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. (c) 2005 Elsevier Ltd. All rights reserved.

Effect of free ammonia and free nitrous acid concentration on the anabolic and catabolic processes of an enriched Nitrosomonas culture

The effects of free ammonia (FA; NH3) and free nitrous acid (FNA; HNO2) concentrations on the metabolisms of an enriched ammonia oxidizing bacteria (AOB) culture were investigated using a method allowing the decoupling of growth and energy generation processes. A lab-scale sequencing batch reactor (SBR) was operated for the enrichment of an AOB culture. Fluorescent in-situ hybridization (FISH) analysis showed that 82% of the bacterial population in the SBR bound to the NEU probe specifically designed for Nitrosomonas europaea. Batch tests were carried out to measure the oxygen and ammonium consumption rates by the culture at various FA and FNA levels, in the presence or absence of inorganic carbon (CO2, HCO3, and CO32-). It was revealed that FA of up to 16.0 mgNH(3)-N (.) L-1, which was the highest concentration used in this study, did not have any inhibitory effect on either the catabolic or anabolic processes of the Nitrosomonas culture. In contrast, FNA inhibited both the growth and energy production capabilities of the Nitrosomonas culture. The inhibition on growth initiated at approximately 0.10 mgHNO(2)-(NL-1)-L-., and the data suggested that the biosynthesis was completely stopped at an FNA concentration of 0.40 mgHNO(2)-N (.) L-1. The inhibition on energy generation initiated at a slightly lower level but the Nitrosomonas culture was still oxidizing ammonia at half of the maximum rate at an FNA concentration of 0.50-0.63 mgHNO(2)-N (.) L-1. The affinity constant of the Nitrosomonas culture with respect to ammonia was determined to be 0.36 mgNH3-N (.) L-1, independent of the presence or absence of inorganic carbon. (c) 2006 Wiley Periodicals, Inc.

Extinction times for a birth-death process with two phases

Many populations have a negative impact on their habitat or upon other species in the environment if their numbers become too large. For this reason they are often subjected to some form of control. One common control regime is the reduction regime: when the population reaches a certain threshold it is controlled (for example culled) until it falls below a lower predefined level. The natural model for such a controlled population is a birth-death process with two phases, the phase determining which of two distinct sets of birth and death rates governs the process. We present formulae for the probability of extinction and the expected time to extinction, and discuss several applications. (c) 2006 Elsevier Inc. All rights reserved.

Significant patterns of population genetic structure and limited gene flow in a threatened macropodid marsupial despite continuous habitat in southeast Queensland, Australia

Many endangered species worldwide are found in remnant populations, often within fragmented landscapes. However, when possible, an understanding of the natural extent of population structure and dispersal behaviour of threatened species would assist in their conservation and management. The brush-tailed rock-wallaby (Petrogale penicillata), a once abundant and widespread rock-wallaby species across southeastern Australia, has become nearly extinct across much of the southern part of its range. However, the northern part of the species' range still sustains many small colonies closely distributed across suitable habitat, providing a rare opportunity to investigate the natural population dynamics of a listed threatened species. We used 12 microsatellite markers to investigate genetic diversity, population structure and gene flow among brush-tailed rock-wallaby colonies within and among two valley regions with continuous habitat in southeast Queensland. We documented high and signifcant levels of population genetic structure between rock-wallaby colonies embedded in continuous escarpment habitat and forest. We found a strong and significant pattern of isolation-by-distance among colonies indicating restricted gene flow over a small geographic scale (< 10 km) and conclude that gene flow is more likely limited by intrinsic factors rather than environmental factors. In addition, we provide evidence that genetic diversity was significantly lower in colonies located in a more isolated valley region compared to colonies located in a valley region surrounded by continuous habitat. These findings shed light on the processes that have resulted in the endangered status of rock-wallaby species in Australia and they have strong implications for the conservation and management of both the remaining 'connected' brush-tailed rock-wallaby colonies in the northern parts of the species' range and the remnant endangered populations in the south.

Stoichiometric and kinetic characterisation of Nitrobacter in mixed culture by decoupling the growth and energy generation processes

The growth, maintenance and lysis processes of Nitrobacter were characterised. A Nitrobacter culture was enriched in a sequencing batch reactor (SBR). Fluorescent in situ hybridisation showed that Nitrobacter constituted 73% of the bacterial population. Batch tests were carried out to measure the oxygen uptake rate and/or nitrite consumption rate when both nitrite and CO2 were in excess, and in the absence of either of these two substrates. The results obtained, along with the SBR performance data, allowed the determination of the maintenance coefficient and in situ cell lysis rate of Nitrobacter. Nitrobacter spends a significant amount of energy for maintenance, which varies considerably with the specific growth rate. At maximum growth, Nitrobacter consume nitrite at a rate of 0.042 mgN/mgCOD(biomass)center dot h for maintenance purposes, which increases more than threefold to 0.143 mgN/mgCOD(biomass)center dot h in the absence of growth. In the SBR, where Nitrobacter grew at 40% of its maximum growth rate, a maintenance coefficient of 0.113 mgN/mgCOD center dot h was found, resulting in 42% of the total amount of nitrite being consumed for maintenance. The above three maintenance coefficient values obtained at different growth rates appear to support the maintenance model proposed in Pirt (1982). The in situ lysis rate of Nitrobacter was determined to be 0.07/day under aerobic conditions at 22 C and pH 7.3. Further, the maximum specific growth rate of Nitrobacter was estimated to be 0.02/h (0.48/day). The affinity constant of Nitrobacter with respect to nitrite was determined to be 1.50 mgNO(2)(-)-N/L, independent of the presence or absence of CO2. (c) 2006 Wiley Periodicals, Inc.

The inhibitory effects of free nitrous acid on the energy generation and growth processes of an enriched Nitrobacter culture

The inhibitory effects of nitrite (NO2-)/free nitrous acid (HNO2-FNA) on the metabolism of Nitrobacter were investigated using a method allowing the decoupling of the growth and energy generation processes. A lab-scale sequencing batch reactor was operated for the enrichment of a Nitrobacter culture. Fluorescent in situ hybridization (FISH) analysis showed that 73% of the bacterial population was Nitrobacter. Batch tests were carried out to assess the oxygen and nitrite consumption rates of the enriched culture at low and high nitrite levels, in the presence or absence of inorganic carbon. It was observed that in the absence of CO2, the Nitrobacter culture was able to oxidize nitrite at a rate that is 76% of that in the presence of CO2, with an oxygen consumption rate that is 85% of that measured in the presence of CO2. This enabled the impacts of nitrite/FNA on the catabolic and anabolic processes of Nitrobacter to be assessed separately. FNA rather than nitrite was likely the actual inhibitor to the Nitrobacter metabolism. It was revealed that FNA of up to 0.05 mg HNO2-N center dot L-1 (3.4 mu M), which was the highest FNA concentration used in this study, did not have any inhibitory effect on the catabolic processes of Nitrobacter. However, FNA initiated its inhibition to the anabolic processes of Nitrobacter at approximately 0.011 mg HNO2-N center dot L-1 (0.8 mu M), and completely stopped biomass synthesis at a concentration of approximately 0.023 mg HNO2-N center dot L-1 (1.6 mu M). The inhibitory effect could be described by an empirical inhibitory model proposed in this paper, but the underlying mechanisms remain to be revealed.

Why are there so few genetic markers available for coral population analyses?

Coral reefs are in serious decline, and research in support of reef management objectives is urgently needed. Reef connectivity analyses have been highlighted as one of the major future research avenues necessary for implementing effective management initiatives for coral reefs. Despite the number of new molecular genetic tools and the wealth of information that is now available for population-level processes in many marine disciplines, scleractinian coral population genetic information remains surprisingly limited. Here we examine the technical problems and approaches used, address the reasons contributing to this delay in understanding, and discuss the future of coral population marker development. Considerable resources are needed to target the immediate development of an array of relevant genetic markers coupled with the rapid production of management focused data in order to help conserve our globally threatened coral reef resources.

A diffusion approach to approximating preservation probabilities for gene duplicates

Consider a haploid population and, within its genome, a gene whose presence is vital for the survival of any individual. Each copy of this gene is subject to mutations which destroy its function. Suppose one member of the population somehow acquires a duplicate copy of the gene, where the duplicate is fully linked to the original gene's locus. Preservation is said to occur if eventually the entire population consists of individuals descended from this one which initially carried the duplicate. The system is modelled by a finite state-space Markov process which in turn is approximated by a diffusion process, whence an explicit expression for the probability of preservation is derived. The event of preservation can be compared to the fixation of a selectively neutral gene variant initially present in a single individual, the probability of which is the reciprocal of the population size. For very weak mutation, this and the probability of preservation are equal, while as mutation becomes stronger, the preservation probability tends to double this reciprocal. This is in excellent agreement with simulation studies.

Stoichiometric and kinetic characterisation of Nitrosomonas sp in mixed culture by decoupling the growth and energy generation processes

A novel method that relies on the decoupling of the energy production and biosynthesis processes was used to characterise the maintenance, cell lysis and growth processes of Nitrosomonas sp. A Nitrosolnonas culture was enriched in a sequencing batch reactor (SBR) with ammonium as the sole energy source. Fluorescent in situ hybridization (FISH) showed that Nitrosomonas bound to the NEU probe constituted 82% of the bacterial population, while no other known ammonium or nitrite oxidizing bacteria were detected. Batch tests were carried out under conditions that both ammonium and CO, were in excess, and in the absence of one of these two substrates. The oxygen uptake rate and nitrite production rate were measured during these batch tests. The results obtained from these batch tests, along with the SBR performance data, allowed the determination of the maintenance coefficient and the in situ cell lysis rate, as well as the maximum specific growth rate of the Nitrosomonas culture. It is shown that, during normal growth, the Nitrosomonas culture spends approximately 65% of the energy generated for maintenance. The maintenance coefficient was determined to be 0.14 - 0.16 mgN mgCOD(biomass)(-1) h(-1), and was shown to be independent of the specific growth rate. The in situ lysis rate and the maximum specific growth rate of the Nitrosomonas culture were determined to be 0.26 and 1.0 day(-1) (0.043 h(-1)), respectively, under aerobic conditions at 30 degrees C and pH7. (c) 2006 Elsevier B.V. All rights reserved.

Optimizing Presence–Absence Surveys For Detecting Population Trends

Presence-absence surveys are a commonly used method for monitoring broad-scale changes in wildlife distributions. However, the lack of power of these surveys for detecting population trends is problematic for their application in wildlife management. Options for improving power include increasing the sampling effort or arbitrarily relaxing the type I error rate. We present an alternative, whereby targeted sampling of particular habitats in the landscape using information from a habitat model increases power. The advantage of this approach is that it does not require a trade-off with either cost or the Pr(type I error) to achieve greater power. We use a demographic model of koala (Phascolarctos cinereus) population dynamics and simulations of the monitoring process to estimate the power to detect a trend in occupancy for a range of strategies, thereby demonstrating that targeting particular habitat qualities can improve power substantially. If the objective is to detect a decline in occupancy, the optimal strategy is to sample high-quality habitats. Alternatively, if the objective is to detect an increase in occupancy, the optimal strategy is to sample intermediate-quality habitats. The strategies with the highest power remained the same under a range of parameter assumptions, although observation error had a strong influence on the optimal strategy. Our approach specifically applies to monitoring for detecting long-term trends in occupancy or abundance. This is a common and important monitoring objective for wildlife managers, and we provide guidelines for more effectively achieving it.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.

On the Structure of Spatial Branching Processes

The paper is a contribution to the theory of branching processes with discrete time and a general phase space in the sense of [2]. We characterize the class of regular, i.e. in a sense sufficiently random, branching processes (Φk) k∈Z by almost sure properties of their realizations without making any assumptions about stationarity or existence of moments. This enables us to classify the clans of (Φk) into the regular part and the completely non-regular part. It turns out that the completely non-regular branching processes are built up from single-line processes, whereas the regular ones are mixtures of left-tail trivial processes with a Poisson family structure.