A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.

Molecular dynamics simulations of the hydrophobin SC3 at a hydrophobic/hydrophilic interface

Hydrophobins are small (similar to 100 aa) proteins that have an important role in the growth and development of mycelial fungi. They are surface active and, after secretion by the fungi, self-assemble into amphipathic membranes at hydrophobic/hydrophilic interfaces, reversing the hydrophobicity of the surface. In this study, molecular dynamics simulation techniques have been used to model the process by which a specific class I hydrophobin, SC3, binds to a range of hydrophobic/ hydrophilic interfaces. The structure of SC3 used in this investigation was modeled based on the crystal structure of the class II hydrophobin HFBII using the assumption that the disulfide pairings of the eight conserved cysteine residues are maintained. The proposed model for SC3 in aqueous solution is compact and globular containing primarily P-strand and coil structures. The behavior of this model of SC3 was investigated at an air/water, an oil/water, and a hydrophobic solid/water interface. It was found that SC3 preferentially binds to the interfaces via the loop region between the third and fourth cysteine residues and that binding is associated with an increase in a-helix formation in qualitative agreement with experiment. Based on a combination of the available experiment data and the current simulation studies, we propose a possible model for SC3 self-assembly on a hydrophobic solid/water interface.

Three-dimensional simulation of the rolling of a saturated fibro-porous media

This paper describes recent advances made in computational modelling of the sugar cane liquid extraction process. The saturated fibro-porous material is rolled between circumferentially grooved rolls, which enhance frictional grip and provide a low-resistance path for liquid flow during the extraction process. Previously reported two-dimensional (2D) computational models, account for the large deformation of the porous material by solving the fully coupled governing fibre stress and fluid-flow equations using finite element techniques. While the 2D simulations provide much insight into the overarching cause-effect relationships, predictions of mechanical quantities such as roll separating force and particularly torque as a function of roll speed and degree of compression are not satisfactory for industrial use. It is considered that the unsatisfactory response in roll torque prediction may be due to the stress levels that exist between the groove tips and roots which have been largely neglected in the geometrically simplified 2D model. This paper gives results for both two- and three-dimensional finite element models and highlights their strengths and weaknesses in predicting key milling parameters. (c) 2005 Elsevier B.V. All rights reserved.

A simulation model of cereal-legume intercropping systems for semi-arid regions II. Model application

Smallholder farmers in Africa practice traditional cropping techniques such as intercropping. Intercropping is thought to offer higher productivity and resource milisation than sole cropping. In this study, risk associated with maize-bean intercropping was evaluated by quantifying long-term yield in both intercropping and sole cropping in a semi-arid region of South Africa (Bloemfontein, Free State) with reference to rainfall variability. The crop simulation model was run with different cultural practices (planting date and plant density) for 52 summer crop growing seasons (1950/1951-2001/2002). Eighty-one scenarios, consisted of three levels of initial soil water, planting date, maize population, and bean population, were simulated. From the simulation outputs, the total land equivalent ratio (LER) was greater than one. The intercrop (equivalent to sole maize) had greater energy value (EV) than sole beans, and the intercrop (equivalent to sole beans) had greater monetary value (MV) than sole maize. From these results, it can be concluded that maize-bean intercropping is advantageous for this semi-arid region. Soil water at planting was the most important factor of all scenario factors, followed by planting date. Irrigation application at planting, November/December planting and high plant density of maize for EV and beans for MV can be one of the most effective cultural practices in the study region. With regard to rainfall variability, seasonal (October-April) rainfall positively affected EV and MV, but not LER. There was more intercrop production in La Nina years than in El Nino years. Thus, better cultural practices may be selected to maximize maize-bean intercrop yields for specific seasons in the semi-arid region based on the global seasonal outlook. (c) 2004 Elsevier B.V. All rights reserved.

Simulation study of ammonia adsorption on graphitized carbon black

GCMC simulations are applied to the adsorption of sub-critical ammonia on graphitized carbon black at 240 K. The carbon black was modelled both with and without carbonyl functional groups. Large differences are seen between the amount adsorbed for different carbonyl configurations at low pressure (P < 10kPa). Once a single layer is formed on the carbon black, the adsorption behaviour is similar between the model surfaces with and without functional groups. Simulation isotherms are qualitatively similar to the few experimental isotherms available in the literature for ammonia on highly graphitized carbon black. The mode of adsorption up to monolayer coverage is exhaustively shown to be two-dimensional clustering using various techniques. A comparison between experiment and simulation isosteric heats shows that a surface without functional groups cannot reproduce the experimental isosteric heats of adsorption, even comparing with the experimental results of carbon black heat treated at 3373 K. The addition of carbonyls produces isosteric heats with similar features to those in the literature if the separation between the carbonyls is small.

Improved algorithms for rare event simulation with heavy tails

The estimation of P(S-n > u) by simulation, where S, is the sum of independent. identically distributed random varibles Y-1,..., Y-n, is of importance in many applications. We propose two simulation estimators based upon the identity P(S-n > u) = nP(S, > u, M-n = Y-n), where M-n = max(Y-1,..., Y-n). One estimator uses importance sampling (for Y-n only), and the other uses conditional Monte Carlo conditioning upon Y1,..., Yn-1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.