This work aims at the identification of a special class nonlinear state space observers for nonlinear multivariable systems directly from input-output data when the data is corrupted with unmeasured disturbances. At the identification stage, the one step ahead predictor form of the model is arranged to have a Weiner-like structure. The linear dynamic component of the predictor is parametrized using generalized orthonormal basis functions. The resulting observer is shown to be a nonlinear ARX (NARX) type model with an infinite but fading memory property. It is also shown that the proposed model structure is capable of capturing input as well as output multiplicity (multiple steady states) behavior. The efficacy of the proposed modeling scheme is demonstrated using simulation studies on a continuously stirred tank reactor (CSTR) process model, which exhibits input multiplicity, and another CSTR process model that exhibits output multiplicities. The types of unmeasured disturbances investigated are (a) unknown input disturbances (such as feed concentration fluctuations), (b) uncertainties in manipulated inputs, and (c) fluctuation in process parameters. The proposed modeling scheme is also validated in real time using a laboratory scale, multivariable experimental system. The analysis of the simulation and experimental studies reveals that the identified models have excellent disturbance modeling and long range prediction abilities. The identified models are also able to capture the steady-state behavior of the systems under consideration reasonably accurately over a wide operating range. The resulting stochastic model can be directly used for the development of an extended Kalman filter and to formulate a nonlinear model predictive control (NMPC) scheme.
Activated carbons prepared from tamarind nutshell, an agricultural waste by-product, have been examined for the removal of phenol from aqueous solutions. The activated carbon was prepared by sulfuric acid activation. Both batch and column studies were performed for the sorption of phenol. The kinetic data were fitted to the models of Lagergren, pseudo-second-order and intraparticle diffusion, and closely followed the pseudo-second-order chemisorption model. The Freundlich and Langmuir isotherm models were well fitted. The solution pH greatly affects the sorption process. The column study results indicate that the sorption of phenol is dependent on the flow rate, the inlet phenol concentration as well as on the particle size of the adsorbent.
Phenol is a refractive pollutant that is generated from almost all the types of industries. Removal of phenol can be achieved economically by using a cost effective technique like adsorption on to activated carbon. The present paper reports on the preparation and characterization of activated carbon from tamarind nutshell, an agricultural waste byproduct, and its use in a packed bed for the removal of phenol. The breakthrough curves for column sorption of phenol from aqueous solutions to TNSAC have been measured at various flow rates and different particle sizes at 28 C. The results obtained showed that the sorption of phenol is dependent on both the flow rate and the particle size of the adsorbent, and that the breakpoint time and phenol removal yield decrease with increasing flow rate and particle size. The overall mass transfer coefficient is calculated from the experimental data and compared with the values obtained from the correlation. Experimental values are in excellent agreement with the predicted values from the correlation.
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