Modeling of anaerobic treatment of wastewater in ponds.

A mathematical model to simulate the performance of anaerobic ponds was developed incorporating both settling of particulate components and the biological anaerobic digestion process. The biological activity includes solubilization of particulate organic matter; methanogenesis and the sulphate reduction process. The model considers that an anaerobic pond comprises a series of equal size columns. Each column has three compartments viz. liquid layer, active sediment layer and inert sediment layer. The existence of organic matter and sulphate removal mechanisms both in the bulk as well as sediment layer of the ponds and the exchange of the soluble components between the layers has been included in the model. The model was transferred to a computer program using VisSim Basic software. The model was verified by comparing simulated results with full-scale as well as with laboratory-scale anaerobic pond performance data. A good agreement between the simulated and the observed pond performance was achieved.     

Harmonic modification and data adaptive filtering based approach to robust pitch estimation

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (PFS). The dominant harmonic (DH) of the PFS is determined and enhanced its amplitude. Normalized autocorrelation function (NACF) is applied to that modified signal. Then empirical mode decomposition (EMD) based data adaptive time domain filtering is applied to the NACF signal. Partial reconstruction is performed in EMD domain. The pitch period is determined from the partially reconstructed signal. The experimental results show that the proposed method performs better than the other recently developed methods for noisy and clean speech signals in terms of gross and fine pitch errors.     

Estimation of gross calorific value of coals using artificial neural networks

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels.     

Bed expansion ratio of mono-sized and binary mixtures in fluidized,spouted, and spout-fluid beds

Studies on bed expansion ratio were carried out in fluidized, spouted, and spout-fluid beds. A single column has been used to compare the characteristics of fluidized, spouted, and spout-fluid beds. Experiments were carried out using air and glass beads under fluidized, spouted, and spout-fluid bed conditions separately to study the effect of gas velocity, bed mass, and particle size on bed expansion ratio. Glass beads of different sizes (0.75, 1.2, 1.7, and 3.075?mm) have been used as solid bed material. Bed expansion ratio was determined for mono-size particles and binary mixtures (different diameter ratios and composition). It was found that the bed expansion ratio decreases with increase in bed mass for only spouting condition and spout-fluidization conditions. The bed expansion ratio increases with increase in bed mass for only fluidization condition.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

A generic classification and object-oriented simulation toolkit forSMT assembly equipment

Applications of computer simulation for surface mount technology (SMT) assembly lines have addressed many aspects of supply chain management, e.g., estimating cycle times, evaluating production equipment and line configurations, optimizing throughput and assessing product design for manufacturability. The implementation of an SMT machine simulator typically requires specialized code based on cycle time characterization experiments for the given machine type. This paper describes a generic simulation toolkit based on a classification of current SMT equipment. A detailed survey of machines has led to the definition of machine families and a generic machine model. A simulation toolkit consisting of object classes written in C++ has been developed to represent the generic machine model. Object-oriented design has enabled the construction of an object hierarchy that models the physical structure of a machine and its operation. A machine definition language has been specified to let the user configure the toolkit for a particular machine. The concept of a "snapshot" object is introduced, as a way to capture simulated operations for both parallel and sequential processes. Finally, this paper presents an application of the toolkit in the optimization of SMT machines. Results are presented for the Universal GSM2, a gantry-type machine     

Modeling response of nitrifying biofilm to inhibitory shock loads.

To study the response of nitrifying biofilm to inhibitory shock loads, a lab-scale nitrifying biofilm reactor was operated in ambient conditions. Shock loads of various concentrations of inhibitory compound were applied to the biofilm. Aniline was used as an inhibitory compound. The experimental results were utilized to develop a model for predicting the variation of effluent nitrate concentration from the biofilm reactor for given shock loads of aniline concentration and exposure time both in exposure as well as in recovery phase. Close agreement between model and experimental observation of bulk aniline concentration and effluent nitrate concentration was obtained which indicates the usefulness of the model to estimate bulk aniline concentration and to predict the response of inhibitory shock loads on nitrifying biofilm.