熔融碳酸盐燃料电池(MCFC)系统建模与控制的研究现状与发展

首先详细介绍MCFC的电极，单电池，电堆，系统四个层次的建模以及MCFC控制的研究现状，并指出现有模型的不足，然后讨论电堆和系统两级建模的发展方向；最后，分析MCFC系统的非线性，大时滞，分布参数，从输入多输出，有约束和随机干扰等特征，并根据这些特征，提出两种适宜的控制方法。     

稳态反应精馏过程的数学模型及算法研究进展

反应精馏将反应和分离操作耦合在相同塔中，近年来发展迅速，本文对稳态反应精馏过程模拟的平衡级模型、非平衡级模型和非平衡池模型及其求解算法进行了分析综述，为反应精馏过程的数学建模、模拟分析及优化设计提供了方法。     

硫黄湿法成型过程数值模拟与操作参数优化

目前对于液硫固化成型机理的认识并不是很完善，而且实验方法也很难观测到颗粒内部，冷却水流场分布等情况，为了解决这一问题，在实验研究的基础上采用计算流体力学（computational fluid dynamics,CFD）模拟方法对硫黄湿法造粒过程进行数值模拟，对冷却成型设备进行数理建模，对比分析了不同下落高度、冷却水温度以及冷却水进口流速下的硫黄颗粒粒径分布与形貌，通过调整以上参数，得到硫黄湿法造粒过程的最佳参数组合并深入分析了硫黄湿法冷却成型规律。在实验结果的基础上，进一步验证及完善了成型罐模型和模拟工艺参数。研究结果表明，随下落高度增大，细粉硫占比增多，但硫黄颗粒粘连情况减少，得到硫黄湿法造粒过程中最佳参数组合为下落高度为30mm、冷却水温度为55℃、冷却水进口流速为2.5m/s。     

以B2XX区块为例浅谈三维地质建模技术现状及提高建模精度的质量控制

由于地质过程的复杂性、建模方法的局限性以及建模资料的不完备性,造成了地质模型的不确定性。本文调研国内外技术发展历程及应用现状,又依据区块流程及具体研究过程总结了三维地质建模质量控制的关键点。实际应用表明可保证模型的质量,提高工作效率。     

微生物发酵过程优化控制技术进展

微生物发酵过程优化控制技术是发酵工程的重要技术。综述了近年来微生物发酵过程优化控制技术的研究现状,讨论了机理分析建模、黑箱建模、混合建模等发酵过程建模方法,对基于模型的优化控制策略进行了分析。指出了基于混合模型和多目标优化策略建立动态优化控制器,是微生物发酵过程优化控制的有效方法,并给出了实现优化控制需要解决的关键问题。     

油藏描述中的储集层混合建模技术

储集层地质模型是油藏描述的重要组成部分，储集层建模技术主要是解决储集层表征问题，使建立的储集层地质模型符合实际储集层特征。重点分析了目前储集层建模技术与方法，提出储集层模型是由构造模型和属性模型组成的二元模型，是由确定性模拟和随机模拟两种方法混合得到的一个确定模型。各种模拟方法以地质条件为约束并服务于解决特定的地质问题。储集层建模遵循的原则：混合建模原则、等时建模原则、地质条件约束建模原则、分级建模原则。     

高塔造粒生产复合肥技术

介绍目前比较典型的塔式造粒生产复合肥的技术,指出造粒塔塔高选择的基本原则及高塔造粒生产复合肥工艺控制中的一些基本方法:原料选择与配方原则,熔融混合温度及混合时间的控制原则,造粒喷头的选择等。     

部分料浆转鼓造粒与熔体塔式造粒的工艺比较

介绍了部分料浆转鼓造粒工艺和熔体塔式造粒工艺的成粒原理和生产流程,以及尿素熔融和尿液输送过程中控制缩二脲含量的方法。熔体塔式造粒装置投资较大,产品无需进行干燥,单位产品能耗较低;部分料浆转鼓造粒装置投资较小,产品需进行干燥,单位产品能耗较高。前者适合生产高氮复混肥,而后者适合生产w(N)≤20%的复混肥,2种工艺在产品规格上互为补充,企业应结合自身情况、资金状况及目标销售市场要求选择合适的工艺路线。     

美国涂布法尿素造粒工程开发

为进一步开发国内涂布法尿素造粒技术，本文着重介绍美国ＴＶＡ３３０ｔ／ｄ料帘涂布法尿素造粒的流程，设备，同时为进行计算机模拟而提出过程的物料平衡，热量平衡，水平衡及相应数据。     

流化床造粒工艺运行探讨

通过与塔式造粒工艺的比较，指出了流化床造粒工艺能耗高、产品同比价格无优势的现状，展望了流化床造粒工艺的发展方向。     

Identification of models for control of wet granulation

The modeling work in this paper provides insight on improved control and design (including measurement selection) of a granulation process. Two different control strategies (MPC and PID) are evaluated on an experimentally validated granulation model. This model is based on earlier work done at The University of Sheffield, UK and Organon, The Netherlands [C.F.W. Sanders, W. Oostra, A.D. Salman, M.J. Hounslow, Development of a predictive high-shear granulation model; experimental and modeling results, 7th World Congress of Chemical Engineering, Glasgow (2005), C11-002]. The granulation kinetics were measured in a 10 liter batch granulator with an experimental design that included four process variables. The aggregation rates were extracted with a Discretized Population Balance (DPB) model. Knowledge of the process kinetics was used to model a continuous (well mixed) granulator. The controller model for the Model Predictive Controller is a linearized state space model, derived from the nonlinear DPB model. It has the four process variables from the experimental design and a feed ratio as input variables. Since the DPB model describes the whole Granule Size Distribution (GSD), candidate sets of lumped output variables were evaluated. When measuring controller performance based on the full granule size distribution, it is shown that a PID controller can actually produce results that fluctuate more than the open-loop response. An MPC controller improves stability on both process outputs and the full granule size distribution. The work shows that measuring and controlling specific number based lumped outputs result in a more stable process than when mass based lumped outputs are used. The paper describes a general strategy of using lab scale batch experiments to design and control (small or large scale) continuous granulators. The continuous experiments in this paper are based on simulation, therefore future experimental validation will elucidate further the link between batch and continuous granulation.     

Model-based analysis of a twin-screw wet granulation system for continuous solid dosage manufacturing

Implementation of twin-screw granulation in a continuous from-powder-to-tablet manufacturing line requires process knowledge development. This is often pursued by application of mechanistic models incorporating the underlying mechanisms. In this study, granulation mechanisms considered to be dominant in the kneading element regions of the granulator i.e., aggregation and breakage, were included in a one-dimensional population balance model. The model was calibrated using the experimentally determined inflow granule size distribution, and the mean residence time was used as additional input to predict the outflow granule size distribution. After wetting, the first kneading block caused an increase in the aggregation rate which was reduced afterwards. The opposite was observed in case of the breakage rate. The successive kneading blocks lead to a granulation regime separation inside the granulator under certain process conditions. Such a physical separation between the granulation regimes is promising for future design and advanced control of the continuous granulation process.     

Model-based design and control of a continuous drum granulation process

This paper is concerned with enhanced process design and control of a multiple-input multiple-output (MIMO) granulation process. The work is based on a first-principles mechanistic three-dimensional population balance model (3D-PBM), which has been previously validated against experiments at the laboratory-scale for various operating conditions and formulations. The main objective of this study is via a novel process design, to control and operate the granulation process under more optimal conditions. Novelty of the work lies in the usage of the validated 3D-PBM to extract suitable multiple control-loop pairings from which an overall control loop performance is qualitatively and quantitatively assessed. Results show that for most existing granulation process configurations, enhanced control-loop performance is not achieved and as a result an alternative process design strategy is necessary. The proposed design demonstrates increased efficiency in the control and operation of the granulation process, which is required for further efficient control and operation of subsequent downstream processes.     

Determination of coalescence kernels for high-shear granulation using DEM simulations

Discrete element method (DEM) modeling is used in parallel with a model for coalescence of deformable surface wet granules. This produces a method capable of predicting both collision rates and coalescence efficiencies for use in derivation of an overall coalescence kernel. These coalescence kernels can then be used in computationally efficient meso-scale models such as population balance equation (PBE) models. A soft-sphere DEM model using periodic boundary conditions and a unique boxing scheme was utilized to simulate particle flow inside a high-shear mixer. Analysis of the simulation results provided collision frequency, aggregation frequency, kinetic energy, coalescence efficiency and compaction rates for the granulation process. This information can be used to bridge the gap in multi-scale modeling of granulation processes between the micro-scale DEM/coalescence modeling approach and a meso-scale PBE modeling approach.     

Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties.     

The kinetics of the granulation process: Right from the early stages

In this study, experimentation and modelling were carried out to understand the granulation process. This work assesses whether there is a significant difference in the aggregation rate of the wet granulation process between the very early stages and later stages of high shear granulation. Measurements of the size distribution and binder content from the beginning of the process, just after liquid binder addition, were carried out. A population balance model based on two different kernels, the Equi Kinetic Energy (EKE) kernel and two-dimensional population balance equations with a Size Independent (SI) kernel, was applied to the high shear granulation process. It was concluded that the population balance equations with SI kernel best described the aggregation in the high shear granulation process. The value of aggregation rate constant in the early stages is smaller than aggregation kernel in the later stages.     

Coupling granule properties and granulation rates in high-shear granulation

It is possible to link granulation rates to granule properties. The linkage is by multiple dimension population balance equations that, by means of simplifying assumptions, can be reduced to multiple one-dimensional (1-D) population balance equations (PBEs). Using simple physically based models, this paper demonstrates how multiple one-dimensional population balance equations can describe the results of high-shear granulation experiments of two different materials, calcium carbonate and lactose. Good agreement between experimental and simulated results was achieved enabling the granulation rates to be defined by two model parameters: the critical binder volume fraction and the aggregation rate constant. The modelling framework presented in this paper also provides a basis for the kinetic analysis of granulation experiments so that with further work, it is possible to determine the effect of process conditions and material properties on the model parameters.     

过程系统能量流结构模型及其应用

综述了过程系统能量综合优化的能量流结构研究与进展；重点介绍了过程系统三环节能量流结构模型，该模型基于对过程系统中能量演变共性规律的认识，定量地描述了过程系统能量流在转换、利用、回收过程中的平衡关系；概述了按照能量流结构进行过程能量综合优化策略和方法；指出了过程系统能量综合优化中能量流结构的进一步发展可以包容其他相关方面，如环境、安全等。     

催化重整集总动力学模型研究进展

催化重整集总动力学建模为重整工艺模拟和优化提供理论基础,模型为模拟重整反应过程,预测产品分布和产品性质提供计算工具。从催化重整集总动力学模型的集总划分、建模数据来源、参数估算以及重整流程模拟等方面,综述模型的发展历程和现状,并展望催化重整集总动力学模型的研究前景。重整模型越来越倾向于开发连续再生装置模型以及不断优化算法,更加的精细化、精确化,不仅能够降低生产成本,更能满足各种环保和生产要求。