A dynamic model with on-line identification for rotary sugar drying processes

A dynamic modelling methodology, which combines on-line variable estimation and parameter identification with physical laws to form an adaptive model for rotary sugar drying processes, is developed in this paper. In contrast to the conventional rate-based models using empirical transfer coefficients, the heat and mass transfer rates are estimated by using on-line measurements in the new model. Furthermore, a set of improved sectional solid transport equations with localized parameters is developed in this work to reidentified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.place the global correlation for the computation of solid retention time. Since a number of key model variables and parameters are identified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.

A generalised dynamic model for char particle gasification with structure evolution and peripheral fragmentation

A generalised model for the prediction of single char particle gasification dynamics, accounting for multi-component mass transfer with chemical reaction, heat transfer, as well as structure evolution and peripheral fragmentation is developed in this paper. Maxwell-Stefan analysis is uniquely applied to both micro and macropores within the framework of the dusty-gas model to account for the bidisperse nature of the char, which differs significantly from the conventional models that are based on a single pore type. The peripheral fragmentation and random-pore correlation incorporated into the model enable prediction of structure/reactivity relationships. The occurrence of chemical reaction within the boundary layer reported by Biggs and Agarwal (Chem. Eng. Sci. 52 (1997) 941) has been confirmed through an analysis of CO/CO2 product ratio obtained from model simulations. However, it is also quantitatively observed that the significance of boundary layer reaction reduces notably with the reduction of oxygen concentration in the flue gas, operational pressure and film thickness. Computations have also shown that in the presence of diffusional gradients peripheral fragmentation occurs in the early stages on the surface, after which conversion quickens significantly due to small particle size. Results of the early commencement of peripheral fragmentation at relatively low overall conversion obtained from a large number of simulations agree well with experimental observations reported by Feng and Bhatia (Energy & Fuels 14 (2000) 297). Comprehensive analysis of simulation results is carried out based on well accepted physical principles to rationalise model prediction. (C) 2001 Elsevier Science Ltd. AH rights reserved.

Extension of dynamic model of impulse behavior of concentrated grounds at high currents

A dynamic model which describes the impulse behavior of concentrated grounds at high currents is described in this paper. This model is an extension of previous models in that it can successfully account for the surge behavior of concentrated grounds over a much wider range of current densities. It is able to describe the well known effect of ionization of soil as well as the observed effect of discrete breakdowns and filamentary arc paths at much higher currents. Results of verification against experimental results are also presented.

A general model for host plant selection in phytophagous insects

We develop a general theoretical framework for exploring the host plant selection behaviour of herbivorous insects. This model can be used to address a number of questions, including the evolution of specialists, generalists, preference hierarchies, and learning. We use our model to: (i) demonstrate the consequences of the extent to which the reproductive success of a foraging female is limited by the rate at which they find host plants (host limitation) or the number of eggs they carry (egg limitation); (ii) emphasize the different consequences of variation in behaviour before and after landing on (locating) a host (termed pre- and post-alighting, respectively); (iii) show that, in contrast to previous predictions, learning can be favoured in post-alighting behaviour-in particular, individuals can be selected to concentrate oviposition on an abundant low-quality host, whilst ignoring a rare higher-quality host; (iv) emphasize the importance of interactions between mechanisms in favouring specialization or learning. (C) 2002 Elsevier Science Ltd.

Structured modeling of recombinant protein production in batch and fed-batch culture of baculovirus-infected insect cells

The infection of insect cells with baculovirus was described in a mathematical model as a part of the structured dynamic model describing whole animal cell metabolism. The model presented here is capable of simulating cell population dynamics, the concentrations of extracellular and intracellular viral components, and the heterologous product titers. The model describes the whole processes of viral infection and the effect of the infection on the host cell metabolism. Dynamic simulation of the model in batch and fed-batch mode gave good agreement between model predictions and experimental data. Optimum conditions for insect cell culture and viral infection in batch and fed-batch culture were studied using the model.

Kinetics of photoacclimation in corals

Traditional models describing the relationship between photosynthesis (P) and irradiance (I) do not account for photoacclimation to short-term variation in irradiance. Here we develop and test a model that predicts the rate of photosynthesis under fluctuating irradiances at the scale of days to weeks. Using oxygen respirometry, we measured the rates of change in the P-I model parameters P-max (maximum rate of gross photosynthesis) and I-k (sub-saturation irradiance) of the photo-symbiotic coral Turbinaria mesenterina (Lamarck) following large and small increases and decreases in growth irradiance. We analyse the behaviour of the dynamic P-I model in turbid-water conditions using a dataset of 3-month continuous irradiance as the input variable. In response to upward or downward changes in experimental growth irradiance, I-k values decreased or increased exponentially, reaching new and stable levels within 5-10 days. I-k responded 4 times stronger than P-max to changes in growth irradiance. The kinetics of I-k did not show hysteresis, and changed in similar ways when irradiance was increased or decreased in small or large amounts. This suggests that mechanisms associated with photo-protection during increases in irradiance, and the maximisation of photosynthetic efficiency during decreases in irradiance, are equally potent. On the scale of months, the dynamic P-I model did not predict higher rates of photosynthesis than the static P-I model, but buffered the variation in photosynthesis during periods of reduced irradiance. Fourier analysis indicated that the kinetics of I-k closely matches the main periodicities in daily irradiance (1-2 weeks). The recorded kinetics of photoacclimation in the Turbinaria-zooxanthella symbiosis is comparable to that of free-living phytoplankton and faster than that of higher plants.

Combined hydraulic and biological modelling and full-scale validation of SBR process

The biological reactions during the settling and decant periods of Sequencing Batch Reactors (SBRs) are generally ignored as they are not easily measured or described by modelling approaches. However, important processes are taking place, and in particular when the influent is fed into the bottom of the reactor at the same time (one of the main features of the UniFed process), the inclusion of these stages is crucial for accurate process predictions. Due to the vertical stratification of both liquid and solid components, a one-dimensional hydraulic model is combined with a modified ASM2d biological model to allow the prediction of settling velocity, sludge concentration, soluble components and biological processes during the non-mixed periods of the SBR. The model is calibrated on a full-scale UniFed SBR system with tracer breakthrough tests, depth profiles of particulate and soluble compounds and measurements of the key components during the mixed aerobic period. This model is then validated against results from an independent experimental period with considerably different operating parameters. In both cases, the model is able to accurately predict the stratification and most of the biological reactions occurring in the sludge blanket and the supernatant during the non-mixed periods. Together with a correct description of the mixed aerobic period, a good prediction of the overall SBR performance can be achieved.

A generalized Markov sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.

Diagnostic goal driven modelling and simulation of multiscale process systems

A systematic goal-driven top-down modelling methodology is proposed that is capable of developing a multiscale model of a process system for given diagnostic purposes. The diagnostic goal-set and the symptoms are extracted from HAZOP analysis results, where the possible actions to be performed in a fault situation are also described. The multiscale dynamic model is realized in the form of a hierarchical coloured Petri net by using a novel substitution place-transition pair. Multiscale simulation that focuses automatically on the fault areas is used to predict the effect of the proposed preventive actions. The notions and procedures are illustrated on some simple case studies including a heat exchanger network and a more complex wet granulation process.

The effect of service evaluations on behavioral intentions and quality of life

Understanding the contribution of marketing to economic and social outcomes is fundamental to broadening the focus of marketing. The authors develop a comprehensive model that integrates the impact of service quality and service satisfaction on both economic and societal outcomes. The model is validated using two random samples involving intensive health services. The results indicate that service quality and service satisfaction significantly enhance quality of life and behavioral intentions, highlighting that customer service has social as well as economic outcomes. This is an important finding given the movement toward recognizing social and environmental outcomes, such as emphasized through triple bottom-line reporting. The findings have important implications for managing service processes, for improving the quality of life of customers, and for enhancing customers' behavioral intentions toward the organization.

The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: A case study of koalas in Queensland, Australia

The loss and fragmentation of forest habitats by human land use are recognised as important factors influencing the decline of forest-dependent fauna. Mammal species that are dependent upon forest habitats are particularly sensitive to habitat loss and fragmentation because they have highly specific habitat requirements, and in many cases have limited ability to move through and utilise the land use matrix. We addressed this problem using a case study of the koala (Phascolarctos cinereus) surveyed in a fragmented rural-urban landscape in southeast Queensland, Australia. We applied a logistic modelling and hierarchical partitioning analysis to determine the importance of forest area and its configuration relative to site (local) and patch-level habitat variables. After taking into account spatial auto-correlation and the year of survey, we found koala occurrence increased with the area of all forest habitats, habitat patch size and the proportion of primary Eucalyptus tree species; and decreased with mean nearest neighbour distance between forest patches, the density of forest patches, and the density of sealed roads. The difference between the effect of habitat area and configuration was not as strong as theory predicts, with the configuration of remnant forest becoming increasingly important as the area of forest habitat declines. We conclude that the area of forest, its configuration across the landscape, as well as the land use matrix, are important determinants of koala occurrence, and that habitat configuration should not be overlooked in the conservation of forest-dependent mammals, such as the koala. We highlight the implications of these findings for koala conservation. (c) 2006 Elsevier Ltd. All rights reserved.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd