The effect of the precursor on the morphology, the structure, and the adsorption properties of the activated carbon in the case of poly(ethylene terephthalate) and cellulose, was studied. Scanning tunnel microscopy, X-ray scattering methods, and adsorption from gas and liquid phases were applied to answer the problem. According to adsorption data, the micropore structure and the chemical character of the two activated samples were found to be practically independent of their origin, but essential structural dissimilarities were concluded from SAXS data. STM illustrated the effect of the precursor's structure only in dimensions which are considerably larger than the size of the regions governing the microporous behavior. The morphological differences in the carbon correspond to the different links between the crystallites. Presumably these connections strongly affect the diffusion of the activating agent and thus the pore structure developing during the activation process.

This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods are proposed. The first one is an alternating optimization algorithm that iteratively refines the estimate of the linear dynamics and the parameters of the static fuzzy model. The second method estimates the parameters of the nonlinear static model and of the linear dynamic model simultaneously by using a constrained recursive least-squares algorithm. The obtained FH model is incorporated in a model-based predictive control scheme and a new constraint-handling method is presented. A simulated water-heater process is used as an illustrative example. A comparison with an affine neural network and a linear model is given. Simulation results show that the proposed FH modeling approach is useful for modular parsimonious modeling and model-based control of nonlinear systems.

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