MODELING AND OPTIMIZATION OF LACTIC ACID SYNTHESIS BY THE ALKALINE DEGRADATION OF FRUCTOSE IN A BATCH REACTOR

The present work deals with the determination of the optimal operating conditions of lactic acid synthesis by the alkaline degradation of fructose. It is a complex transformation for which detailed knowledge is not available. It is carried out in a batch or semi-batch reactor. The “Tendency Modeling” approach, which consists of the development of an approximate stoichiometric and kinetic model, has been used. An experimental planning method has been utilized as the database for model development. The application of the experimental planning methodology allows comparison between the experimental and model response. The model is then used in an optimization procedure to compute the optimal process. The optimal control problem is converted into a nonlinear programming problem solved using the sequencial quadratic programming procedure coupled with the golden search method. The strategy developed allows simultaneously optimizing the different variables, which may be constrained. The validity of the methodology is illustrated by the determination of the optimal operating conditions of lactic acid production.     

MODELING AND OPTIMIZATION OF LACTIC ACID SYNTHESIS BY THE ALKALINE DEGRADATION OF FRUCTOSE IN A BATCH REACTOR

The present work deals with the determination of the optimal operating conditions of lactic acid synthesis by the alkaline degradation of fructose. It is a complex transformation for which detailed knowledge is not available. It is carried out in a batch or semi-batch reactor. The “Tendency Modeling” approach, which consists of the development of an approximate stoichiometric and kinetic model, has been used. An experimental planning method has been utilized as the database for model development. The application of the experimental planning methodology allows comparison between the experimental and model response. The model is then used in an optimization procedure to compute the optimal process. The optimal control problem is converted into a nonlinear programming problem solved using the sequencial quadratic programming procedure coupled with the golden search method. The strategy developed allows simultaneously optimizing the different variables, which may be constrained. The validity of the methodology is illustrated by the determination of the optimal operating conditions of lactic acid production.     

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 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.     

Decision support for the development,simulation and optimization of dynamic process models

Simulation is besides experimentation the major method for designing,analyzing and optimizing chemical processes.The ability of simulations to reflect real process behavior strongly depends on model quality.Validation and adaption of process models are usually based on available plant data.Using such a model in various simulation and optimization studies can support the process designer in his task.Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation.In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

Hierarchical multiscale model-based design of experiments, catalysts, and reactors for fuel processing

In this paper a hierarchical multiscale simulation framework is outlined and experimental data injection into this framework is discussed. Specifically, we discuss multiscale model-based design of experiments to optimize the chemical information content of a detailed reaction mechanism in order to improve the fidelity and accuracy of reaction models. Extension of this framework to product (catalyst) design is briefly touched upon. Furthermore, we illustrate the use of such detailed and reduced kinetic models in reactor optimization as an example toward more conventional process design. It is proposed that hierarchical multiscale modeling offers a systematic framework for identification of the important scale(s) and model(s) where one should focus research efforts on. The ammonia decomposition on ruthenium to produce hydrogen and the water–gas shift reactions on platinum for converting syngas to hydrogen serve as illustrative fuel processing examples of various topics. The former is used to illustrate hierarchical multiscale model development and model-based parameter estimation as well as product engineering. The latter is employed to demonstrate model reduction and process optimization. Finally, opportunities for process design and control in portable microchemical devices (lab-on-a chip) for power generation are discussed.     

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.     

基于变分贝叶斯算法的线性变参数系统辨识

线性变参数系统（LPV）将多阶段、非线性的过程建模转化为线性多模型的辨识问题,是解决非线性过程建模的一个有效手段。由于实际工业过程存在各种干扰因素,导致被建模系统呈现随机性及模型参数的不确定性。针对这一问题,考虑采用变分贝叶斯（VB）算法对LPV模型进行辨识。该算法首先给定参数相应的先验分布,通过最大化目标函数的下界,从而估计得到参数的后验分布。不仅可实现对参数的点估计,同时量化了估计值的不确定性。针对典型二阶过程和连续搅拌反应釜（CSTR）,运用提出的算法进行仿真实验,表明了该贝叶斯估计方法的优越性。     

Two state estimators for the barium sulfate precipitation in a semi-batch reactor

The on-line determination of particle property distributions by direct measurements is often difficult, because the measurement equations are not invertible or because the inverse problem is ill-posed. If the process is observable, one can use state estimation techniques in order to reconstruct unmeasurable internal states of the process. This is discussed here for a semi-batch precipitation reactor. A square root unscented Kalman filter and state estimation by online minimisation are studied for the case of a measurable average particle size. Both estimators use a one-dimensional population balance model. The two approaches are compared in simulations.     

Optimization of grade transitions in polyethylene solution polymerization process under uncertainty

Model-based dynamic optimization is an effective tool for control and optimization of chemical processes, especially during transitions in operation. This study considers the dynamic optimization of grade transitions for a solution polymerization process. Here, a detailed dynamic model comprises the entire flowsheet and includes a method-of-moments reactor model to determine product properties, a simple yet accurate vapor–liquid equilibrium (VLE) model derived from rigorous calculations, and a variable time delay model for recycle streams. To solve the grade transition problem, both single stage and multistage optimization formulations have been developed to deal with specification bands of product properties.This dynamic optimization framework demonstrates significant performance improvements for grade transition problems. However, performance can deteriorate in the presence of uncertainties, disturbances and model mismatch. To deal with these uncertainties, this study applies robust optimization formulations through the incorporation of back-off constraints within the optimization problem. With back-off terms calculated from Monte Carlo simulations, the resulting robust optimization formulation can be solved with the same effort as the nominal dynamic optimization problem, and the resulting solution is shown to be robust under various uncertainty levels with minimal performance loss. Additional case studies show that our optimization approach extends naturally to different regularizations and multiple sources of uncertainty.     

发酵过程生物量软测量技术的研究进展

生物量是发酵过程中的关键过程参数之一,它直接影响着发酵过程的优化和控制。综述了近年来发酵过程生物量软测量技术的研究现状,讨论了基于过程机理分析、回归分析、状态估计和神经网络等的软测量建模方法,对基于神经网络和改进的神经网络建模方法进行了分析。指出基于多尺度建立软测量混合模型,是实现发酵过程生物量在线测量的有效方法,并给出了建立混合模型需要解决的关键问题。     

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Multiscale models have been developed to simulate the behavior of spatially‐heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially‐dependent. While such a model provides an adequate representation of the catalytic reactor, model‐plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE‐based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2374–2390, 2016     

Pattern formation in models of fixed-bed reactors

This work reviews and compares spatiotemporal patterns in three models of adiabatic fixed catalytic beds for reactions with oscillatory kinetics: homogeneous and heterogeneous models, which are studied using generic first-order kinetics, and a detailed model of CO oxidation in the monolithic reactor. These three models describe reactors with one, two or all three phases (fluid-, solid- and adsorbed-phases), respectively. Pattern selection is based on the oscillatory or bistable nature of the kinetics and on the nature of fronts. The heterogenous and detailed models may exhibit local bistability while the homogeneous model does not admit this property: a simple conversion between the parameters of the homogeneous and heterogeneous models is suggested.

The spatiotemporal patterns in the reactor can be predicted from the sequence of phase planes spanned by the reactor. Stationary or oscillatory front solutions, oscillatory states that sweep the whole surface or excitation fronts may be realized in the homogeneous and heterogeneous models. The detailed model of the converter may exhibit oscillatory motion, which may be periodic or chaotic, in which typically a hot domain enters the reactor exit and moves quickly upstream; the following extinction occurs almost simultaneously due to strong coupling by convection.     

乙炔加氢反应过程混合建模与优化

针对传统单一建模方法所构建的乙炔加氢反应器数学模型存在预测性能无法满足工业实际应用需求的问题,提出了一种机理与神经网络嵌套的建模方法,充分利用机理模型包含的能质约束信息降低神经网络模型的约束违反度,得到了能够良好描述实际工业乙炔加氢反应过程特性的混合模型。基于反应器混合模型,研究了以运行效益为目标函数的优化问题。主要决策变量包括：一段反应器进料中氢气与乙炔的摩尔比（R _H/A）、进料温度和反应器运行周期等几个关键参数。针对反应器长期运行后,催化剂活性降低造成的处理能力下降的问题,提出了反应温度补偿机制和R _H/A并行调节的运行优化策略,并采用序列法对反应器运行周期进行离散化处理。通过引入差异化变异策略、潜在解替代策略对两阶段差分算法进行改进,采用增量式编码法结合改进两阶段差分算法,对优化问题进行求解。结果证实了优化策略与改进算法的有效性,并据此确定了反应器最佳运行方案。     

间歇过程实时优化

由于生产过程中参数的不确定性和各种扰动作用,间歇生产过程的最优操作条件需要随时作出调整以保证生产的正常进行和满足生产产品质量的要求.针对间歇生产过程的操作条件的变化和不确定性因素的影响提出一种应用于间歇生产过程中的实时优化策略,其主要构成步骤包括动态模型建立、模型降阶、动态优化、在线监测、模型更新和在线调整,并以一个典...     

Particle nucleation rates in continuous emulsion polymerisation reactors

The design of a continuous, stirred emulsion polymerisation reactor requires a detailed characterisation of the particle nucleation rate. Here, consideration is given to systems which use micelle-forming surfactants and water-insoluble monomers. The surfactant micelles will be considered to participate in two competing rate processes; the nucleation of polymer particles and dissolution into the aqueous phase. When the reactor operates in a steady state it is possible to obtain a size distribution function for the particles. This distribution function is used in the development of a number of diffusion equations and conservation equations. By making various sets of assumptions concerning the nucleation process, these equations are then used to obtain expressions for the particle number. By comparing the different models with experimental results it can be seen that satisfactory predictions for the particle number can be obtained without assuming that the polymer particles are always saturated with surfactant. The models also show that the radical absorption processes are not controlled by diffusion in the aqueous phase. In some cases the particle number does not reach a steady state but oscillates with time. The nature of these oscillations is described by the solution of nonlinear differential equations. Boundary conditions for these equations depend on the reaction conditions.     

Dynamic modeling of an industrial diesel hydroprocessing plant by the method of continuous lumping

Diesel hydroprocessing is an important refinery process which consists of hydrodesulfurization to remove the undesired sulfur from the oil feedstock followed by hydrocracking and fractionation to obtain diesel with desired properties. Due to the new emission standards to improve the air quality, there is an increasing demand for the production of ultra low sulfur diesel fuel. This paper is addressing the development of a reliable dynamic process model which can be used for real-time optimization and control purposes to improve the process conditions of existing plants to meet the low-sulfur demand. The overall plant model consists of a hydrodesulfurization (HDS) model for the first two reactor beds followed by a hydrocracking (HC) model for the last cracking bed. The models are dynamic, non-isothermal, pseudo-homogeneous plug flow reactor models. Reaction kinetics are modeled using the method of continuous lumping which treats the reaction medium as a continuum of species whose reactivities depend on the true boiling point of the mixture. The key modeling parameters are estimated using industrial data. Steady-state and dynamic model predictions of the reactor bed temperatures, sulfur removal, and diesel production match closely the plant data.     

UNCERTAINTY AT BOTH DESIGN AND OPERATION STAGES

Process uncertainty is almost always an issue during the design of chemical processes (CP). In the open literature it has been shown that consideration of process uncertainties in optimal design necessitates the incorporation of process flexibility. Such an optimal design can presumably operate reliably in the presence of process and modeling uncertainty. Halemane and Grossmann (1983) introduced a feasibility function for evaluating CP flexibility. They also formulated a two-stage optimization problem for estimating the optimal design margins. These formulations, however, are based implicitly on the assumption that during the operation stage, uncertain parameters can be determined with enough precision. This assumption is rather restrictive and is often not met in practice. When available experimental information at the operation stage does not allow a more precise estimate of some of the uncertain parameters, new formulations of the flexibility condition and the optimization problem under uncertainty are needed. In this article, we propose such formulations, followed by some computational experiments.