Insights into the competitive adsorption of pollutants on a mesoporous alumina-silica nano-sorbent synthesized from coal fly ash and a waste aluminium foil.

A highly efficient and low-cost alumina-silica nano-sorbent was fabricated and characterized to understand the key factors responsible for its superiority over the existing adsorbents in treating the industry-discharged wastewater for the removal of dyes and heavy metals. As compared to the properties of raw fly ash, the following fundamental improvements were observed for the alumina-silica nano-sorbent: (a) transformation of throttled mesopores into slit-type pores, (b) increment in the surface area by 65-fold, (c) change in the morphology from spherical particles to a flake-type structure with sharp edges, (d) reduction in the average crystal size from 61.143 to 27.176 nm, and (e) increase in the pore volume from 0.005 to 0.50 cm3 g-1. These desired properties of the nano-sorbent were obtained by blending a waste aluminium foil with fly ash. This process increased the ratio of alumina to silica from 0.59 : 1 to an optimum ratio of 1.9 : 1, beyond which the particles agglomerated and the pore volume reduced. Eventually, the precipitated hydroxides were calcined at 700 °C that favoured the formation of γ-alumina. Moreover, this heat treatment changed its crystallinity and morphology of γ-alumina, which abruptly enhanced its activity towards the pollutants. The obtained product (nano-sorbent) was tested for the removal of lead and malachite green from a model wastewater solution over a wide range of initial pollutant concentrations and adsorbent dosages. After observing almost complete removal capacity and reusability for the pollutants, we propose this synthesized adsorbent as a universal material for treating industrial wastewater.

Nonlinear control of a multicomponent distillation process coupled with a binary distillation model as an EKF predictor.

The work is devoted to design the globally linearizing control (GLC) strategy for a multicomponent distillation process. The control system is comprised with a nonlinear transformer, a nonlinear closed-loop state estimator [extended Kalman filter (EKF)], and a linear external controller [conventional proportional integral (PI) controller]. The model of a binary distillation column has been used as a state predictor to avoid huge design complexity of the EKF estimator. The binary components are the light key and the heavy key of the multicomponent system. The proposed GLC-EKF (GLC in conjunction with EKF) control algorithm has been compared with the GLC-ROOLE [GLC coupled with reduced-order open-loop estimator (ROOLE)] and the dual-loop PI controller based on set point tracking and disturbance rejection performance. Despite huge process/predictor mismatch, the superiority of the GLC-EKF has been inspected over the GLC-ROOLE control structure.

A hybrid feedback linearizing-Kalman filtering control algorithm for a distillation column.

This paper studies the design of a discrete-time multivariable feedback linearizing control (FLC) structure. The control scheme included (i) a transformer [also called the input/output (I/O) linearizing state feedback law] that transformed the nonlinear u-y to a linearized v-y system, (ii) a closed-loop observer [extended Kalman filter (EKF)], which estimated the unmeasured states, and (iii) a conventional proportional integral (PI) controller that was employed around the v-y system as an external controller. To avoid the estimator design complexity, the design of EKF for a binary distillation column has been performed based on a reduced-order compartmental distillation model. Consequently, there is a significant process/predictor mismatch, and despite this discrepancy, the EKF estimated the required states of the simulated distillation column precisely. The FLC in conjunction with EKF (FLC-EKF) and that coupled with a measured composition-based reduced-order open-loop observer (FLC-MCROOLO) have been synthesized. The FLC structures showed better performance than the traditional proportional integral derivative controller. In practice, the presence of uncertainties and unknown disturbances are common, and in such situations, the proposed FLC-EKF control scheme ensured the superiority over the FLC-MCROOLO law.

Globally linearized control on diabatic continuous stirred tank reactor: a case study.

This paper focuses on the promise of globally linearized control (GLC) structure in the realm of strongly nonlinear reactor system control. The proposed nonlinear control strategy is comprised of: (i) an input-output linearizing state feedback law (transformer), (ii) a state observer, and (iii) an external linear controller. The synthesis of discrete-time GLC controller for single-input single-output diabatic continuous stirred tank reactor (DCSTR) has been studied first, followed by the synthesis of feedforward/feedback controller for the same reactor having dead time in process as well as in disturbance. Subsequently, the multivariable GLC structure has been designed and then applied on multi-input multi-output DCSTR system. The simulation study shows high quality performance of the derived nonlinear controllers. The better-performed GLC in conjunction with reduced-order observer has been compared with the conventional proportional integral controller on the example reactor and superior performance has been achieved by the proposed GLC control scheme.

Nonlinear model-based control algorithm for a distillation column using software sensor.

This paper presents the design of model-based globally linearizing control (GLC) structure for a distillation process within the differential geometric framework. The model of a nonideal binary distillation column, whose characteristics were highly nonlinear and strongly interactive, is used as a real process. The classical GLC law is comprised of a transformer (input-output linearizing state feedback), a nonlinear state observer, and an external PI controller. The tray temperature based short-cut observer (TTBSCO) has been used as a state estimator within the control structure, in which all tray temperatures were considered to be measured. Accordingly, the liquid phase composition of each tray was calculated online using the derived temperature-composition correlation. In the simulation experiment, the proposed GLC coupled with TTBSCO (GLC-TTBSCO) outperformed a conventional PI controller based on servo performances with and without measurement noise as well as on regulatory behaviors. In the subsequent part, the GLC law has been synthesized in conjunction with tray temperature based reduced-order observer (GLC-TTBROO) where the distillate and bottom compositions of the distillation process have been inferred from top and bottom product temperatures respectively, which were measured online. Finally, the comparative performance of the GLC-TTBSCO and the GLC-TTBROO has been addressed under parametric uncertainty and the GLC-TTBSCO algorithm provided slightly better performance than the GLC-TTBROO. The resulting control laws are rather general and can be easily adopted for other binary distillation columns.