The FUELCON meta-architecture, II: alternatives for parameter prediction

We continue the discussion of the decision points in the FUELCON metaarchitecture. Having discussed the relation of the original expert system to its sequel projects in terms of an AND/OR tree, we consider one further domain for a neural component: parameter prediction downstream of the core reload candidate pattern generator, thus, a replacement for the NOXER simulator currently in use in the project.

A comprehensive copper stockpile leach model: background and parameter sensitivity

The design and development of a comprehensive computational model of a copper stockpile leach process is summarized. The computational fluid dynamic software framework PHYSICA+ and various phenomena were used to model transport phenomena, mineral reaction kinetics, bacterial effects, and heat, energy and acid balances for the overall leach process. In this paper, the performance of the model is investigated, in particular its sensitvity to particle size and ore permeability. A combination of literature and laboratory sources was used to parameterize the model. The simulation results from the leach model are compared with closely controlled column pilot scale tests. The main performance characteristics (e.g. copper recovery rate) predicted by the model compare reasonably well with the experimental data and clearly reflect the qualitiative behavior of the process in many respects. The model is used to provide a measure of the sensitivity of ore permeability on leach behavior, and simulation results are examined for several different particle size distributions.

The influence of crank configuration on muscle activity and torque production during arm crank ergometry

This study investigated the effect of crank configuration on muscle activity and torque production during submaximal arm crank ergometry. Thirteen non-specifically trained male participants volunteered. During the research trials they completed a warm-up at 15 W before two 3-min exercise stages were completed at 50 and 100 W; subjects used either a synchronous or asynchronous pattern of cranking. During the final 30-s of each submaximal exercise stage electromyographic and torque production data were collected. After the data had been processed each parameter was analysed using separate 2-way ANOVA tests with repeated measures. The activity of all muscles increased in line with external workload, although a shift in the temporal pattern of muscle activity was noted between crank configurations. Patterns of torque production during asynchronous and synchronous cranking were distinct. Furthermore, peak, minimum and delta (peak-minimum) torque values were different (P < 0.05) between crank configurations at both workloads. For example, at 100 W, peak torque using synchronous [19.6 (4.3) Nm] cranking was higher (P < 0.05) compared to asynchronous [16.8 (1.6) Nm] cranking. In contrast minimum torque was lower (P < 0.05) at 100 W using synchronous [4.8 (1.7) Nm] compared to asynchronous [7.3 (1.2) Nm] cranking. There was a distinct bilateral asymmetry in torque production during asynchronous cranking with the dominant transmitting significantly more force to the crank arm. Taken together, these preliminary data demonstrate the complex nature of muscle activity during arm crank ergometry performed with an asynchronous or synchronous crank set-up. Further work is required to determine how muscle activity (EMG activity) and associated patterns of torque production influence physiological responses and functional capacity during arm crank ergometry.