsequently, this model is seamlessly integrated in a previously‐introduced pseudo‐transient process modeling and flowsheet optimization framework. Our developments are illustrated with two optimal design case studies, an MHEX representative of air separation operation and a natural gas liquefaction process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1856–1866, 2015     

Equation‐oriented flowsheet simulation and optimization using pseudo‐transient models

Tight integration through material and energy recycling is essential to the energy efficiency and economic viability of process and energy systems. Equation‐oriented (EO) steady‐state process simulation and optimization are key enablers in the optimal design of integrated processes. A new process modeling and simulation concept based on pseudo‐transient continuation is introduced. An algorithm for reformulating the steady‐state models of process unit operations as differential‐algebraic equation systems that are statically equivalent with the original model is presented. These pseudo‐transient models improve the convergence of EO process flowsheet simulations by expanding the convergence basin. This concept is used to build a library of pseudo‐transient models for common process unit operations, and this modeling concept seamlessly integrates with a previously developed time‐relaxation optimization algorithm. Two design case studies are presented to validate the proposed framework. © 2014 American Institute of Chemical Engineers AIChE J 60: 4104–4123, 2014     

Modular dynamic simulation for integrated particulate processes by means of tool integration

In this contribution a sequential modular strategy for the dynamic simulation of particulate process flowsheets is presented and the efficiency of the approach is demonstrated by means of an example process for the crystallization of pentaerythritol. The flowsheet of the process consists of a number of different unit operations, e.g. evaporator, crystallizer, hydrocyclone, and mixer, which are described by mathematical models of largely varying complexity and structure. A key advantage of the presented sequential modular strategy is that specialized tools can be selected for the modelling and solution of each unit operation in the flowsheet. The tools are then coupled together by means of the tool integration framework CHEOPS in order to capture the overall structure of the flowsheet. In a case study, a startup of a crystallization flowsheet is carried out. As a result, detailed information about the dynamic plantwide process behavior is obtained. The practical relevance of the approach is demonstrated by means of a scenario where potential blocking of the filters following the crystallizer has been analyzed.     

Integration of Design and Control: A Robust Control Approach Using MPC

This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.     

Integrated Optimization Strategies for Dynamic Process Operations

Process systems engineering faces increasing demands and opportunities for better process modeling and optimization strategies, particularly in the area of dynamic operations. Modern optimization strategies for dynamic optimization trace their inception to the groundbreaking work Pontryagin and his coworkers, starting 60 years ago. Since then the application of large-scale non-linear programming strategies has extended their discoveries to deal with challenging real-world process optimization problems. This study discusses the evolution of dynamic optimization strategies and how they have impacted the optimal design and operation of chemical processes. We demonstrate the effectiveness of dynamic optimization on three case studies for real-world reactive processes. In the first case, we consider the optimal design of runaway reactors, where simulation models may lead to unbounded profiles for many choices of design and operating conditions. As a result, optimization based on repeated simulations typically fails, and a simultaneous, equationbased approach must be applied. Next we consider optimal operating policies for grade transitions in polymer processes. Modeled as an optimal control problem, we demonstrate how product specifications lead to multistage formulations that greatly improve process performance and reduce waste. Third, we consider an optimization strategy for the integration of scheduling and dynamic process operation for general continuous/batch processes. The method introduces a discrete time formulation for simultaneous optimization of scheduling and operating decisions. For all of these cases we provide a summary of directions and challenges for future integration of these tasks and extensions in optimization formulations and strategies.     

精馏过程多工况优化设计的能量流结构理论

以“三环节”能量流结构理论为基础建立了精馏过程多工况优化设计的火用经济数学模型,提出了精馏过程的多工况优化设计策略方法：将其分解为单个塔的多工况优化设计和塔间热集成的优化两个层次,有效输出火用价c_U和待回收火用价c_O为协调变量来解决多工况优化设计问题.并给出了应用实例.     

化工过程系统集成建模的面向智能体方法

引　言化工过程系统中常用的建模方法是结构化分析方法和面向对象方法 .面向对象方法较好地克服了结构化方法按功能将系统分解的缺点 ,但对化工生产过程的故障诊断、模拟优化、排产等具有主动性的事物采用对象来描述具有一定的缺点[1] .因此 ,在面向对象方法的基础上提出了面向     

Rechnergestützte Erstellung verfahrenstechnischer Prozeßmodelle

Computer-aided generation of chemical engineering process models. The availability of adequate process models is still the most striking bottleneck for routine application of model-based techniques in process design and operation. The development of knowledge-based software tools to support process modeling is considered an important contribution to a solution of this problem. After a brief summary of the modeling tools offered in current simulator architectures, a future knowledge-based modeling tool is sketched. In addition to the software engineering, the acquisition and formalization of the modeling knowledge – comprising meaningful submodels and generic modeling strategies – is essential for the feasibility of the approach and for the acceptance of a tool. Therefore, these two themes are discussed in considerable detail. Finally, first prototype developments towards a knowledge-based modeling tool are presented.     

A Modular Mixed‐Integer Non‐Linear Programming Algorithm for Synthesis of Chemical Processes

A modular approach to the formulation and a solution of mixed‐integer non‐linear programming (MINLP) problems are presented, which reduce the size of MINLP and the computational expenses effectively. The method decomposes the synthesis task into three hierarchical levels—the superstructure, the structure, and the modules, with the layer of modules being the most critical to the problem solution. The strategy has been implemented in a simulation environment in which the variables of interest are defined as implicit functions of the optimization variables. The implicit relationships are handled using a data‐oriented process simulation technique (DOPS) that significantly simplify the simulation. The method has been effectively applied to two case studies, one from literature for the synthesis of hydrodesalkylation, and another from industrial process manufacturing methylene diphenylene diisocyanates.     

Continuous global optimization of structured process systems models

Nonconvex models for the optimization of process systems in chemical engineering give rise to multiple suboptimal solutions, and a number of complications that often cause failure of standard local optimization techniques. A deterministic branch and bound algorithm is presented in this paper for the global optimization of structured process systems models that include non-convexities introduced by concave univariate, bilinear and linear fractional terms. The proposed branch and contract algorithm relies on a bounds contraction operation, which consists of the solution of a finite sequence of convex bounds-contraction subproblems for the subset of nonconvex variables in a problem. The application of the proposed algorithm is illustrated with several numerical examples, which include heat exchanger networks, chemical reactors, simplified process flowsheets, and waste-water treatment systems. The results show that by executing the contraction operation at selected branch and bound nodes, large portions of the search region over which the objective function takes only values above a known upper bound are eliminated. It is shown that with the proposed approach the total number of nodes in the solution tree is kept relatively small, and for some problems, no branching is required at all.     

Mathematical programming tools for chemical engineering process design synthesis

The objective of this paper is to review different optimization techniques used in chemical process synthesis. The paper first defines the process synthesis problem as a mixed-integer programming problem and then discusses the three approaches which have been used to solve it. In the discrete approach, where the operating conditions of unit operations are fixed, combined heuristic and algorithmic procedures are promising tools. In the continuous approach, the synthesis problem is a nonlinear programming problem, where the design and structural variables are optimized simultaneously. Since efficient large-scale nonlinear programming codes are now available, this approach will be a great success. A recent alternative consists in merging the two previous approaches to implement large-scale mixed-integer nonlinear programming codes. This method will have wide applications in complex chemical process synthesis.     

Integrated scheduling and dynamic optimization of batch processes using state equipment networks

A systematic framework for the integration of short‐term scheduling and dynamic optimization (DO) of batch processes is described. The state equipment network (SEN) is used to represent a process system, where it decomposes the process into two basic kinds of entities: process materials and process units. Mathematical modeling based on the SEN framework invokes both logical disjunctions and operational dynamics; thus the integrated formulation leads to a mixed‐logic dynamic optimization (MLDO) problem. The integrated approach seeks to benefit the overall process performance by incorporating process dynamics into scheduling considerations. The solution procedure of an MLDO problem is also addressed in this article, where MLDO problems are translated into mixed‐integer nonlinear programs using the Big M reformulation and the simultaneous collocation method. Finally, through two case studies, we show advantages of the integrated approach over the conventional recipe‐based scheduling method. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Optimization of IGCC processes with reduced order CFD models

Integrated gasification combined cycle (IGCC) plants have significant advantages for efficient power generation with carbon capture. Moreover, with the development of accurate CFD models for gasification and combined cycle combustion, key units of these processes can now be modeled more accurately. However, the integration of CFD models within steady-state process simulators, and subsequent optimization of the integrated system, still presents significant challenges. This study describes the development and demonstration of a reduced order modeling (ROM) framework for these tasks. The approach builds on the concepts of co-simulation and ROM development for process units described in earlier studies. Here we show how the ROMs derived from both gasification and combustion units can be integrated within an equation-oriented simulation environment for the overall optimization of an IGCC process. In addition to a systematic approach to ROM development, the approach includes validation tasks for the CFD model as well as closed-loop tests for the integrated flowsheet. This approach allows the application of equation-based nonlinear programming algorithms and leads to fast optimization of CFD-based process flowsheets. The approach is illustrated on two flowsheets based on IGCC technology.     

A systems engineering perspective on process integration in industrial biotechnology

Biotechnology has many applications in health care, agriculture, industry and the environment. By using renewable raw materials, biotechnology contributes to lowering greenhouse gas emissions and moving away from a petro‐based towards a circular sustainable economy. However, major developments are still needed to make industrial biotechnology an economic alternative to conventional processes for fuels, specialty and/or bulk chemicals production. Process integration is a holistic approach to process design, which emphasizes the unity of the process and considers the interactions between different unit operations from the outset, rather than optimizing them separately. Furthermore, it also involves the substitution of two or more unit operations by one single novel unit capable of achieving the same process goal. Conversely, process systems engineering (PSE) deals with the analysis, design, optimization, operation and control of complex process systems, as well as the development of model‐based methods and tools that allow the systematic development of processes and products across a wide range of systems involving physical and chemical change. Mature tools and applications are available for chemical technology and steps have been taken to apply PSE principles also to bioprocess technology. This perspective paper argues that an interdisciplinary approach is needed towards integrated bio‐processing in order to link basic developments in biosciences with possible industrial applications. PSE can foster the application of existing and the development of new methods and tools for bioprocess integration that could promote the sustainable production of bio‐/chemical products. The inclusion of PSE principles and methods in biochemical engineering curricula and research is essential to achieve such goals. © 2014 Society of Chemical Industry     

Gray‐Box Modeling for the Optimization of Chemical Processes

The availability of predictive models for chemical processes is the basic prerequisite for offline process optimization. In cases where a predictive model is missing for a process unit within a larger process flowsheet, measured operating data of the process can be used to set up such models combining physical knowledge and process data. In this contribution, the creation and integration of such gray‐box models within the framework of a flowsheet simulator is presented. Results of optimization using different gray‐box models are shown for a virtual cumene process.     

Process integration technology review: background and applications in the chemical process industry

Process integration is a holistic approach to process design and operation which emphasizes the unity of the process. Process integration design tools have been developed over the past two decades to achieve process improvement, productivity enhancement, conservation in mass and energy resources, and reductions in the operating and capital costs of chemical processes. The primary applications of these integrated tools have focused on resource conservation, pollution prevention and energy management. Specifically, the past two decades have seen the development and/or application of process integration design tools for heat exchange networks (HENs), wastewater reduction and water conservation networks, mass exchange networks (MENs), heat‐ and energy‐induced separation networks (HISENs and EISENs), waste interception networks (WINs) and heat‐ and energy‐induced waste minimization networks (HIWAMINs and EIWAMINs), to name a few. This paper provides an overview of some of these developments and outlines major driving forces and hurdles. The fundamental aspects of this approach along with their incorporation in an overall design methodology will be discussed. The paper also highlights several recent applications of process integration to industrial processes. Copyright © 2003 Society of Chemical Industry     

Energy optimization of bioethanol production via gasification of switchgrass

In this article, we address the conceptual design of the bioethanol process from switchgrass via gasification. A superstructure is postulated for optimizing energy use that embeds direct or indirect gasification, followed by steam reforming or partial oxidation. Next, the gas composition is adjusted with membrane‐PSA or water gas shift. Membrane separation, absorption with ethanol‐amines and PSA are considered for the removal of sour gases. Finally, two synthetic paths are considered, high alcohols catalytic process with two possible distillation sequences, and syngas fermentation with distillation, corn grits, molecular sieves and pervaporation as alternative dehydration processes. The optimization of the superstructure is formulated as an mixed‐integer nonlinear programming problem using short‐cut models, and solved through a special decomposition scheme that is followed by heat integration. The optimal process consists of direct gasification followed by steam reforming, removal of the excess of hydrogen and catalytic synthesis, yielding a potential operating cost of $0.41/gal. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Identification of uncertain MIMO Wiener and Hammerstein models

Several approaches can be found in the literature to perform the identification of block oriented models (BOMs). In this sense, an important improvement is to achieve robust identification to cope with the presence of uncertainty.In this work, two special and widely used BOMs are considered: Hammerstein and Wiener models. The models herein treated are assumed to be described by parametric representations. The approach introduced in this work for the identification of the multiple input-multiple output (MIMO) uncertain model is performed in a single step. The uncertainty is described as a set of parameters which is found through the solution of an optimization problem.A distillation column simulation model is presented to illustrate the robust identification approach. This process is an interesting benchmark due to its well-known nonlinear dynamics. Both Hammerstein and Wiener models are used to represent this plant in the presence of uncertainty. A comparative study between these models is established.     

Optimum heat storage design for solar‐driven absorption refrigerators integrated with heat exchanger networks

A methodology is presented for optimizing hybrid renewable energy‐fossil fuel systems with short‐term heat storage. The considered system is an absorption‐refrigeration (AR) cycle integrated with a heat exchanger network (HEN) requiring cooling below ambient temperature. The AR cycle can be driven by multiple energy sources including excess energy from hot process streams, renewable energy sources (solar and biofuels), and fossil fuels. A two‐step approach based on mixed integer nonlinear programming methods is used for the optimization. First, the problem of optimal energy integration in the hybrid energy system without heat storage is solved on a monthly basis by minimizing simultaneously the total annual cost and the overall greenhouse gas emissions. In the second step, the multi‐tank thermal energy storage (TES) design problem is solved. The design involves the identification of the optimal number of storage tanks, their sizes, configuration and operation policies. The TES optimization is carried out on an hourly basis while incorporating the design targets determined by the first step. © 2013 American Institute of Chemical Engineers AIChE J, 60: 909–930, 2014