Preparation of briquettes from the Golden Horn bottom sediments by hydro-thermal agglomeration process

The Golden Horn (GH) sediments, which consist mainly of clay, organic substances and heavy metals, are formed with the contribution of industrial and domestic wastes released in the Golden Horn Estuary. On account of their mineralogical and chemical composition, these sediments may be regarded as a suitable raw material for briquette production. In this study, the utilization of GH dredged bottom sediments was investigated for preparation of briquettes. Dried GH sediments were mixed with time and sand in different percentages, moulded at various squeezing pressures and hardened under several steam pressure values by autoclaving. The briquettes produced through these different process conditions were tested for compressive strength according to the Turkish standards (TS705). It was found that variations in compressive strength were dependent on the amount of lime (Ca(OH)(2)) and sand (SiO2) added. Results show that the compressive strength increased with increasing lime and decreasing sand in the mixtures prepared for briquettes. It is concluded that briquettes with a compressive strength value of 294 kgf cm(-2) can be produced. This allows the GH sediments to be taken into account as a raw material in brick production, as far as compressive strength requirements are concerned. This possibility may represent an important way either for reducing environmental pollution or for recycling waste materials in industrial applications

Nonlinear Stochastic Control Part I: A Moment-Based Approach

This paper describes a new stochastic control methodology for nonlinear affine systems subject to parametric and functional uncertainties, with random excitations. The primary objective of this method is to control the statistical nature of the state of a nonlinear system to designed (attainable) statistical properties (e.g. moments). This methodology involves a constrained optimization problem for obtaining the undetermined control parameters, where the norm of the error between the desired and actual stationary moments of state/output responses is minimized subject to constraints on moments corresponding to a stationary distribution. To overcome the difficulties in solving the associated Fokker-Planck equation, generally experienced in nonlinear stochastic control and filtering problems, an approximation using the direct quadrature method of moments is proposed. In this innovative approach, the state probability density function is expressed in terms of a finite collection of Dirac delta functions, with the associated weights and locations determined by moment equations. The advantages of the proposed method are: (1) robustness with respect to parametric and functional uncertainties; (2) ability to control any specified stationary moments of the states/output probability density function; and (3) the state process can be Non-Gaussian. A numerical simulation is used to demonstrate the capability of the proposed nonlinear stochastic control method. Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved