ORC工质选择的多级非结构性模糊决策分析

工质的选择是有机朗肯循环(ORC)系统优化中的关键问题之一。建立了基于多级非结构性模糊决策分析方法的ORC工质优选体系,根据影响因素的非结构性的特点建立三级模糊优选模型,综合考虑ORC系统的技术性能、经济性能和环保性能3方面因素的影响,并针对影响ORC工质优选的因素复杂、确定隶属函数主观因素较强的情况引入非结构性模糊决策法以确定其隶属度与权重。应用此模型对150℃热源条件下某ORC系统进行工质的优选,得到了不同评价级对应的优选工质序列。R123是对应三级评价准则下该ORC系统的最优工质,验证了多级非结构性模糊决策模型在ORC工质优选中的适用性。     

基于动态规划的油气管网路径优选

油气管网路径优选的目标是使油气管网的运行费用最低,以管网系统输送的总费用最小为目标函数,建立路径优选的数学模型,并应用动态规划法进行了求解.具体实例表明,应用动态规划能够对油气管网的运行管理进行优化,确定运行费用最低的输送路径及最小运行费用.     

多目标半结构理论在煤矸石加工方案优选中的应用

运用二元比较模糊决策分析法确定定性目标相对优属度,设想将定量目标当作定性目标,从而得到定量目标与定性目标相对统一计算标准的相对优属度,通过多目标半结构模糊优选理论模型进行方案的优选。     

多级模糊模式识别法在通风系统方案优选中的应用

针对复杂的矿井通风系统,从解决矿山实际问题的角度提出若干优化方案,但是由于通风系统的不确定性,很难从技术可行的优化方案中选择更为经济、合理的方案,因此本文在数值模拟的基础上建立模糊综合评价模型,对模糊评价指标进行量化,采用多级模糊模式识别法确定最优相对隶属度矩阵,并利用级别特征值对样本进行分级,在技术可行的若干优化方案中,从矿山安全和经济两个方面对通风系统的优化方案进行综合评价,确定最优的评价方案。以安徽某铁矿矿井通风系统改造方案的优选为例进行验证,其优选结果证明该方法量化程度高、准确可靠、实用性强,为企业管理者的决策提供了指导。     

化工系统工程讲座(十二)——第七讲 动态过程系统的最优化方法(Ⅱ)

<正> 三、动态规划 动态规划是解决多级决策过程最优化问题的一种方法。所谓多级决策过程是指由于过程的特殊性可以将过程分为若干级,而在每一级(或阶段)都必须作出决定,以便使整个过程是取得最优效果的一种过程。     

基于改进灰色关联逼近理想解法的采矿方法优选

将改进的隶属度计算方法和组合赋权方式应用于灰色关联逼近理想解法,建立了采矿方法优选的改进灰色关联逼近理想解法模型,并以上横山矿段钒矿为例证明了该方法用于采矿方案优选的有效性。评价结果与模糊物元法和多级模糊优选法所得结果取得了很好的一致性,且具有更高的分散性,易于决策,可作为采矿方法优选决策的可靠依据。     

污水处理工艺流程方案的模糊优选法

针对目前污水处理工艺流程选择涉及因素多,且较为复杂的状况,在综合考虑各种影响因素的基础上,利用模糊综合评判理论建立了多级综合评判优选模型,应用实例介绍了该模型的使用方法。研究结果表明,利用此模型能较好地对各种污水处理工艺流程方案进行综合评价,从而为快速、准确地优选出所需的污水处理工艺流程提供了依据。     

基于模糊层次分析法的采矿方法优选

为了解决某铁矿采矿方法设计问题,提出了基于模糊层次分析法的多目标决策模型。根据该矿的地质条件初选出3种开采方案,首先构建多目标决策体系,其次利用模糊层次分析法确定各因素权重,再利用模糊数学确定各因素隶属矩阵并进行综合评判,最后根据隶属度对方案进行优选,优选结果表明阶段空场嗣后充填法为最佳采矿方法。     

多目标决策--模糊综合评判优选采矿方案

基于矿业模糊多目标决策过程评判指标多和定性、定量指标并存的特点,建立多指标层次模糊综合评判决策模型,并对某铜矿的开采方案优选进行实例分析计算,取得了满意的效果。     

粮食干燥技术评价指标体系的构建

通过运用模糊综合评价法、层次分析法对粮食干燥技术构建评价指标体系,提出运用多级模糊综合评价方法以及全面的定量化和定性化的层次结构评价模式,通过300t/d玉米真空低温连续干燥实际案例进行综合评价,建立了塔型连续真空干燥设计方案的模糊综合评价、粮食干燥企业工艺优选模糊综合评价、粮食干燥品质质量AHP综合评价和粮食干燥整体效益AHP综合评价,结果表明四种综合评判体系的构建是可行的。     

Multiobjective dynamic optimization of batch free radical polymerization process catalyzed by mixed initiator systems

The optimal control policies for batch free radical polymerization of styrene catalyzed by a binary mixture of monofunctional initiators have been determined using a multiobjective dynamic optimization technique. The process objectives considered in the optimization include monomer conversion, polymer molecular weight, initiator residue level, and total reaction time. It is illustrated through model simulations and experiments that the performance of the batch polymerization process can be improved significantly through the use of optimal initiator mixture and polymerization temperature programming. This paper also illustrates how the multiobjection optimization technique can be used effectively to solve complex polymerization reactor optimization problems with detailed reaction models.     

Hydrogen and CO2 Management in the Refinery with Fuzzy Multiobjective Nonlinear Programming

An innovative approach for optimization of the hydrogen network in a refinery is presented. The optimization problem was formulated as a fuzzy‐based multiobjective nonlinear programming (FMONLP), aiming at simultaneous minimization of the total annual cost and CO₂ emission. This is achieved by defining an objective function with a weighted sum of the annual cost and CO₂ emission. The weighting factors are considered as fuzzy parameters which are described based on the experts' experiences. The applicability of the proposed approach is illustrated by optimization of an Iranian refinery hydrogen network.     

Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed‐integer dynamic optimization problem or a large‐scale mixed‐integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed‐integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2379–2406, 2013     

Performance analysis and fuzzy optimization of a two-stage fermentation process with cell recycling including an extractor for lactic acid production

In this study, we consider a two-stage fermenter with cell recycling including an extractor to produce lactic acid. When the extractor was omitted, the proposed process was simplified to two special processes. Under the same operating conditions, we compared the overall lactic acid productivity and glucose conversion for the three processes. The proposed fermentation process was more efficient than the other processes. To simultaneously obtain the maximum productivity and conversion, the problem is formulated as a fuzzy multiobjective optimization problem. The fuzzy decision is introduced to convert the multiobjective fuzzy optimization problem into the fuzzy goal attainment problem. Hybrid differential evolution is applied to solve the fuzzy goal attainment problem to obtain a global Pareto solution.     

Multiobjective optimization of an industrial grinding operation under uncertainty

Multiobjective optimization of an industrial grinding operation under various parameter uncertainties is carried out in this work. Two sources of uncertainties considered here are related to the (i) parameters that are used inside a model representing the process under consideration and subjected to experimental and regression errors and (ii) parameters that express operators’ choice for assigning bounds in the constraints and operators prefer them to be expressed around some value rather than certain crisp value. Uncertainty propagation of these parameters through nonlinear model equations is reflected in terms of system constraints and objectives that are treated here using chance constrained fuzzy simulation based approach. Such problems are treated in literature using the standard two stage stochastic programming methodology that has a drawback of leading to combinatorial explosion with an increase in the number of uncertain parameters. This problem is overcome here using a combination of fuzzy and chance constrained programming approach that tackles the problem by representing and treating the uncertain parameters in a different manner. Simultaneous maximization of grinding circuit throughput and percent passing mid size fraction are studied here with upper bound constraints for various performance metrics for the grinding circuit, e.g. percent passing of fine and coarse size classes, percent solids in the grinding circuit final outlet stream and circulation load of the grinding circuit. Uncertain parameters considered are grindability indices of rod mill and ball mill, sharpness indices of primary and secondary cyclones and the respective upper bounds for the constraints mentioned above. The deterministic multiobjective grinding optimization model of Mitra and Gopinath [2004. Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm. Chem. Eng. Sci. 59, 385-396.] forms the basis of this work on which various effects of uncertain parameters are shown and analyzed in a Pareto fashion. Nondominated sorting genetic algorithm, NSGA II, a popular elitist evolutionary multiobjective optimization approach, is used for this purpose.     

A fuzzy multiobjective approach for minimization of injection molding defects

This paper describes a fuzzy multiobjective optimization approach for determining the set‐points of the injection molding processing parameters to minimize the defects formed on the molded parts. The severities of the defects are represented by membership functions using the fuzzy set theory. The minimization of these membership functions, which is a multiobjective optimization problem, is transformed into a substitute problem. The preference function in the substitute problem is original and is proposed specifically for characterizing the quality requirements of the injection molding defects. The formulated optimization problem is solved with design of experiments, in which the process behavior is approximated empirically by a set of quadratic polynomials that can be easily optimized. Experimental results are presented to emphasis the workability of the proposed methodology.     

A multiobjective approach to design chemical plants with robust dynamic operability characteristics

In chemical plants, operability problems arise mainly due to poor process designs, inaccurate models and/or the control system designs that are unable to cope with process uncertainties. In this paper, a process design methodology is presented that addresses the issue of improving dynamic operability in the present of process uncertainty through appropriate design modifications. The multiobjective nature of the design problem is carefully exploited in the subsequent formulations and a nonlinear programming approach is taken for the simultaneous treatment of both steady-state and dynamic constraints.

Scope—Today, a chemical engineer faces the challenge of designing chemical plants that can operate safely, smoothly and profitably within a dynamic process environment. For a typical chemical plant, major contributions to such an environment originate from external disturbances such as variations in the feedstock quality, different product specifications and/or internal disturbances like catalyst poisoning and heat-exchanger fouling. To guarantee a flexible operation despite such upsets, traditionally, the procedure was either to oversize the equipment or to place large storage tanks between the processing units. Proposed design methods attempted to find optimal operating regimes for chemical plants while compensating for process uncertainty through empirical overdesign factors.

Studies concerned with the interplay between the process design and operation aspects have appeared recently [1, 2] and focused on achieving better controllability upon modifying the plant design, without explicitly considering process uncertainty. Nevertheless, maintaining satisfactory dynamic operability in an environment of uncertainty remained as a pressing issue and the need was raised quite frequently for a rigorous treatment of the topic [3].

The development of new analytical tools [4, 5] made it possible to consider dynamic operability at the process design stage and modify the plant design accordingly. In this paper, a methodology is presented, that systematically guides the designer towards process designs with better dynamic operability and economics, The problem is formulated within a multiobjective optimization framework and makes extensive use of singular-value decomposition and nonlinear semi-infinite programming techniques.

Conclusions and Significance—A multiobjective optimization problem is proposed for designing chemical processes with better dynamic operability characteristics. Robustness indices are used as the indicators of dynamic operability and placed as constraints within the optimization scheme. A semi-infinite nonlinear programming problem results due to the frequency-dependent nature of such constraints. A discretization procedure is suggested to handle the infinite number of constraints and an ellipsoid algorithm allows an interactive solution of the process design problem. A process consisting of three CSTRs is treated as an example, illustrating the potential of the methodology in solving design-related operability problems.     

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.     

基于多目标的模糊神经网络及在pH控制过程中的应用

设计了一种基于多目标的动态模糊递归神经网络（FRNN）建模方法,用于pH中和过程的广义预测控制。所设计的多目标优化算法以提高拟合精度和简化网络结构为原则,同时优化模糊神经网络中的模糊规则数、隶属度函数中心点及其宽度,由此得到的FRNN模型可以高精度拟合pH中和过程。依据该动态模型,在控制过程的每一个控制周期得到其局部线性模型,将广义预测控制中复杂的非线性优化问题转化为简单的二次线性规划问题。仿真对比结果验证了所提方法的有效性。     

THE MINIMUM OF TOTAL AVAILABLE ENERGY (ENERGY) DISSIPATION IN THE MULTISTAGE FLUIDIZED WET BEDS

This article represents a second half of the work in optimization of the fluidized drying and moistening processes by the method of dynamic programming. The problem considered in Part I concerns the case of fluidized drying and moistening in the continuous processes for the variable inlet gas temperature. In this work, the generalized aspect of the continuous and multistage adiabatic processes for the case when the decision variables on the stage are gas enthalpy, gas humidity and dry gas flow rate, is considered. The continuous processes are considered here only as a limiting case of the multistage ones.

The two types of the thermodynamic performance indexes based on the idea of energy as a thermodynamic measure of the substance value are considered. The first type is related to the economic costs of production and the second to the overall economic costs. The equivalency of the optimization results for either of the two types of energy costs is discussed- Also, the nature of the optimal trajectories and decisions is considered.