Kinetic and thermodynamics of chromium ions adsorption onto low-cost dolomite adsorbent

The chromium bearing wastewater in this study was used to simulate the low concentration discharge from a major aerospace manufacturing facility in the UK. Removal of chromium ions from aqueous solutions using raw dolomite was achieved using batch adsorption experiments. The effect of; initial Cr(VI) concentration, amount of adsorbent, solution temperature, dolomite particle size and shaking speed was studied. Maximum chromium removal was found at pH 2.0. A kinetic study yielded an optimum equilibrium time of 96 h with an adsorbent dose of 1 g/L Sorption studies were conducted over a concentration range of 5-50 mg/L Cr(VI) removal decreased with an increase in temperature (q(max): 20 degrees C = 10.01 mg/g; 30 degrees C = 8.385 mg/g; 40 degrees C = 6.654 mg/g; and 60 degrees C = 5.669 mg/g). Results suggest that the equilibrium adsorption was described by the Freundlich model. The kinetic processes of Cr(VI) adsorption onto dolomite were described in order to provide a more clear interpretation of the adsorption rate and uptake mechanism. The overall kinetic data was acceptably explained by a pseudo first-order rate model. Evaluated Delta G degrees and Delta H degrees specify the spontaneous and exothermic nature of the reaction. The adsorption takes place with a decrease in entropy (Delta S degrees is negative). (C) 2011 Elsevier B.V. All rights reserved.

Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

Optimal proportional-integral-derivative control of shape memory alloy actuators using genetic algorithm and the Preisach model

Shape memory alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics, and so on. Although the number of applications is increasing, there has been limited success in precise motion control owing to the hysteresis effect of these smart actuators. The present paper proposes an optimization of the proportional-integral-derivative (PID) control method for SMA actuators by using genetic algorithm and the Preisach hysteresis model.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.