Dynamic simulation of chemical processes

This thesis describes the design and implementation of an interactive dynamic simulator called DASPRII. The starting point of this research has been an existing dynamic simulation package, DASP. DASPII is written in standard FORTRAN 77 and is implemented on universally available IBM-PC or compatible machines. It provides a means for the analysis and design of chemical processes. Industrial interest in dynamic simulation has increased due to the recent increase in concern over plant operability, resiliency and safety. DASPII is an equation oriented simulation package which allows solution of dynamic and steady state equations. The steady state can be used to initialise the dynamic simulation. A robust non linear algebraic equation solver has been implemented for steady state solution. This has increased the general robustness of DASPII, compared to DASP. A graphical front end is used to generate the process flowsheet topology from a user constructed diagram of the process. A conversational interface is used to interrogate the user with the aid of a database, to complete the topological information. An original modelling strategy implemented in DASPII provides a simple mechanism for parameter switching which creates a more flexible simulation environment. The problem description generated is by a further conversational procedure using a data-base. The model format used allows the same model equations to be used for dynamic and steady state solution. All the useful features of DASPI are retained in DASPII. The program has been demonstrated and verified using a number of example problems, Significant improvements using the new NLAE solver have been shown. Topics requiring further research are described. The benefits of variable switching in models has been demonstrated with a literature problem.

A study of the dynamic behaviour of a pulsed column solvent extraction plant

A study has been made of the dynamic behaviour of a nuclear fuel reprocessing plant utilising pulsed solvent extraction columns. A flowsheet is presented and the choice of an extraction device is discussed. The plant is described by a series of modules each module representing an item of equipment. Each module consists of a series of differential equations describing the dynamic behaviour of the equipment. The model is written in PMSP, a language developed for dynamic simulation models. The differential equations are solved to predict plant behaviour with time. The dynamic response of the plant to a range of disturbances has been assessed. The interactions between pulsed columns have been demonstrated and illustrated. The importance of auxillary items of equipment to plant performance is demonstrated. Control of the reprocessing plant is considered and the effect of control parameters on performance assessed.

Hidden state models for time series

Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state inference or parameter estimation are reviewed in detail. A key part of multivariate time series analysis is identifying the delay between different variables. We present a novel approach for time delay estimating in a non-stationary environment. The technique makes use of hidden Markov models and we demonstrate its application for estimating a crucial parameter in the oil industry. We then focus on hybrid models that we call dynamical local models. These models combine and generalise hidden Markov models and state space models. Probabilistic inference is unfortunately computationally intractable and we show how to make use of variational techniques for approximating the posterior distribution over the hidden state variables. Experimental simulations on synthetic and real-world data demonstrate the application of dynamical local models for segmenting a time series into regimes and providing predictive distributions.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field.

Flexible high speed machinery

The thesis describes an investigation into methods for the design of flexible high-speed product processing machinery, consisting of independent electromechanically actuated machine functions which operate under software coordination and control. An analysis is made of the elements of traditionally designed cam-actuated, mechanically coupled machinery, so that the operational functions and principal performance limitations of the separate machine elements may be identified. These are then used to define the requirements for independent actuators machinery, with a discussion of how this type of design approach is more suited to modern manufacturing trends. A distributed machine controller topology is developed which is a hybrid of hierarchical and pipeline control. An analysis is made, with the aid of dynamic simulation modelling, which confirms the suitability of the controller for flexible machinery control. The simulations include complex models of multiple independent actuators systems, which enable product flow and failure analyses to be performed. An analysis is made of high performance brushless d.c. servomotors and their suitability for actuating machine motions is assessed. Procedures are developed for the selection of brushless servomotors for intermittent machine motions. An experimental rig is described which has enabled the actuation and control methods developed to be implemented. With reference to this, an evaluation is made of the suitability of the machine design method and a discussion is given of the developments which are necessary for operational independent actuators machinery to be attained.

The application of computerised modelling techniques in manufacturing system design

The absence of a definitive approach to the design of manufacturing systems signifies the importance of a control mechanism to ensure the timely application of relevant design techniques. To provide effective control, design development needs to be continually assessed in relation to the required system performance, which can only be achieved analytically through computer simulation. The technique providing the only method of accurately replicating the highly complex and dynamic interrelationships inherent within manufacturing facilities and realistically predicting system behaviour. Owing to the unique capabilities of computer simulation, its application should support and encourage a thorough investigation of all alternative designs. Allowing attention to focus specifically on critical design areas and enabling continuous assessment of system evolution. To achieve this system analysis needs to efficient, in terms of data requirements and both speed and accuracy of evaluation. To provide an effective control mechanism a hierarchical or multi-level modelling procedure has therefore been developed, specifying the appropriate degree of evaluation support necessary at each phase of design. An underlying assumption of the proposal being that evaluation is quick, easy and allows models to expand in line with design developments. However, current approaches to computer simulation are totally inappropriate to support the hierarchical evaluation. Implementation of computer simulation through traditional approaches is typically characterized by a requirement for very specialist expertise, a lengthy model development phase, and a correspondingly high expenditure. Resulting in very little and rather inappropriate use of the technique. Simulation, when used, is generally only applied to check or verify a final design proposal. Rarely is the full potential of computer simulation utilized to aid, support or complement the manufacturing system design procedure. To implement the proposed modelling procedure therefore the concept of a generic simulator was adopted, as such systems require no specialist expertise, instead facilitating quick and easy model creation, execution and modification, through simple data inputs. Previously generic simulators have tended to be too restricted, lacking the necessary flexibility to be generally applicable to manufacturing systems. Development of the ATOMS manufacturing simulator, however, has proven that such systems can be relevant to a wide range of applications, besides verifying the benefits of multi-level modelling.

Some aspects of the dynamic performance of machine tool structural joints

The work presented in this thesis is concerned with the dynamic behaviour of structural joints which are both loaded, and excited, normal to the joint interface. Since the forces on joints are transmitted through their interface, the surface texture of joints was carefully examined. A computerised surface measuring system was developed and computer programs were written. Surface flatness was functionally defined, measured and quantised into a form suitable for the theoretical calculation of the joint stiffness. Dynamic stiffness and damping were measured at various preloads for a range of joints with different surface textures. Dry clean and lubricated joints were tested and the results indicated an increase in damping for the lubricated joints of between 30 to 100 times. A theoretical model for the computation of the stiffness of dry clean joints was built. The model is based on the theory that the elastic recovery of joints is due to the recovery of the material behind the loaded asperities. It takes into account, in a quantitative manner, the flatness deviations present on the surfaces of the joint. The theoretical results were found to be in good agreement with those measured experimentally. It was also found that theoretical assessment of the joint stiffness could be carried out using a different model based on the recovery of loaded asperities into a spherical form. Stepwise procedures are given in order to design a joint having a particular stiffness. A theoretical model for the loss factor of dry clean joints was built. The theoretical results are in reasonable agreement with those experimentally measured. The theoretical models for the stiffness and loss factor were employed to evaluate the second natural frequency of the test rig. The results are in good agreement with the experimentally measured natural frequencies.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

A dynamic model for the activated sludge treatment of coke oven effluents

This is an Inter-Disciplinary Higher Degree (IHD) thesis about Water Pollution Control in the Iron and Steel Industry. After examining the compositions, and various treatment methods, for the major effluent streams from a typical Integrated Iron and Steel works, it was decided to concentrate investigative work on the activated-sludge treatment of coke-oven effluents. A mathematical model of this process was developed in an attempt to provide a tool for plant management that would enable improved performance, and enhanced control of Works Units. The model differs from conventional models in that allowance is made for the presence of two genera of microorganisms, each of which utilises a particular type of substrate as its energy source. Allowance is also made for the inhibitive effect of phenol on thiocyanate biodegradation, and for the self-toxicity of the bacteria when present in a high substrate concentration environment. The enumeration of the kinetic characteristics of the two groups of micro-organisms was shown to be of major importance. Laboratory experiments were instigated in an attempt to determine accurate values of these coefficients. The use of the Suspended Solids concentration was found to be too insensitive a measure of viable active mass. Other measures were investigated, and Adenosine Triphosphate concentration was chosen as the most effective measure of bacterial populations. Using this measure, a model was developed for phenol biodegradation from experimental results which implicated the possibility of storage of substate prior to metabolism. A model for thiocyanate biodegradation was also developed, although the experimental results indicate that much work is still required in this area.

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.

The role of models@run.time in supporting on-the-fly interoperability

Models at runtime can be defined as abstract representations of a system, including its structure and behaviour, which exist in tandem with the given system during the actual execution time of that system. Furthermore, these models should be causally connected to the system being modelled, offering a reflective capability. Significant advances have been made in recent years in applying this concept, most notably in adaptive systems. In this paper we argue that a similar approach can also be used to support the dynamic generation of software artefacts at execution time. An important area where this is relevant is the generation of software mediators to tackle the crucial problem of interoperability in distributed systems. We refer to this approach as emergent middleware, representing a fundamentally new approach to resolving interoperability problems in the complex distributed systems of today. In this context, the runtime models are used to capture meta-information about the underlying networked systems that need to interoperate, including their interfaces and additional knowledge about their associated behaviour. This is supplemented by ontological information to enable semantic reasoning. This paper focuses on this novel use of models at runtime, examining in detail the nature of such runtime models coupled with consideration of the supportive algorithms and tools that extract this knowledge and use it to synthesise the appropriate emergent middleware.

Modeling and validating dynamic adaptation

This paper discusses preliminary work on modeling and validation dynamic adaptation. The proposed approach is on the use of aspect-oriented modeling (AOM) and models at runtime. Our approach covers design and runtime phases. At design-time, a base model and different variant architecture models are designed and the adaptation model is built. Crucially, the adaptation model includes invariant properties and constraints that allow the validation of the adaptation rules before execution. During runtime, the adaptation model is processed to produce a correct system configuration that can be executed.

An aspect-oriented and model-driven approach for managing dynamic variability

Constructing and executing distributed systems that can adapt to their operating context in order to sustain provided services and the service qualities are complex tasks. Managing adaptation of multiple, interacting services is particularly difficult since these services tend to be distributed across the system, interdependent and sometimes tangled with other services. Furthermore, the exponential growth of the number of potential system configurations derived from the variabilities of each service need to be handled. Current practices of writing low-level reconfiguration scripts as part of the system code to handle run time adaptation are both error prone and time consuming and make adaptive systems difficult to validate and evolve. In this paper, we propose to combine model driven and aspect oriented techniques to better cope with the complexities of adaptive systems construction and execution, and to handle the problem of exponential growth of the number of possible configurations. Combining these techniques allows us to use high level domain abstractions, simplify the representation of variants and limit the problem pertaining to the combinatorial explosion of possible configurations. In our approach we also use models at runtime to generate the adaptation logic by comparing the current configuration of the system to a composed model representing the configuration we want to reach. © 2008 Springer-Verlag Berlin Heidelberg.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.