L亮氨酸发酵过程的模拟优化
Optimisation of the Start-Up Process
gPROMS实验设计功能的应用研究
gPROMS在化工中的应用
Dynamic Process Modeling Combining Models and Experimental Data to Solve Industrial Problems
A HYBRID CFD COMPARTMENTALIZATION MODELING FRAMEWORK FOR THE SCALEUP OF BATCH COOLING CRYSTALLIZATION PROCESSES
Dimensional Analysis and Inference for Gproms
An integrated modelling framework for the design, operation and control of marine energy systems |
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An integrated modelling framework for the design, operation and control of marine energy systems.pdf
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海洋能源系统的集成建模框架的设计、操控和控制 Rapidly varying fuel costs, environmental
concerns and forthcoming emissions regulations
impose a pressure on ships to operate in a more
efficient, cost-effective and environmentally friendly
way. The propulsion power and energy producing onboard
installation– i.e. the marine energy system – is
the main contributor to the overall cost-effectiveness,
emissions footprint and efficiency of the vessel. To
meet those stringent and often contradicting requirements,
the sophistication and, hence, complexity of
modern marine energy systems increases, while operating
frequently at extreme conditions and close to
the design limit. The challenge of making both existing
and new marine energy systems more energy
efficient and environmentally friendly imposes a need
for new approaches for system configuration, design,
operation and control that are able to consider the energy
production and conversion onboard ships (fuel,
mechanical, electrical, thermal) in an integrated manner.
At the same time, simultaneous assessment of
performance, safety, and reliability of marine systems,
especially under real service conditions and transient
operation modes are becoming increasingly important
for both ship-owners and classification societies.
To date, however, there is no formal methodological
framework to cover the aforementioned needs in
a holistic way. In this paper we present a novel approach
for integrated dynamic process modelling and
simulation of marine energy systems. Our methodology
is based on the mathematical modelling of the
dynamic thermofluid behaviour of components including
energy conversion and rotating machinery such
as heat exchangers, evaporators, compressors, turbochargers,
pumps, valves, pipes, etc. The component
process models are generic, reconfigurable, suitable
for different types of studies and valid for a wide
range of operating conditions. Then, following a hierarchical
decomposition approach the lower-level component
models are used to synthesise higher level subsystems
and, in turn, complete energy systems. Experimental
or service data are used for model verification
and validation. The models are implemented
in state of the art process modelling tools, where they
are coupled with representations of operational scenarios/
profiles. In that manner we are able to perform
a variety of model-based studies and applications like
steady-state and dynamic simulation, design, optimisation
and control of user-defined energy system configurations
under realistic service conditions.
The developed modelling framework aims at providing
model-based decision support on: a) energy
and emissions optimal design of onboard machinery,
b) performance evaluation under real-service dynamic
conditions for the whole mission envelope of
the system, and c) assessment of the potential and
operational capabilities of innovative designs. The
main benefit from this holistic approach is that the
steady-state design characteristics, off-design operational
modes and dynamic/transient behaviour can be
simultaneously assessed and/or optimised in a unified
and consistent modelling framework. The presented
approach can significantly aid the design process for
new systems as well as the energy management, performance
prognosis, and control optimisation and reconfiguration
for existing vessels. The main characteristics
and benefits of our methodology are illustrated
via the dynamic modelling of a marine combined cycle
system.
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A HYBRID CFD COMPARTMENTALIZATION MODELING FRAMEWORK FOR THE SCALEUP OF BATCH COOLING CRYSTALLIZATION PROCESSES.pdf
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混合CFD区分建模领域按比例放大的间歇冷却结晶过程 This article may be used for research, teaching, and private study purposes. Any
substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,
systematic supply, or distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation
that the contents will be complete or accurate or up to date. The accuracy of any
instructions, formulae, and drug doses should be independently verified with primary
sources. The publisher shall not be liable for any loss, actions, claims, proceedings,
demand, or costs or damages whatsoever or howsoever caused arising directly or
indirectly in connection with or arising out of the use of this material.
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Dimensional Analysis and Inference for Gproms.pdf
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Gproms量纲分析和推理 In the thesis we describe a dimensional analysis and inference system for a strongly typed language for simulation of physical systems, gPROMS. We show how gPROMS can benefit from such a system and we believe that it will increase the physical correctness of the simulation models.
Our proposed dimensional inference system is based on generating dimensional constraint equations from the model equations using formal inference rules. The resulting constraints constitute a linear system of equations which is solves by means of linear algebra. If the system of equations is solvable the simulation model is proven to be dimensionally consistent.
We have implemented our dimensional inference algorithms in Java in the form of a stand-alone tool. The tool will be used by the industry to quickly locate physical errors in simulation models resulting from dimensionally inconsistent equations.
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Dynamic Process Modeling Combining Models and Experimental Data to Solve Industrial Problems.pdf
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动态过程建模：结合模型和实验数据来解决工业问题
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gPROMS在化工中的应用.pdf
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gPRoMS V3．0是帝国理工学院(Imperial College London)PSE(Process
System Enterpri se Ltd．)研究中心2006年推出的最新研究成果，具有卓越的化
工过程建模、求解与优化功能。
论文通过3个典型化工过程研究了gPRoMS的模拟、参数估计、优化及与外
部软件EXCEL的连接功能，通过1个较复杂过程，研究了gPROMS模拟复杂过程
的能力。即(I)以邻二甲苯氧化生产苯酐过程研究gPROMS的模拟功能； (2)
以偏三甲苯液相空气氧化过程研究gPROMS的参数估计及其与ExcEL的连接功能；
(3)以青霉素半连续发酵过程研究gPRoMS的优化功能；(4)以甲醇一水双塔精
馏过程研究gPRoMS模拟复杂过程的能力。
建立了邻二甲苯氧化生产苯酐过程二维动态模型方程，利用gPROMS求解，
通过模拟结果及进料口温度和冷却剂温度对反应的影响分析，表明gPRoMS能很
好模拟邻二甲苯氧化生产苯酐过程。
利用gPROMS估计偏三甲苯液相空气氧化过程动力学参数，估计结果同文献
计算值相比，更与试验数据吻合；完成外部数据库ExCEL文件，通过gPROMS的
连接，实现偏三甲苯液相空气间歇、连续氧化过程数据的在线导入导出。
在青霉素发酵过程非结构模型的基础上，在满足比生长率、基质浓度及体积
约束的条件下，以青霉素浓度最大化为目标建立优化函数。用gPROMS求解、寻
优。通过调节初始浓度和改变加料方法，获得最优结果是原始结果的五倍多，表
明gPRoMS具有强大的优化功能。
以新型甲醇精馏过程一主塔为高压塔和常压塔双塔精馏为例，在gPRoMS中构
造模型，逐级分解模块。在给定的分离条件下，通过进料流率、回流比对塔顶产
物浓度的影响，进料板处温度及塔内温度的变化，证实模拟结果符合理论分析与
实际操作过程，表明gPROMS能方便地模拟复杂精馏过程。
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gPROMS实验设计功能的应用研究.pdf
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Based on the oxidation process for liquid 1，2，4-trimethylbenzene witll air，the gPROMS experiment de—
sign is studied．The method and steps of gPROMS experiment design are presented through designing experiment．estimating
parameters and checking model and parameters．
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L亮氨酸发酵过程的模拟优化.pdf
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以L-亮氨酸发酵过程中菌种~基质~产物关系的动力学模型为基础 应用gPROMS软件 采用增广Lagrange
乘子法对该发酵过程进行了模拟优化 提出了使用T@9806菌株进行发酵的优化实验方案 使产物浓度由22.734g~
L-1提高到31.024g~L-1 比已有的优化结果提高了4.154g~L-1 大幅提高了实验效率。
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Optimisation of the Start-Up Process.pdf
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多段蒸发系统启动时的工艺优化 Evaporation processes are of great importance in the chemical industry.
They are commonly used to concentrate a solution containing the desired
product. In most cases the solvent is either water or an alcoholic solvent,
and heat is supplied by condensing steam. The dynamics of an evaporator
is described by differential algebraic equations, where differential equations
correspond to the mass and energy conservation laws. A challenging feature
of evaporation processes is their hybrid behaviour. Due to phase transitions,
the dynamics of the evaporator drastically changes exhibiting both structural
transformations and state jumps. Hence, an evaporation system should
be modelled and analysed in the framework of hybrid control systems.
The proposed topic deals with the problem of start-up of a multi-stage
evaporator. Obviously, this problem is rather complicated because of the
system complexity. Thus, the design procedure will include not only controltheoretical,
but also numerical analysis of the system under consideration1.
Moreover, understanding of a physical nature of the underlying processes is
extremely helpful.
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共 3 个关于本帖的回复 最后回复于 2014-9-18 17:03